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sgarg87/big_mech_isi_gg | ['semantic parsing'] | ['Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text'] | edge_vectors/prv_learned_amr_sdg_edge_vectors_frm_3k_sentences/edge_vectors.py edge_vectors/word_vectors.py edge_vectors/add_inverse_edge_vectors.py edge_vectors/read_dot_file.py constants_absolute_path.py pubmed45_dataset/amr_sets.py edge_vectors/edge_labels.py config.py edge_vectors/util.py graph_distribution_kernel/graph_distributions_divergence.py pubmed45_dataset/__init__.py edge_vectors/edge_labels_propagation.py constants.py get_inverse_of_edge_label EdgeLabelVectorsPropagation InverseEdgeVectors get_edge_label_wordvector_file_path load_obj get_descendants get_ancestors get_descendants_inc_inlaws build_nodes_tree_from_amr_dot_file simplify_nodes_tree_names get_children_and_child_in_laws Node get_descendants_undirected unique_list WordVectors EdgeVectorsAmrSdg GraphDistributionDivergence endswith lower list remove create_children_and_in_laws_list unique_list insert set create_children_list list remove unique_list insert set list remove unique_list insert create_undirected_children_list set list print debug name append values create_children_list list unique_list print debug name insert copy index dummy_label keys replace get_label get_edge_list get_destination Node sub graph_from_dot_file append absolute_path get_source pop deepcopy list print debug remove_list_of_nodes get_name_str keys add set | This code is in reference to the the paper below. Sahil Garg, Aram Galstyan, Ulf Hermjakob, and Daniel Marcu. Extracting biomolecular interactions using semantic parsing of biomedical text. In Proc. of AAAI, 2016. The above work is done as a part of DARPA project "Big Mechanism" at ISI, USC (sponsored by DARPA Big Mechanism program (W911NF-14-1-0364)). http://www.darpa.mil/program/big-mechanism | 3,600 |
sgenza/tf_crnn | ['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'] | train.py model/crnn.py model/dataset.py model/__init__.py evaluate.py model/utils.py main _get_args main _get_args CRNN DataLoaderSVT DataLoaderIIIT5K DataLoaderSynth90K seed_everything read_config arrays2labels sparse_tuple_from add_argument ArgumentParser config read_config _get_args seed str set_random_seed list asarray extend zip enumerate len | # Convolutional Recurrent Neural Network (CRNN) This is a TensorFlow implementation of CRNN for scene text recognition.\ The model consists of a CNN stage extracting features which are fed to an RNN stage and a CTC-loss.\ You can find the original paper [here](https://arxiv.org/abs/1507.05717).  ## Requirements The following OS and software versions have been tested: - Ubuntu 18.04.5 LTS - Python 3.7.4 - CUDA 10.1 | 3,601 |
sgflower66/SPI-Optimizer | ['stochastic optimization'] | ['SPI-Optimizer: an integral-Separated PI Controller for Stochastic Optimization'] | experiment/experiment_code/optimizers/PIDACC.py utils/progress/progress/bar.py utils/logger.py 2D_function/arrow_pic/draw_lag_1picmom.py 2D_function/arrow_pic/draw_lag_TRIpicnag.py utils/progress/progress/helpers.py utils/progress/progress/__init__.py 2D_function/arrow_pic/draw_lag_GOLDpicm99 (another copy).py 2D_function/arrow_pic/draw_lag_ROSpicm99 (copy).py experiment/experiment_code/models/cliquenet.py experiment/experiment_code/models/cifar/preresnet.py 2D_function/arrow_pic/draw_lag_1picnag.py 2D_function/arrow_pic/draw_lag_GOLDpicnag (another copy).py experiment/experiment_code/models/cifar/vgg.py experiment/experiment_code/models/cifar/__init__.py utils/progress/progress/spinner.py utils/eval.py experiment/experiment_code/sgdsp99.py utils/misc.py experiment/experiment_code/models/cifar/alexnet.py utils/progress/setup.py experiment/experiment_pic/draw_alexnetc100_200.py experiment/experiment_code/train_momentsp99.py experiment/experiment_code/mnist_pid.py experiment/experiment_code/optimizers/PIDOPT.py experiment/experiment_pic/draw_resnet56_c10.py 2D_function/arrow_pic/draw_lag_1picsgd.py experiment/experiment_pic/draw_mnist.py experiment/experiment_code/mnist_gds.py experiment/experiment_code/mnist_sp99.py experiment/experiment_code/optimizers/neumann.py experiment/experiment_pic/draw_alexnetc100.py 2D_function/arrow_pic/draw_lag_ROSpicsgd (copy).py experiment/experiment_code/models/cifar/densenet.py utils/__init__.py experiment/experiment_pic/draw_wrn_c100.py experiment/experiment_code/gds.py experiment/experiment_code/train_nag.py 2D_function/arrow_pic/draw_lag_TRIpicm99.py utils/progress/test_progress.py experiment/experiment_code/models/cifar/wrn.py experiment/experiment_pic/draw_alexnetc10.py experiment/experiment_code/models/cifar/cliquenet.py experiment/experiment_code/models/cifar/resnext.py experiment/experiment_code/optimizers/pd.py 2D_function/arrow_pic/draw_lag_TRIpicmom.py utils/progress/progress/counter.py 2D_function/arrow_pic/draw_lag_ROSpicmom (copy).py 2D_function/arrow_pic/draw_lag_GOLDpicsgd (another copy).py utils/visualize.py experiment/experiment_code/train_gds.py 2D_function/loss_pic/draw_loss.py experiment/experiment_pic/draw_resnet56_c100.py 2D_function/arrow_pic/draw_lag_TRIpicsgd.py 2D_function/arrow_pic/draw_lag_ROSpicnag (copy).py experiment/experiment_code/mnist_nag.py experiment/experiment_code/models/cifar/resnet.py 2D_function/arrow_pic/draw_lag_GOLDpicmom (another copy).py experiment/experiment_code/sgd.py experiment/experiment_code/models/cifar/preresnet44.py experiment/experiment_code/train_moment.py experiment/experiment_code/models/cifar/utils.py experiment/experiment_code/optimizers/pid.py experiment/experiment_code/mnist_moment.py 2D_function/arrow_pic/draw_lag_1picm99.py g momentum fn lossfn m99 g nesterov sgd momentum fn lossfn m99 g nesterov momentum fn lossfn g nesterov sgd momentum fn lossfn m99 g momentum fn lossfn m99 g nesterov sgd momentum fn lossfn m99 g nesterov momentum fn lossfn g nesterov sgd momentum fn lossfn m99 g momentum fn lossfn m99 g nesterov sgd momentum fn lossfn m99 g nesterov momentum fn lossfn g nesterov sgd momentum fn lossfn m99 g momentum fn lossfn m99 g nesterov sgd momentum fn lossfn m99 g nesterov momentum fn lossfn g nesterov sgd momentum fn lossfn m99 newtons draw_segments_on_img g nesterov nm map_xy_to_img_coords sgd momentum fn lossfn NE generate_background_cost map_cost_to_color frcg m99 m99jf dampnm SGD Net Net SGD SGD cliquenet AlexNet alexnet cliquenet densenet Transition DenseNet Bottleneck BasicBlock preresnet PreResNet Bottleneck conv3x3 BasicBlock CifarPreResNet ResNetBasicblock preresnet44 ResNet Bottleneck conv3x3 resnet BasicBlock ResNeXtBottleneck resnext CifarResNeXt clique_block compress transition global_pool attention vgg19 VGG vgg16_bn vgg19_bn vgg11_bn make_layers vgg11 vgg13 vgg13_bn vgg16 wrn BasicBlock NetworkBlock WideResNet Neumann PIDOptimizer PIDOptimizer PIDACC PIDOPT main excel_table_byname open_excel main excel_table_byname open_excel main excel_table_byname open_excel main excel_table_byname open_excel main excel_table_byname open_excel main excel_table_byname open_excel main excel_table_byname open_excel accuracy plot_overlap savefig Logger LoggerMonitor init_params AverageMeter mkdir_p get_mean_and_std make_image show_mask_single show_mask gauss colorize show_batch sleep FillingSquaresBar FillingCirclesBar IncrementalBar ChargingBar ShadyBar PixelBar Bar Countdown Stack Counter Pie SigIntMixin WriteMixin WritelnMixin PieSpinner MoonSpinner Spinner PixelSpinner LineSpinner Progress Infinite norm str g zeros_like print copy lossfn append array range str g zeros_like print copy sign lossfn append range array clip str g zeros_like print copy lossfn append array range str g zeros_like print copy lossfn append array range log2 str g zeros_like print copy lossfn append array range str print copy dot gfun array append str hess print solve copy gfun array append str hess print solve copy gfun array append append array copy g copy fn append abs array min fn linspace meshgrid round max uint8 COLOR_HSV2BGR astype shape cvtColor line zip AlexNet CifarPreResNet CifarResNeXt 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 WideResNet open_workbook nrows open_excel sheet_by_name row_values append range show plot xlabel FormatStrFormatter ylabel MultipleLocator ylim title set_major_formatter legend xlim excel_table_byname axvline axhline col_values ncols topk size t eq mul_ expand_as append sum max asarray arange plot numbers enumerate len print DataLoader div_ zeros range len normal constant isinstance kaiming_normal Conv2d bias modules BatchNorm2d weight Linear makedirs numpy range zeros unsqueeze gauss show make_image imshow make_grid make_image subplot make_grid size clone axis upsampling imshow expand_as range make_image subplot make_grid size clone axis upsampling imshow expand_as cpu range len | sgflower66/SPI-Optimizer | 3,602 |
shabnam-b/reddit-pos-ensemble | ['part of speech tagging'] | ['A Cross-Genre Ensemble Approach to Robust Reddit Part of Speech Tagging'] | Scripts/flair_train_predict.py Scripts/ensemble.py process_features read_predictions train_xgb train predict glob print append exit fit LabelEncoder transform enumerate append column_stack print LabelEncoder accuracy_score inverse_transform train DMatrix predict fit make_tag_dictionary model load value results tokens model sort write open | shabnam-b/reddit-pos-ensemble | 3,603 |
shahriarta/Data-Guided-Regulation | ['time series'] | ['On Regularizability and its Application to Online Control of Unstable LTI Systems'] | F-DGR-Simulation.py DGR-Simulation.py instability_number.py DGR-Simulation-revised.py data2hankel deepc plot_controller Trajectory_generator mpc plots_traj dynamics Trajectory_generator plots dynamics Trajectory_generator plots dynamics range __len__ concatenate norm Problem data2hankel Minimize concatenate print Variable solve __len__ sum_squares split Problem Minimize Variable solve range normal T svd norm matrix_power print deepc hstack dare pinv eye append zeros array range len set_axes_locator grid show subplot list set_title axvline ylim legend append range get_position plot add_artist set mark_inset norm xlabel axes rc InsetPosition figure yscale list plot xlabel rc grid ylabel __len__ tight_layout figure legend range set_axes_locator grid show subplot set_title axvline ylim title legend append range get_position plot add_artist set xlim mark_inset norm xlabel axes rc InsetPosition figure | # Data-Guided Regulation (DGR) In this repository, we provide the code for the simulations of the following manuscript: <br> <br> ### [Online Regulation of Unstable LTI Systems from a Single Trajectory](https://128.84.21.199/abs/2006.00125) Shahriar Talebi, Siavash Alemzadeh, Niyousha Rahimi, Mehran Mesbahi <br> <br> --- ## Abstract <div align="justify"> Recently, data-driven methods for control of dynamic systems have received considerable attention in system theory and machine learning as they provide a mechanism for feedback synthesis from the observed time-series data. However learning, say through direct policy updates, often requires assumptions such as knowing <em> a priori </em> that the initial policy (gain) is stabilizing, e.g., when the open-loop system is stable. In this paper, we examine online regulation of (possibly unstable) partially unknown linear systems with no <em> a priori </em> assumptions on the initial controller. First, we introduce and characterize the notion of "regularizability" for linear systems that gauges the capacity of a system to be regulated in finite-time in contrast to its asymptotic behaviour (commonly characterized by stabilizability/controllability). Next, having access only to the input matrix, we propose the Data-Guided Regulation synthesis that (as its name suggests) regulates the underlying states while also generating informative data that can subsequently be used for data-driven stabilization or system identification. The analysis is also related in spirit, to the spectrum and the "instability number" of the underlying linear system, a novel geometric property studied in this work. We further elucidate our results by considering special structures for system parameters as well as boosting the performance of the algorithm via a rank-one matrix update using the discrete nature of data collection in the problem setup. Finally, we demonstrate the utility of the proposed approach via an example involving direct (online) regulation of the X-29 aircraft. </div> <br>  | 3,604 |
shahrukhqasim/TIES-2.0 | ['table recognition'] | ['Rethinking Table Recognition using Graph Neural Networks'] | python/bin/checks/explore_network_x.py python/models/fast_conv_segment.py python/bin/checks/check_monte_carlo.py python/models/gravnet_segment.py python/libs/plots.py python/iterators/table_adjacency_parsing_iterator.py python/models/model_factory.py python/iterators/iterator_interface.py python/models/model_interface.py python/readers/image_words_reader.py python/layers/ties.py python/models/fcnn_segment.py python/models/basic_model.py python/libs/visual_feedback_generator.py python/models/dgcnn_model.py python/bin/analyse/table_adjacency_inference_analyser.py python/libs/inference_output_streamer.py python/bin/checks/check_pdf.py python/models/conv_segment.py python/models/dgcnn_segment.py python/libs/configuration_manager.py python/bin/iterate/table_adjacency_parsing.py python/models/garnet_segment.py python/bin/checks/check_monte_carlo_2.py python/ops/ties.py python/models/network_segment_interface.py python/libs/helpers.py str2bool analyse make_image Iterator TableAdjacencyParsingIterator dgcnn_model ConfigurationManager get_num_parameters InferenceOutputStreamer plot_few VisualFeedbackGenerator BasicModel BasicConvSegment DgcnnModel DgcnnSegment FastConvSegment FcnnSegment GarNetSegment GravnetSegment ModelFactory ModelInterface NetworkSegmentInterface layer_GravNet2 gather_features_from_conv_head edge_conv_layer ImageWordsReader config join call init input get_config_param number close imshow savefig figure PdfPages range dense layer_global_exchange concat edge_conv_layer high_dim_dense get_shape trainable_variables int uint8 number print COLOR_GRAY2BGR astype close copy imshow rectangle file_path savefig figure randint PdfPages range cvtColor len concat float32 shape int64 cast tile dense indexing_tensor concat edge_activation aggregation_function gather_nd tile expand_dims indexing_tensor collapse_to_vertex print concat shape reduce_mean gather_nd high_dim_dense | # TIES-2.0 TIES was my undergraduate thesis, Table Information Extraction System. I picked the name from there and made it 2.0 from there. This is a repository containing source code for the arxiv paper 1905.13391 ([link](https://arxiv.org/pdf/1905.13391.pdf)). This paper has been accepted into ICDAR 2019. To cite the paper, use: ``` @article{rethinkingGraphs, author = {Qasim, Shah Rukh and Mahmood, Hassan and Shafait, Faisal}, title = {Rethinking Table Recognition using Graph Neural Networks}, | 3,605 |
shakex/Recurrent-Decoding-Cell | ['medical image segmentation', 'semantic segmentation'] | ['Segmenting Medical MRI via Recurrent Decoding Cell'] | utils/utils.py models/SegNet.py schedulers/schedulers.py loader/__init__.py models/UNet.py models/CRDN.py train.py models/FCN.py schedulers/__init__.py metrics.py loader/hvsmrLoader.py models/__init__.py loss/__init__.py optimizers/__init__.py utils/__init__.py test.py loader/mrbrainsLoader.py loader/brainwebLoader.py loss/loss.py utils/calMeanStd.py averageMeter runningScore boxplotvis test array2dataframe train brainwebLoader debug_load debug_load hvsmrLoader mrbrainsLoader debug_load get_loader cross_entropy2d get_loss_function UNetRNN ResNetFCN ResNet50RNN ResNetRNN ResNet152RNN ResNet18RNN ResNet101RNN Bottleneck unetConv2 ResNetUNet conv3x3 unetUp VGG16RNN RDC ResNet34RNN ResNet50FCN BasicBlock ResNet50UNet get_upsampling_weight FCN segnetDown2 SegNet conv2DBatchNormRelu segnetUp2 segnetDown3 segnetUp3 UNetSegNet conv2DBatchNormRelu UNetFCN unetConv2 UNet VGGUNet unetUp segnetUp2 segnetUp3 unetUp2 get_model _get_model_instance get_optimizer WarmUpLR PolynomialLR ConstantLR get_scheduler convert_state_dict alpha_blend recursive_glob get_logger convert_state_dict get_list subplots n_classes data_loader DataLoader runningScore device list set_title set_xlabel savefig load_state_dict to eval boxplot get_scores items print set_ylabel set_style get_loader len array2dataframe get_list concat n_classes data_loader runningScore get_loader len concat DataFrame model get_loss_function zero_grad data_loader n_classes DataLoader runningScore DataParallel save device get_optimizer seed list get_scheduler load_state_dict to get update format eval avg manual_seed info isfile item get_scores load averageMeter time items join backward print parameters reset get_loader loss_fn optimizer_cls step len brainwebLoader format print squeeze n_classes DataLoader decode_segmap pjoin numpy imsave hvsmrLoader mrbrainsLoader size cross_entropy interpolate view format info ResNetRNN ResNetRNN ResNetRNN ResNetRNN ResNetRNN ResNetUNet ResNetFCN zeros abs pop deepcopy _get_model_instance model format info pop format get info zeros size OrderedDict items list join setFormatter format replace getLogger addHandler Formatter setLevel INFO FileHandler | # Recurrent Decoding Cell [](https://github.com/shakex/Recurrent-Decoding-Cell/blob/master/LICENSE) This is the PyTorch implementation for **AAAI 2020** paper [Segmenting Medical MRI via Recurrent Decoding Cell](https://arxiv.org/abs/1911.09401) by Ying Wen, Kai Xie, Lianghua He.  ## Overview [Recurrent Decoding Cell](https://github.com/shakex/Recurrent-Decoding-Cell) (RDC) is a novel feature fusion unit used in the encoder-decoder segmentation network for MRI segmentation. RDC leverages convolutional RNNs (e.g. [ConvLSTM](https://arxiv.org/abs/1506.04214), [ConvGRU](https://arxiv.org/abs/1706.03458)) to memorize the long-term context information from the previous layers in the decoding phase. The RDC based encoder-decoder network named Convolutional Recurrent Decoding Network (CRDN) achieves promising semgmentation reuslts -- **99.34% dice score on [BrainWeb](https://brainweb.bic.mni.mcgill.ca/brainweb/), 91.26% dice score on [MRBrainS](https://mrbrains13.isi.uu.nl/), and 88.13% dice score on [HVSMR](http://segchd.csail.mit.edu/data.html)**. The model is also robust to image noise and intensity non-uniformity in medical MRI. ## Models Implemented * [FCN](https://arxiv.org/abs/1411.4038) * [SegNet](https://arxiv.org/abs/1511.00561) * [UNet](https://arxiv.org/abs/1505.04597) | 3,606 |
shalder/knowledge_graph | ['anomaly detection'] | ['The Semantic Knowledge Graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domain'] | my_kg_code/cv_match.py my_kg_code/build_jobs.py my_kg_code/build_candidates.py my_kg_code/scalability_create_cv_jd.py my_kg_code/headway_gremlin_connection.py my_kg_code/build_skills_kw_graph.py my_kg_code/jd_cv_graph_match.py my_kg_code/load_graph.py find_min_path cv_jd_match find_common_ancestor HWGremlinConnection SkillsKWGraph id toList str list join print find_common_ancestor difference find_min_path floor intersection append len | #Semantic Knowledge Graph *A graph structure, build automatically from a corpus of data, for traversing and measuring relationships within a domain* The Semantic Knowledge Graph serves as a data scientist's toolkit, allowing you to discover and compare any entities modeled within a corpus of data from any domain. For example, if you indexed a corpus of job postings, you could figure out what the most related job titles are for the phrase "account manager", and subsequently what the top skills are for each of those job titles. You can also use the system to rank a list of entities or keywords based upon their statistical relationship with any other group of entities or terms, and you can traverse these relationships any number of levels deep. The Semantic Knowledge Graph will allow you to slice and dice the universe of terms and entites represented within your corpus in order to discover as many of these insights as you have the time and curiosity to pursue. The Semantic Knowledge Graph is packaged as a request handler plugin for the popular Apache Solr search engine. Fundamentally, you must create a schema representing your corpus of data (from any domain), send the corpus of documents to Solr (script to do this is included), and then you can send queries to the Semantic Knowledge Graph request handler to discover and/or score relationships. ## License and Citation The Semantic Knowledge Graph source code is licensed under the [ASL 2.0 License](https://github.com/careerbuilder/semantic-knowledge-graph/blob/master/LICENSE). A research paper describing the Semantic Knowledge Graph is being published in the proceedings of the 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics: [The Semantic Knowledge Graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domain](https://arxiv.org/abs/1609.00464). Please cite the Semantic Knowledge Graph in your publications if it helps your research: @article{grainger2016SemanticKnowledgeGraph, | 3,607 |
shana9pm/ArtStyleTransfer | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | utils.py Settings.py ArtStyleTransfer.py get_total_loss get_feature_reps calculate_style_loss get_grad callbackF calculate_content_loss get_content_loss get_style_loss get_Gram_matrix calculate_loss get_content_loss_forward get_style_loss_forward outImageUtils outImageUtils2 inputImageUtils postprocess_array save_original_size preprocess_array build_parser BuildModel reshape function get_content_loss get_feature_reps get_style_loss abs sum square variable get_Gram_matrix zip dot transpose get_total_loss function gradients reshape astype input reshape transpose output eval get_layer append update deepcopy calculate_style_loss calculate_content_loss join format postprocess_array save_original_size append get_style_loss get_feature_reps get_content_loss reshape function reshape function add_argument ArgumentParser preprocess_input img_to_array load_img size variable expand_dims open preprocess_input expand_dims astype placeholder placeholder resize preprocess_array array open fromarray save resize VGG16 reshape astype clip reshape astype | # ArtStyleTransfer
This is an implementation of paper "A Neural Algorithm of Artistic Style".
https://arxiv.org/pdf/1508.06576.pdf for reference.
This is the final project for Columbia University STAT GR5242 Advance Machine Learning.
Group Leader:Haiqi Li
Team Member:Yutong Zhang,Yifan Wu,Ziyan Xu
| 3,608 |
shannonfenn/Multi-Label-Curricula-via-Minimum-Feature-Selection | ['multi label classification'] | ['Target Curricula via Selection of Minimum Feature Sets: a Case Study in Boolean Networks'] | boolnet/test/functions/convert.py runexp.py boolnet/test/test_utils.py check_config.py boolnet/test/test_biterror.py boolnet/test/test_optimisers.py boolnet/exptools/config_schemata.py boolnet/utils.py boolnet/exptools/learn_boolnet.py boolnet/learners.py boolnet/test/test_example_generator.py boolnet/test/test_boolnet.py boolnet/test/test_networkstate.py boolnet/exptools/config_filtering.py boolnet/test/test_operator_iterator.py boolnet/test/functions/validate.py boolnet/conftest.py boolnet/optimisers.py setup.py boolnet/test/test_config_filtering.py boolnet/exptools/config_tools.py main create_result_dir run_parallel run_tasks initialise initialise_logging run_sequential parse_arguments run_scooped main scoop_worker_wrapper test_location error_matrix_harness scalar_function any_function per_output_function fn_value_stop_criterion BasicLearner LAHC_perc LAHC RestartLocalSearch SA HC stepped_exp_decrease geometric sample_packed order_from_rank unpack inverse_permutation rank_with_ties_broken partition_packed PackedMatrix list_regex_match filter_keys path_value_pairs conditionally_required permutation integer_multiplier_string conditionally_forbidden dump_results_partial update_nested build_filename generate_tasks file_instance split_instance load_samples load_dataset get_seed split_variables_from_base generated_instance generate_configurations ValidationError validate_schema ExperimentJSONEncoder insert_default_log_keys seed_rng add_noise build_training_set build_states build_result_map random_network learn_bool_net test_e3_general test_function_value test_e6_general TestFunctionality harness_to_fixture TestExceptions adder2 network_file_instance test_filter_keys test_list_regex_match test_path_value_pairs TestIterators TestExampleGenerator TestExampleIteratorFactory test_output_matrix test_function_value test_construction_exceptions state test_from_operator_combined_attributes single_move_invariant test_from_operator_func_value test_multiple_move_output_different test_activation_matrix test_multiple_reverts_error_matrix to_binary test_input_matrix multiple_move_invariant output_different packed_zeros test_target_matrix harness_to_fixture test_error_matrix harnesses_with_property single_layer_zero run_instance test_move_with_initial_evaluation test_pre_evaluated_network state_harness build_instance all_possible_inputs sample_type test_single_move_output_different test_multiple_moves_error_matrix state_params TestIterators TestExampleIteratorFactory TestSA test_order_from_rank harness test_rank_with_ties_broken main main unique_rows load format experiment print add_argument verbose ArgumentParser generate_tasks parse_args generate_configurations len join basicConfig add_argument ArgumentParser join format strftime makedirs load create_result_dir experiment result_dir name exit copy run_parallel run_sequential write run_scooped update dump_results_partial map_as_completed Bar finish next enumerate update dump_results_partial map Bar finish next enumerate join time batch_mode initialise initialise_logging parse_arguments load join param copy dirname array unpackmat is_minimiser optimum range Ne partition_columns PackedMatrix Ne sample_columns zeros_like enumerate permutation zeros_like where unique zip rank_with_ties_broken list items isinstance zip list_regex_match join path_value_pairs format seed randint dump write fileno fsync flush get items list isinstance get join seed load build_filename isinstance hsplit get_seed array update file_instance split_instance copy load_samples zip append generated_instance vstack build_filename build_filename num_operands operator_from_name schema get extend update deepcopy update_nested enumerate experiment_schema Bar load_dataset split_variables_from_base instance_schema next validate_schema append insert_default_log_keys update deepcopy Bar append next enumerate seed randint min choice randint empty range get int dtype astype shuffle Ne zeros packmat round range seed_rng monotonic get add_noise int No endswith build_training_set build_result_map run get BNState state_from_operator add_function sample_packed partition_packed function_from_name build_states restarts warning function_value values list get update format network partial_networks enumerate gates join fullmatch feature_sets filter_keys array function_from_name arange zeros_like evaluate shape eval_class assert_array_almost_equal zeros_like evaluate shape eval_class assert_array_almost_equal packmat array zeros_like evaluate shape eval_class assert_array_almost_equal packmat array uint32 safe_load BoolNet array sorted path_value_pairs filter_keys dict deepcopy vstack partition_packed PackedMatrix uintp deepcopy param arange size array array eval_func build_instance error_matrix revert_move move_to_random_neighbour range function_from_name add_function assert_array_almost_equal function_value MoveAndExpected harness_to_fixture copy append array PackedMatrix vstack assert_array_equal input_matrix eval_func build_instance assert_array_equal target_matrix eval_func build_instance assert_array_equal eval_func output_matrix build_instance assert_array_equal eval_func activation_matrix build_instance error_matrix assert_array_equal eval_func build_instance output_different output_different eval_func build_instance error_matrix move expected assert_array_equal apply_move eval_func build_instance error_matrix assert_array_equal apply_move eval_func build_instance error_matrix revert_move reversed assert_array_equal apply_move eval_func_s evaluate build_instance revert_all_moves activation_matrix set_gates assert_array_equal eval_func_f move_to_random_neighbour range gates state_from_operator run_instance state_from_operator run_instance rank_with_ties_broken order_from_rank asarray array tolist savez descr view unique | shannonfenn/Multi-Label-Curricula-via-Minimum-Feature-Selection | 3,609 |
shantanur8/Real-time-neural-style-transfer | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | main.py | shantanur8/Real-time-neural-style-transfer | 3,610 |
shanxuanchen/FacialExpressionRecognition | ['facial expression recognition'] | ['Island Loss for Learning Discriminative Features in Facial Expression Recognition'] | IslandLoss/generateData.py IslandLoss/modelGenerator.py CenterLoss/CenterLoss.py CenterLoss/generateData.py CenterLoss/modelGenerator.py IslandLoss/IslandLoss.py Reverse resize_to_224 getData2 CenterLossLayer generateModel2 baseModel Reverse resize_to_224 getData2 IslandLossLayer generateModel2 baseModel float16 reshape astype resize int resize_to_224 strip open append split Model load_weights output compile Model Input baseModel | # FacialExpressionRecognition （持续更新ing） Use CenterLoss , IslandLoss at, solve the Facial Expression Recognition task By Keras. (Use FER2013 Dataset) # Center Loss 论文地址：https://link.springer.com/chapter/10.1007/978-3-319-46478-7_31 # Island Loss 论文地址: https://arxiv.org/abs/1710.03144 | 3,611 |
sharathadavanne/seld-dcase2020 | ['sound event localization and detection'] | ['A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection'] | batch_feature_extraction.py keras_model.py seld.py metrics/SELD_evaluation_metrics.py visualize_SELD_output.py cls_data_generator.py utils/interpolateDirections.py cls_feature_class.py parameter.py metrics/evaluation_metrics.py calculate_dev_results_from_dcase_output.py get_nb_files DataGenerator FeatureClass nCr create_folder get_model load_seld_model masked_mse get_params main collect_test_labels plot_functions collect_classwise_data plot_func er_overall_framewise distance_between_gt_pred compute_doa_scores_clas reshape_3Dto2D distance_between_spherical_coordinates_rad f1_overall_framewise compute_doa_scores_regr_xyz cart2sph SELDMetrics distance_between_cartesian_coordinates f1_overall_1sec compute_sed_scores early_stopping_metric er_overall_1sec sph2cart distance_between_gt_pred_xyz compute_doa_scores_regr least_distance_between_gt_pred distance_between_spherical_coordinates_rad SELDMetrics early_stopping_metric distance_between_cartesian_coordinates get_nb_files DataGenerator FeatureClass nCr create_folder get_model load_seld_model masked_mse get_params main collect_test_labels plot_functions collect_classwise_data plot_func er_overall_framewise distance_between_gt_pred compute_doa_scores_clas reshape_3Dto2D distance_between_spherical_coordinates_rad f1_overall_framewise compute_doa_scores_regr_xyz cart2sph SELDMetrics distance_between_cartesian_coordinates f1_overall_1sec compute_sed_scores early_stopping_metric er_overall_1sec sph2cart distance_between_gt_pred_xyz compute_doa_scores_regr least_distance_between_gt_pred get_nb_files DataGenerator FeatureClass nCr create_folder get_model load_seld_model masked_mse get_params main collect_test_labels plot_functions collect_classwise_data plot_func er_overall_framewise distance_between_gt_pred compute_doa_scores_clas reshape_3Dto2D distance_between_spherical_coordinates_rad f1_overall_framewise compute_doa_scores_regr_xyz cart2sph SELDMetrics distance_between_cartesian_coordinates f1_overall_1sec compute_sed_scores early_stopping_metric er_overall_1sec sph2cart distance_between_gt_pred_xyz compute_doa_scores_regr least_distance_between_gt_pred append int print format makedirs format print exit Model summary Input compile enumerate repeat_elements cast print format exit int list format items print exit dict format print get_data_gen_mode generate zeros subplot list plot grid close savefig figure legend range len reshape_3Dto2D get_nb_frames SELDMetrics plot_functions save predict_generator early_stopping_metric append get_filelist segment_labels range format replace regression_label_format_to_output_format DataGenerator nb_frames_1s compute_doa_scores_regr_xyz fit_generator compute_sed_scores write_output_format_file get_params FeatureClass zeros get_frame_per_file enumerate join get_data_sizes time create_folder collect_test_labels load_seld_model print compute_seld_scores get_nb_classes get_model update_seld_scores_xyz append list keys list plot set_xticklabels set_yticklabels grid xlim keys float sum sum int ceil zeros float max range int ceil zeros float max range er_overall_1sec f1_overall_1sec int astype float sum enumerate int astype float sum enumerate int T get_matrix_index distance_between_gt_pred pi float sum array range distance_between_spherical_coordinates_rad linear_sum_assignment zeros sum array linear_sum_assignment zeros sum array distance_between_cartesian_coordinates arccos cos pi sin abs clip sqrt arccos clip pi cos sin sqrt arctan2 mean distance_between_spherical_coordinates_rad linear_sum_assignment zeros sum array distance_between_cartesian_coordinates | # DCASE 2020: Sound event localization and detection (SELD) task [Please visit the official webpage of the DCASE 2020 Challenge for details missing in this repo](http://dcase.community/challenge2020/task-sound-event-localization-and-detection). As the baseline method for the [SELD task](https://www.aane.in/research/computational-audio-scene-analysis-casa/sound-event-localization-detection-and-tracking), we use the SELDnet method studied in the following papers. If you are using this baseline method or the datasets in any format, then please consider citing the following two papers. If you want to read more about [generic approaches to SELD then check here](https://www.aane.in/research/computational-audio-scene-analysis-casa/sound-event-localization-detection-and-tracking). > Sharath Adavanne, Archontis Politis, Joonas Nikunen and Tuomas Virtanen, "Sound event localization and detection of overlapping sources using convolutional recurrent neural network" in IEEE Journal of Selected Topics in Signal Processing (JSTSP 2018) > Sharath Adavanne, Archontis Politis and Tuomas Virtanen, "Localization, Detection and Tracking of Multiple Moving Sound Sources with a Convolutional Recurrent Neural Network" in the Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2019) ## BASELINE METHOD In comparison to the SELDnet studied in the papers above, we have changed the following to improve its performance and evaluate the performance better. * **Features**: The original SELDnet employed naive phase and magnitude components of the spectrogram as the input feature for all input formats of audio. In this baseline method, we use separate features for first-order Ambisonic (FOA) and microphone array (MIC) datasets. As the interaural level difference feature, we employ the 64-band mel energies extracted from each channel of the input audio for both FOA and MIC. To encode the interaural time difference features, we employ intensity vector features for FOA, and generalized cross correlation features for MIC. * **Loss/Objective**: The original SELDnet employed mean square error (MSE) for the DOA loss estimation, and this was computed irrespecitve of the presence or absence of the sound event. In the current baseline, we used a masked-MSE, which computes MSE only when the sound event is active in the reference. | 3,612 |
shariqfarooq123/AdaBins | ['depth estimation', 'monocular depth estimation'] | ['AdaBins: Depth Estimation using Adaptive Bins'] | train.py models/layers.py models/unet_adaptive_bins.py dataloader.py model_io.py models/__init__.py models/miniViT.py evaluate.py infer.py utils.py loss.py DepthDataLoader _is_numpy_image preprocessing_transforms ToTensor remove_leading_slash _is_pil_image DataLoadPreprocess eval compute_errors convert_arg_line_to_args predict_tta _is_pil_image ToTensor InferenceHelper _is_numpy_image SILogLoss BinsChamferLoss load_checkpoint save_weights save_checkpoint load_weights validate convert_arg_line_to_args is_rank_zero colorize main_worker train log_images count_parameters denormalize colorize RunningAverage compute_errors b64_to_pil RunningAverageDict edges PointCloudHelper PixelWiseDotProduct PatchTransformerEncoder mViT Encoder DecoderBN UnetAdaptiveBins UpSampleBN maximum mean sqrt abs log max_depth min_depth interpolate Tensor to numpy clip print save_dir RunningAverageDict makedirs split join state_dict save makedirs join save makedirs load join load_state_dict load get list items replace load_state_dict startswith cmapper get_cmap colorize log int init_process_group batch_size print set_device num_workers build convert_sync_batchnorm distributed DataParallel rank DistributedDataParallel train cuda gpu data validate model clip_grad_norm_ zero_grad w_chamfer criterion_ueff save_checkpoint device log load_state_dict to range SILogLoss epoch inf eval init same_lr criterion_bins backward print AdamW parameters OneCycleLR step len to sub sobel | shariqfarooq123/AdaBins | 3,613 |
sharmaGIT/D-NetPAD | ['iris recognition'] | ['D-NetPAD: An Explainable and Interpretable Iris Presentation Attack Detector'] | train_DNetPAD.py dataset_Loader.py test_DNetPAD.py Evaluation.py fineTune_DNetPAD.py datasetLoader evaluation | # D-NetPAD Code for Iris Presentation Attack Detection based on DenseNet Architecture. # Requirement Pytorch, Numpy, Scipy, Pillow # Description The D-NetPAD takes a cropped iris image as input and produces a PA score between 0 and 1, where 0 means bonafide and 1 means presentation attack. Sample cropped iris images are provided in CroppedImages folder. <img src="https://github.com/sharmaGIT/D-NetPAD/blob/master/Images/Architecture.jpg" width="800" height="200"> # Testing The model can be downloaded from [here](https://drive.google.com/drive/folders/178o1ujoUb3b5HYi8_51b1r8XZ2wbEYc7?usp=sharing). Copy the model into the Model folder and run the following command: python test_D-NetPAD.py -imageFolder CroppedImages | 3,614 |
sharpenb/Uncertainty-Event-Prediction | ['anomaly detection'] | ['Uncertainty on Asynchronous Time Event Prediction'] | src/models/gp.py src/models/base.py src/train.py src/data.py src/models/dirichlet.py src/models/dpp.py split_on_sequence break_down_sequences pad_sequence get_dataset create_dataset split_on_data load_dataset list_datasets training_loop BaseModel get_shape Dirichlet get_shape DirichletPointProcess get_shape GaussianProcess dict set load len len range concatenate convert_to_tensor int32 from_tensor_slices float32 split_on_sequence break_down_sequences pad_sequence create_dataset split_on_data load_dataset max append run as_list shape | # Uncertainty on Asynchronous Time Event Prediction This repository presents the experiments of the paper: [Uncertainty on Asynchronous Time Event Prediction](http://papers.nips.cc/paper/9445-uncertainty-on-asynchronous-time-event-prediction.pdf)<br> Marin Bilos, Bertrand Charpentier, Stephan Günnemann<br> Conference on Neural Information Processing Systems (NeurIPS), 2019. *Spotlight talk* [[Paper](http://papers.nips.cc/paper/9445-uncertainty-on-asynchronous-time-event-prediction.pdf)|[Publisher](http://papers.nips.cc/paper/9445-uncertainty-on-asynchronous-time-event-prediction)] ## Model Diagram <div id="banner" style="overflow: hidden;justify-content:space-around;"> <div class="" style="display: inline-block;"> <img src="assets/model-diagram.png" width="1000" height="200"> | 3,615 |
shatha2014/FashionRec | ['word embeddings'] | ['Deep Text Mining of Instagram Data Without Strong Supervision'] | information_extraction/Preprocessor.py information_extraction/InformationExtraction.py clean_data/ig_json_clean.py information_extraction/deepomatic.py cnn_classification/pre_process.py wordvecs/pre_process_corpus.py information_extraction/rankings_helper.py cnn_classification/model_serving.py wordvecs/wordvecs.py information_extraction/dd_client.py information_extraction/ie_eval.py cnn_classification/train.py information_extraction/fast_analysis.py information_extraction/dd_bench.py writeToFile format saveCorpusFile parse_raw corpora_stats append_corpus mapRow select main sparkConf parse_args cleanOutputDir parseUser classify load_graph print_ops load_vocab batch_iter learn_generative make_binary_labels sum_dicts test_labels_to_csv testlabels_to_onehot combine_labels2 dict_to_onehot merge_totals make_binary_labels2 normalize_labels combine_labels sparkConf votes_to_onehot keyword_labeling_funs majority_vote pre_process_labels combine_labels_features pre_process_features filter_dict split hype_random build_graph save_model plot setup init_graph define_placeholders restore_model training_step apply_labeling define_optimizer main hype_grid combine_rdd parse_args TaskError HTTPHelper BadStatus Client lookup_company_probase emoji_LF create_tf_idf clarifai_lookup read_gazetter filterOccurenceCount parse_raw analyze_user lookup_material_probase filterEmojis premap_post parse_args text_clustering_LF map_post re_rank_materials sparkConf main re_rank_brands liktekit_LF deepomatic_lookup brand_rank_mapper clean_text deep_detect_lookup google_vision_LF material_rank_mapper lookup_company_probase create_vocab create_tf_idf column get_userhandles calculate_p_values_sem_syn_probase read_gazetter t_test_p_value eval_vectors lookup_material_probase parse_args extract_features get_hashtags text_clustering_LF syntactic_clustering_predict re_rank_materials eval annotations_to_csv main re_rank_brands calculate_p_values_vectors text_clustering_LF_syntactic semantic_clustering_predict brand_rank_mapper clean_text eval_syn_vs_sem material_rank_mapper InformationExtractor PreProcessor apk ndcg_at_k dcg_score r_precision mapk average_precision_score dcg_from_ranking dcg_at_k mean_average_precision mean_reciprocal_rank precision_at_k ranking_precision_score average_precision ndcg_score ndcg_from_ranking normalize_clean corpora_stats append_corpora removeUnFrequentWords main parse_args save_results test_fashion_retrofitted train_fasttext_fashionrec append_to_file test_word2vec_google_news_300 accuracy_percentage test_fashion save_to_file save_fasttext_bin_to_vec test_glove_twitter_200 readCorpus corpus_stats test_glove_wiki_300 retrofit test_glove_commoncrawl_300 read_lexicon norm_word save_glove_bin_to_vec train_all test_fasttext_wiki_300 gensimModelToDict test train_word2vec_fashionrec main train_glove_fashionrec save_retrofitted_to_vec does_not_match retrofitting convert_gensim_to_word2vec_format my_vector_getter convert_glove_to_word2vec_format train train_word2vec_wordrank json data join hasattr replace text caption id comments edges add_argument ArgumentParser saveCorpusFile collect saveAsTextFile dirname array makedirs map selectExpr writeToFile format parallelized parse_raw print output select repartition toDF documentformat input rmtree join dirname append walk makedirs join format print len set open append walk split listdir list corpora_stats append_corpus output input parse_args cleanOutputDir SQLContext SparkContext print name get_operations time info tolist len pad run get_json append range split items sorted list float sum range len append range items list append items list range set print len format set open list learn_generative uniform load_word2vec_format append fit_transform range testlabels_to_onehot VocabularyProcessor astype vocabulary_ set load items majority_vote constant float32 split zeros array len add append array range len int format learned_lf_stats print csr_matrix to_csv SnorkelSession append marginals train array range GenerativeModel enumerate items list sum values items sorted list set add float sum keys len items list load normalize_labels loads open textFile collect repartition SparkContext load collect textFile repartition open append SparkContext load open seed int permutation arange format print float len int permutation arange min array range len load append open load format print len loads load_word2vec_format open float32 int32 placeholder constant reshape concat enumerate len count_nonzero ones_like zeros_like minimize where AdamOptimizer cast equal scalar global_variables_initializer Session local_variables_initializer run join format hamming_loss print transpose average_precision_score dict precision_recall_curve add_summary append f1_score range scalar run pretrained reset_default_graph vectordim vectors str labelstest labelstrain savetxt append vocabulary_ main print featurestrain combine_labels_features testsplit featurestest maxdocumentsize split array len pretrained reset_default_graph vectordim vectors str tolist labelstest labelstrain savetxt append vocabulary_ main print featurestrain combine_labels_features testsplit featurestest maxdocumentsize split array len batch_iter build_graph save_model init_graph training_step Saver save merge_all savetxt format plot define_placeholders FileWriter close define_optimizer zip graph print array freeze_graph write_graph graph_def save show str print xlabel grid ylabel title legend Saver test_labels_to_csv multichannel pre_process_labels pre_process_features keyword_labeling_funs merge_totals combine_labels2 combine_labels encode get Dictionary TfidfModel enumerate Counter SparkConf csv deepdetect textanalysis format liktekit_LF text_clustering_LF print deepomatic_lookup liketkit clarifai deep_detect_lookup google deepomatic google_vision_LF clarifai_lookup Row map_candidates_to_ontology image_path google_vision_lookup dict map_candidates_to_ontology dict url map_candidates_to_ontology url dict map_candidates_to_ontology dict url emojis emoji_classification extend dict links find_closest_semantic_hierarchy hieararchy liketkit_classification id top_category_items sorted list find_closest_semantic_hierarchy segmented_hashtags hashtags re_rank_materials caption styles set patterns re_rank_brands keys tags companies dict comments find_closest_semantic userhandles lookup_material_probase rank_probase_result_material lookup_probase lookup_company_probase rank_probase_result_company lookup_probase decode imagepath strip search extend group startswith segment tokenize append format partitions parse_raw print map output repartition saveAsTextFile zipWithIndex input count materials create_tf_idf brands conf probasematerials vectors deepdetect read_gazetter analyze_user itemtopcategory styles InformationExtractor patterns items probasebrands load append open add list enumerate set range get_userhandles join list items strip extend set segment tokenize append get_hashtags format text_clustering_LF print append enumerate format print text_clustering_LF_syntactic append enumerate strip list sorted len tolist dcg_at_k mean_average_precision split precision_at_k append range ndcg_at_k replace set mean_reciprocal_rank float items remove dcg_score average_precision_score ranking_precision_score average_precision ndcg_score r_precision hieararchy sorted list re_rank_materials styles set companies patterns find_closest_syntactic2 dict hieararchy re_rank_brands keys find_closest_syntactic_hierarchy load t_test_p_value len column append range open column t_test_p_value range ttest_rel materials create_tf_idf brands conf probasematerials open read_gazetter input append extract_features itemtopcategory styles patterns InformationExtractor annotations_to_csv eval load items probasebrands labels semantic_clustering_predict testvectors len materials create_tf_idf brands conf probasematerials vectors calculate_p_values_sem_syn_probase read_gazetter input extract_features itemtopcategory syntactic_clustering_predict styles patterns InformationExtractor annotations_to_csv eval items probasebrands labels semantic_clustering_predict len basicConfig eval_syn_vs_sem WordNetLemmatizer update TweetTokenizer words WordNetLemmatizer set PerceptronTagger sum take unique sum range unique len log2 take arange len dcg_score log2 asarray arange len dcg_from_ranking len enumerate size sorted dcg_at_k update str print words set print str input join normalize_clean append_corpora removeUnFrequentWords len set list format float sum range len join evaluate_word_pairs accuracy append_to_file accuracy_percentage load_word2vec_format save_results load_word2vec_format save_results load_word2vec_format save_results load_word2vec_format save_results load_word2vec_format save_results load_word2vec_format save_results load_word2vec_format str save_results load save_word2vec_format wv glove2word2vec get_words str format corpus_stats save_model load_model print now train_unsupervised save_to_file save_fasttext_bin_to_vec str format corpus_stats print now wv LineSentence Word2Vec save save_to_file str format corpus_stats print now wv LineSentence Word2Vec save save_to_file add_dictionary str format corpus_stats readCorpus print fit Glove now dictionary save save_to_file save_glove_bin_to_vec matrix Corpus split ravel index lower search open split deepcopy set intersection keys range len list keys format print doesnt_match load_word2vec_format split int gensimModelToDict retrofit read_lexicon load_word2vec_format save_retrofitted_to_vec test_fasttext_wiki_300 test_glove_wiki_300 test_word2vec_google_news_300 test_fashion test_glove_twitter_200 train_word2vec_fashionrec train_glove_fashionrec train_fasttext_fashionrec train_fasttext_fashionrec | # Instagram Text Analysis - FashionRec ## Overview This repository contains scripts and programs used to analyze textual data from the Instagram API in the context of automatic intelligent fashion identification, classification and recommendation. ### Modules - `./clean_data` contains scripts for converting JSON Instagram data to csv,tsv,reduced json files that are suited for processing - `./data_exploration` contains some descriptive analytics about the data - `./fasttext_on_spark` contains a scalable implementation of FastText to run on Spark clusters - `./information_extraction` contains scripts for unsupervised information extraction using semantic/syntactic clustering of text to match it to an ontology/domain data as well as using several external APIs as sources of distant supervision. Also contains scripts for evaluation. - `./wordvecs` contains scripts training and evaluating word embeddings, as well as normalizing text corpora to be used for training word embeddings. - `./cnn_classification` contains scripts for training a weakly supervised CNN text classifier and model serving. | 3,616 |
shawnlimn/UnifiedParser_RST | ['discourse parsing'] | ['A Unified Linear-Time Framework for Sentence-Level Discourse Parsing'] | Parser/module.py Segmenter/model.py Parser/main.py Parser/Metric.py Segmenter/solver.py Parser/Training.py Segmenter/train.py Parser/model.py Parser/DataHandler.py get_RelationAndNucleus getLabelOrdered parse_args getEvalData getBatchMeasure getMeasurement getMicroMeasure ParsingNet DecoderRNN PointerAtten LabelClassifier EncoderRNN getBatchData getBatchData_training Train PointerNetworks get_batch_test sample_a_sorted_batch_from_numpy TrainSolver arange tolist extend append array len split add_argument ArgumentParser split findall range append len list set intersection getEvalData keys len range getMeasurement len deepcopy list tolist argsort sample array range len deepcopy list tolist argsort sample array range len pop deepcopy list insert argsort sample array range append len deepcopy list sample array range len | # A Unified Linear-Time Framework for Sentence-Level Discourse Parsing This repository contains the source code of our paper "[A Unified Linear-Time Framework for Sentence-Level Discourse Parsing](https://arxiv.org/abs/1905.05682)" in ACL 2019. ## Getting Started These instructions will help you to run our unified discourse parser based on RST dataset. ### Prerequisites ``` * PyTorch 0.4 or higher * Python 3 * AllenNLP ``` | 3,617 |
shayanray/ApplyingCommonSense | ['word embeddings'] | ['ConceptNet 5.5: An Open Multilingual Graph of General Knowledge'] | experiments/Vanshaj/src/config.py experiments/Shayan/src-exp2_lastsent/models/cnn_bilstm_attn_sent.py experiments/Shayan/src/models/ffnn.py experiments/Shayan/src-exp3-cnnbilstm_lastsent/models/cnn_lstm_sent.py experiments/Shayan/src-exp3-cnnbilstm_lastsent/models/ffnn.py experiments/Vanshaj/src/models/skip_thoughts/nbsvm.py experiments/Gulshan/src_expt1/models/SiameseLSTM.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/training/tools.py experiments/Shayan/src/siameseLSTM.py experiments/Vanshaj/src/models/skip_thoughts/eval_trec.py experiments/Gulshan/src_expt2/models/skip_thoughts/decoding/layers.py experiments/Shayan/src/models/skip_thoughts/training/model.py experiments/Gulshan/src_expt1/models/skip_thoughts/decoding/homogeneous_data.py experiments/Gulshan/src_expt2/models/skip_thoughts/eval_classification.py experiments/Gulshan/src_expt2/models/skip_thoughts/skipthoughts.py experiments/Gulshan/src_expt1/models/skip_thoughts/eval_msrp.py experiments/Vanshaj/src/models/skip_thoughts/decoding/utils.py experiments/Shayan/demo-src/config.py experiments/Gulshan/src/models/skip_thoughts/training/tools.py experiments/Gulshan/src_expt2/run.py experiments/Shayan/demo-src/sentiment.py experiments/Gulshan/src_expt2/models/skip_thoughts/training/homogeneous_data.py experiments/Shayan/src/models/cnn_ngrams.py experiments/Gulshan/src/training_utils.py experiments/Gulshan/src_expt1/models/skip_thoughts/eval_classification.py experiments/Shayan/src/models/skip_thoughts/eval_rank.py experiments/Shayan/src-exp3-cnnbilstm_lastsent/models/SiameseLSTM.py experiments/Gulshan/src_expt1/training_utils.py experiments/Vanshaj/src/models/skip_thoughts/decoding/homogeneous_data.py experiments/Gulshan/src/models/skip_thoughts/eval_rank.py experiments/Gulshan/src/models/skip_thoughts/decoding/homogeneous_data.py experiments/Gulshan/src_expt2/models/skip_thoughts/dataset_handler.py experiments/Gulshan/src_expt1/models/cnn_ngrams.py experiments/Shayan/src/models/skip_thoughts/decoding/vocab.py experiments/Vanshaj/src/models/skip_thoughts/decoding/tools.py experiments/Shayan/src-exp2_lastsent/negative_endings.py experiments/Gulshan/src_expt2/config.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/eval_sick.py experiments/Vanshaj/src/models/skip_thoughts/dataset_handler.py experiments/Gulshan/src_expt1/models/skip_thoughts/training/homogeneous_data.py experiments/Shayan/src/models/skip_thoughts/training/tools.py experiments/Gulshan/src_expt2/models/skip_thoughts/decoding/vocab.py experiments/Shayan/src/models/skip_thoughts/training/homogeneous_data.py experiments/Shayan/src-exp3-cnnbilstm_lastsent/config.py experiments/Shayan/src-exp2_lastsent/siameseLSTM.py experiments/Gulshan/src_expt3_only_last_sentence/models/ffnn.py experiments/Shayan/src-exp2_lastsent/models/cnn_ngrams.py experiments/Shayan/src/negative_endings.py experiments/Shayan/src-exp2_lastsent/training_utils.py experiments/Shayan/src/conceptnetnumbatch.py experiments/Gulshan/src_expt1/models/skip_thoughts/decoding/vocab.py experiments/Shayan/src-exp2_lastsent/conceptnetnumbatch.py experiments/Gulshan/src_expt1/data_utils.py experiments/Vanshaj/src/models/skip_thoughts/training/vocab.py experiments/Shayan/demo-src/run.py experiments/Gulshan/src/models/cnn_ngrams.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/eval_classification.py experiments/Shayan/src/models/cnn_bilstm_attn_sent.py experiments/Gulshan/src_expt2/models/skip_thoughts/training/layers.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/decoding/tools.py experiments/Gulshan/src_expt2/models/skip_thoughts/decoding/search.py experiments/Shayan/src-exp2_lastsent/models/cnn_lstm_sent.py experiments/Vanshaj/src/models/skip_thoughts/training/train.py experiments/Gulshan/src/preprocessing.py experiments/Gulshan/src/models/skip_thoughts/eval_trec.py experiments/Gulshan/src_expt2/models/skip_thoughts/eval_rank.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/training/vocab.py experiments/Gulshan/src_expt1/models/skip_thoughts/training/vocab.py experiments/Gulshan/src/models/SiameseLSTM.py experiments/Gulshan/src_expt3_only_last_sentence/models/cnn_lstm_sent.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/training/utils.py experiments/Vanshaj/src/models/skip_thoughts/decoding/layers.py experiments/Gulshan/src_expt2/models/cnn_lstm_sent.py experiments/Shayan/src-exp3-cnnbilstm_lastsent/models/cnn_bilstm_attn_sent.py experiments/Gulshan/src_expt2/preprocessing.py experiments/Gulshan/src_expt2/models/skip_thoughts/decoding/homogeneous_data.py experiments/Vanshaj/src/models/skip_thoughts/decoding/optim.py experiments/Gulshan/src_expt1/models/skip_thoughts/decoding/layers.py experiments/Vanshaj/src/models/skip_thoughts/decoding/train.py experiments/Shayan/src/models/skip_thoughts/nbsvm.py experiments/Vanshaj/src/training_utils.py experiments/Gulshan/src_expt1/models/skip_thoughts/decoding/search.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/decoding/optim.py experiments/Shayan/src/models/skip_thoughts/decoding/utils.py experiments/Gulshan/src/siameseLSTM.py experiments/Gulshan/src_expt2/models/skip_thoughts/eval_sick.py experiments/Gulshan/src_expt1/models/skip_thoughts/eval_trec.py experiments/Shayan/demo-src/conceptnetnumbatch.py experiments/Shayan/src/models/skip_thoughts/eval_sick.py experiments/Gulshan/src/models/skip_thoughts/training/homogeneous_data.py experiments/Gulshan/src_expt1/config.py experiments/Vanshaj/src/models/skip_thoughts/training/layers.py experiments/Gulshan/src/models/skip_thoughts/decoding/search.py experiments/Gulshan/src/run.py experiments/Gulshan/src_expt3_only_last_sentence/training_utils.py experiments/Gulshan/src/models/skip_thoughts/decoding/layers.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/training/model.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/training/homogeneous_data.py experiments/Shayan/src/models/skip_thoughts/training/utils.py experiments/Gulshan/src_expt1/sentiment.py experiments/Vanshaj/src/run.py experiments/Vanshaj/src/models/skip_thoughts/decoding/model.py experiments/Shayan/src-exp3-cnnbilstm_lastsent/conceptnetnumbatch.py experiments/Gulshan/src_expt1/models/skip_thoughts/eval_sick.py experiments/Gulshan/src_expt2/data_utils.py experiments/Shayan/src/models/skip_thoughts/training/vocab.py experiments/Shayan/src/preprocessing.py experiments/Vanshaj/src/models/skip_thoughts/eval_sick.py experiments/Shayan/src/config.py experiments/Shayan/src/data_utils.py experiments/Gulshan/src/models/skip_thoughts/eval_sick.py experiments/Gulshan/src_expt3_only_last_sentence/run.py experiments/Gulshan/src/models/skip_thoughts/decoding/tools.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/eval_msrp.py experiments/Shayan/src/run.py experiments/Shayan/src-exp3-cnnbilstm_lastsent/data_utils.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/skipthoughts.py experiments/Gulshan/src_expt3_only_last_sentence/models/cnn_ngrams.py experiments/Vanshaj/src/models/skip_thoughts/training/optim.py experiments/Gulshan/src_expt2/models/skip_thoughts/training/model.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/decoding/search.py experiments/Gulshan/src_expt1/preprocessing.py experiments/Gulshan/src_expt2/models/skip_thoughts/decoding/tools.py experiments/Shayan/src/models/skip_thoughts/dataset_handler.py experiments/Shayan/src/models/skip_thoughts/training/optim.py experiments/Gulshan/src_expt1/models/skip_thoughts/training/utils.py experiments/Gulshan/src_expt3_only_last_sentence/preprocessing.py experiments/Gulshan/src/models/skip_thoughts/decoding/model.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/decoding/utils.py experiments/Gulshan/src/models/ffnn.py experiments/Shayan/src/models/skip_thoughts/skipthoughts.py experiments/Vanshaj/src/data_utils.py experiments/Gulshan/src_expt1/models/skip_thoughts/skipthoughts.py experiments/Gulshan/src/negative_endings.py experiments/Vanshaj/src/models/skip_thoughts/skipthoughts.py experiments/Gulshan/src_expt2/siameseLSTM.py experiments/Gulshan/src_expt2/models/skip_thoughts/training/train.py experiments/Shayan/demo-src/negative_endings.py experiments/Shayan/src/models/skip_thoughts/decoding/model.py experiments/Gulshan/src_expt1/run.py experiments/Gulshan/src_expt1/models/ffnn.py experiments/Gulshan/src/config.py experiments/Gulshan/src_expt1/models/cnn_lstm_sent.py experiments/Gulshan/src/models/skip_thoughts/skipthoughts.py experiments/Vanshaj/src/sentiment.py experiments/Shayan/src/models/skip_thoughts/decoding/layers.py experiments/Gulshan/src/models/skip_thoughts/decoding/train.py experiments/Gulshan/src_expt2/models/ffnn.py experiments/Gulshan/src_expt1/models/skip_thoughts/training/train.py experiments/Gulshan/src_expt2/models/skip_thoughts/decoding/optim.py experiments/Shayan/src-exp2_lastsent/sentiment.py experiments/Vanshaj/src/models/skip_thoughts/eval_classification.py experiments/Vanshaj/src/models/ffnn.py experiments/Vanshaj/src/models/skip_thoughts/training/model.py experiments/Gulshan/src_expt2/models/SiameseLSTM.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/training/optim.py experiments/Gulshan/src/models/skip_thoughts/training/utils.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/eval_rank.py experiments/Gulshan/src_expt2/models/cnn_ngrams.py experiments/Shayan/src-exp3-cnnbilstm_lastsent/preprocessing.py experiments/Gulshan/src_expt1/models/skip_thoughts/decoding/utils.py experiments/Gulshan/src_expt2/models/skip_thoughts/nbsvm.py experiments/Shayan/demo-src/models/cnn_bilstm_attn_sent.py experiments/Vanshaj/src/models/skip_thoughts/decoding/vocab.py experiments/Gulshan/src_expt1/negative_endings.py experiments/Gulshan/src/models/skip_thoughts/training/vocab.py experiments/Shayan/demo-src/models/SiameseLSTM.py experiments/Vanshaj/src/models/cnn_ngrams.py experiments/Gulshan/src/models/skip_thoughts/decoding/optim.py experiments/Shayan/src/models/skip_thoughts/decoding/train.py experiments/Shayan/src/models/skip_thoughts/eval_msrp.py experiments/Vanshaj/src/negative_endings.py experiments/Gulshan/src_expt2/models/skip_thoughts/training/optim.py experiments/Shayan/demo-src/data_utils.py experiments/Gulshan/src_expt3_only_last_sentence/negative_endings.py experiments/Gulshan/src_expt1/models/skip_thoughts/training/layers.py experiments/Shayan/src/models/skip_thoughts/eval_trec.py experiments/Shayan/demo-src/models/cnn_lstm_sent.py experiments/Gulshan/src_expt1/models/skip_thoughts/decoding/train.py experiments/Gulshan/src/sentiment.py experiments/Gulshan/src/models/skip_thoughts/eval_msrp.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/decoding/homogeneous_data.py experiments/Shayan/src-exp3-cnnbilstm_lastsent/sentiment.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/decoding/train.py experiments/Shayan/demo-src/models/ffnn.py experiments/Vanshaj/src/models/skip_thoughts/training/homogeneous_data.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/eval_trec.py experiments/Gulshan/src_expt1/models/skip_thoughts/nbsvm.py experiments/Gulshan/src_expt2/models/skip_thoughts/training/utils.py experiments/Gulshan/src/models/cnn_lstm_sent.py experiments/Shayan/demo-src/preprocessing.py experiments/Gulshan/src_expt1/models/skip_thoughts/training/tools.py experiments/Shayan/src/models/skip_thoughts/training/train.py experiments/Vanshaj/src/models/skip_thoughts/eval_msrp.py experiments/Gulshan/src/data_utils.py experiments/Shayan/src/models/cnn_lstm_sent.py experiments/Gulshan/src/models/skip_thoughts/training/layers.py experiments/Gulshan/src_expt2/models/skip_thoughts/training/tools.py experiments/Gulshan/src/models/skip_thoughts/decoding/vocab.py experiments/Gulshan/src_expt2/models/skip_thoughts/training/vocab.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/decoding/layers.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/nbsvm.py experiments/Shayan/src-exp2_lastsent/models/SiameseLSTM.py experiments/Gulshan/src/models/skip_thoughts/decoding/utils.py experiments/Shayan/src/models/skip_thoughts/decoding/homogeneous_data.py experiments/Gulshan/src_expt1/models/skip_thoughts/decoding/tools.py experiments/Gulshan/src_expt2/models/skip_thoughts/eval_msrp.py experiments/Gulshan/src_expt2/models/skip_thoughts/decoding/model.py experiments/Shayan/src/models/skip_thoughts/training/layers.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/decoding/model.py experiments/Shayan/conceptnet-poc/newsgroup/with cnet/pretrained_word_embeddings.py experiments/Shayan/src-exp2_lastsent/data_utils.py experiments/Shayan/src-exp3-cnnbilstm_lastsent/training_utils.py experiments/Vanshaj/src/siameseLSTM.py experiments/Vanshaj/src/models/cnn_lstm_sent.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/training/train.py experiments/Vanshaj/src/models/skip_thoughts/decoding/search.py experiments/Shayan/conceptnet-poc/newsgroup/without cnet/pretrained_word_embeddings.py experiments/Gulshan/src_expt3_only_last_sentence/siameseLSTM.py experiments/Shayan/demo-src/training_utils.py experiments/Gulshan/src_expt1/models/skip_thoughts/training/optim.py experiments/Shayan/src-exp3-cnnbilstm_lastsent/models/cnn_ngrams.py experiments/Shayan/src/models/skip_thoughts/decoding/search.py experiments/Gulshan/src_expt3_only_last_sentence/data_utils.py experiments/Gulshan/src_expt3_only_last_sentence/config.py experiments/Gulshan/src_expt1/models/skip_thoughts/eval_rank.py experiments/Shayan/src-exp2_lastsent/run.py experiments/Gulshan/src_expt3_only_last_sentence/sentiment.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/dataset_handler.py experiments/Shayan/src-exp3-cnnbilstm_lastsent/siameseLSTM.py experiments/Gulshan/src_expt1/models/skip_thoughts/training/model.py experiments/Shayan/src-exp3-cnnbilstm_lastsent/negative_endings.py experiments/Gulshan/src_expt1/models/skip_thoughts/decoding/model.py experiments/Shayan/src-exp2_lastsent/preprocessing.py experiments/Gulshan/src/models/skip_thoughts/training/model.py experiments/Gulshan/src/models/skip_thoughts/nbsvm.py experiments/Gulshan/src_expt1/siameseLSTM.py experiments/Gulshan/src_expt2/models/skip_thoughts/decoding/utils.py experiments/Gulshan/src_expt2/training_utils.py experiments/Shayan/src/models/skip_thoughts/decoding/optim.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/decoding/vocab.py experiments/Shayan/src/models/SiameseLSTM.py experiments/Gulshan/src/models/skip_thoughts/training/optim.py experiments/Gulshan/src_expt1/models/skip_thoughts/dataset_handler.py experiments/Vanshaj/src/preprocessing.py experiments/Shayan/src/models/skip_thoughts/eval_classification.py experiments/Shayan/demo-src/models/cnn_ngrams.py experiments/Shayan/src/training_utils.py experiments/Shayan/src-exp2_lastsent/config.py experiments/Shayan/src-exp2_lastsent/models/ffnn.py experiments/Vanshaj/src/models/skip_thoughts/eval_rank.py experiments/Shayan/demo-src/siameseLSTM.py experiments/Gulshan/src/models/skip_thoughts/dataset_handler.py experiments/Vanshaj/src/models/skip_thoughts/training/utils.py experiments/Gulshan/src_expt2/models/skip_thoughts/decoding/train.py experiments/Shayan/src/sentiment.py experiments/Gulshan/src/models/skip_thoughts/eval_classification.py experiments/Gulshan/src_expt2/models/skip_thoughts/eval_trec.py experiments/Gulshan/src/models/skip_thoughts/training/train.py experiments/Shayan/src-exp3-cnnbilstm_lastsent/run.py experiments/Gulshan/src_expt1/models/skip_thoughts/decoding/optim.py experiments/Gulshan/src_expt2/sentiment.py experiments/Gulshan/src_expt3_only_last_sentence/models/SiameseLSTM.py experiments/Gulshan/src_expt3_only_last_sentence/models/skip_thoughts/training/layers.py experiments/Vanshaj/src/models/SiameseLSTM.py experiments/Vanshaj/src/models/skip_thoughts/training/tools.py experiments/Gulshan/src_expt2/negative_endings.py experiments/Shayan/src/models/skip_thoughts/decoding/tools.py combine_story lemmatize generate_vocabulary merge_vocab combine_sentences load_vocabulary get_indexes_from_words generate_vocab_pos count_words word_cleaning get_answers get_wordnet_pos filter_words pad_endings get_words_from_indexes get_index_from_tag check_for_unk load_pos_vocabulary generate_vocab_pos_upenn load_data get_context_sentence random_negative_endings Negative_endings combine_matrix_cols open_csv_asmatrix pos_tagging_text load_train_val_datasets_pos_tagged sentences_to_sentiments train_verifier full_sentence_story preprocess pos_tag_dataset eliminate_id endings_to_sentiments generate_binary_verifiers get_predicted_labels _setup_argparser get_latest_model initialize_negative_endings get_submission_filename get_verifiers_difference sentiment_analysis load_sentiment sentence_sentiment cosine_distance siamese exponent_neg_manhattan_distance embedding cosine_dist_output_shape initialize_negative_endings eliminate_tags_corpus eliminate_tags_in_val_endings embedding batches_backwards_neg_endings pad_restructure_valset pad_restructure_trainset eliminate_tags_in_contexts full_stories_together batch_iter_train_cnn batch_iter_val_cnn_sentiment aggregate_contexts batch_iter_backward_train_cnn eliminate_tags_corpus batches_pos_neg_endings batch_iter_val_cnn Cnn_lstm_sentiment CNN_ngrams transform batch_iter FFNN SiameseLSTM load_rt load_subj load_cr shuffle_data load_data load_mpqa compute_labels compute_nb eval_nested_kfold is_number eval_kfold evaluate load_data feats build_encoder fflayer l2norm param_init_fflayer adam load_params init_params i2t zipp build_model trainer itemlist get_layer _p norm_weight init_tparams evaluate unzip linear validate_options t2i train_model evaluate encode_labels prepare_model load_data prepare_labels evaluate eval_kfold prepare_data load_data build_dict process_text tokenize compute_ratio init_params_bi build_encoder param_init_gru load_model encode load_params init_params load_tables gru_layer Encoder ortho_weight preprocess get_layer _p word_features nn_words norm_weight init_tparams build_encoder_bi nn prepare_data HomogeneousData gru_layer param_init_fflayer get_layer param_init_gru fflayer init_params build_sampler build_model adam gen_sample run_sampler load_model trainer zipp tanh norm_weight init_tparams relu unzip linear concatenate ortho_weight itemlist _p load_params build_dictionary load_dictionary save_dictionary grouper prepare_data HomogeneousData gru_layer param_init_fflayer get_layer param_init_gru fflayer init_params build_encoder build_encoder_w2v build_model adam train_regressor get_embeddings load_googlenews_vectors load_model lookup_table preprocess encode apply_regressor trainer zipp tanh norm_weight init_tparams concatenate unzip linear ortho_weight itemlist _p load_params build_dictionary load_dictionary save_dictionary combine_story lemmatize generate_vocabulary merge_vocab combine_sentences load_vocabulary get_indexes_from_words generate_vocab_pos count_words word_cleaning get_answers get_wordnet_pos filter_words pad_endings get_words_from_indexes get_index_from_tag check_for_unk load_pos_vocabulary generate_vocab_pos_upenn load_data get_context_sentence random_negative_endings Negative_endings combine_matrix_cols open_csv_asmatrix pos_tagging_text load_train_val_datasets_pos_tagged sentences_to_sentiments train_verifier full_sentence_story preprocess pos_tag_dataset eliminate_id endings_to_sentiments generate_binary_verifiers get_predicted_labels _setup_argparser get_latest_model initialize_negative_endings get_submission_filename get_verifiers_difference sentiment_analysis load_sentiment sentence_sentiment cosine_distance siamese exponent_neg_manhattan_distance embedding cosine_dist_output_shape initialize_negative_endings eliminate_tags_corpus eliminate_tags_in_val_endings embedding batches_backwards_neg_endings pad_restructure_valset pad_restructure_trainset eliminate_tags_in_contexts full_stories_together batch_iter_train_cnn batch_iter_val_cnn_sentiment aggregate_contexts batch_iter_backward_train_cnn eliminate_tags_corpus batches_pos_neg_endings batch_iter_val_cnn Cnn_lstm_sentiment CNN_ngrams transform batch_iter FFNN SiameseLSTM load_rt load_subj load_cr shuffle_data load_data load_mpqa compute_labels compute_nb eval_nested_kfold is_number eval_kfold evaluate load_data feats build_encoder fflayer l2norm param_init_fflayer adam load_params init_params i2t zipp build_model trainer itemlist get_layer _p norm_weight init_tparams evaluate unzip linear validate_options t2i train_model evaluate encode_labels prepare_model load_data prepare_labels evaluate eval_kfold prepare_data load_data build_dict process_text tokenize compute_ratio init_params_bi build_encoder param_init_gru load_model encode load_params init_params load_tables gru_layer Encoder ortho_weight preprocess get_layer _p word_features nn_words norm_weight init_tparams build_encoder_bi nn prepare_data HomogeneousData gru_layer param_init_fflayer get_layer param_init_gru fflayer init_params build_sampler build_model adam gen_sample run_sampler load_model trainer zipp tanh norm_weight init_tparams relu unzip linear concatenate ortho_weight itemlist _p load_params build_dictionary load_dictionary save_dictionary grouper prepare_data HomogeneousData gru_layer param_init_fflayer get_layer param_init_gru fflayer init_params build_encoder build_encoder_w2v build_model adam train_regressor get_embeddings load_googlenews_vectors load_model lookup_table preprocess encode apply_regressor trainer zipp tanh norm_weight init_tparams concatenate unzip linear ortho_weight itemlist _p load_params build_dictionary load_dictionary save_dictionary combine_story lemmatize generate_vocabulary merge_vocab combine_sentences load_vocabulary get_indexes_from_words generate_vocab_pos count_words word_cleaning get_answers get_wordnet_pos filter_words pad_endings get_words_from_indexes get_index_from_tag check_for_unk load_pos_vocabulary generate_vocab_pos_upenn load_data get_context_sentence random_negative_endings Negative_endings combine_matrix_cols open_csv_asmatrix pos_tagging_text load_train_val_datasets_pos_tagged sentences_to_sentiments train_verifier full_sentence_story preprocess pos_tag_dataset eliminate_id endings_to_sentiments generate_binary_verifiers get_predicted_labels _setup_argparser get_latest_model initialize_negative_endings get_submission_filename get_verifiers_difference sentiment_analysis load_sentiment sentence_sentiment cosine_distance siamese exponent_neg_manhattan_distance embedding cosine_dist_output_shape initialize_negative_endings eliminate_tags_corpus eliminate_tags_in_val_endings embedding batches_backwards_neg_endings pad_restructure_valset pad_restructure_trainset eliminate_tags_in_contexts full_stories_together batch_iter_train_cnn batch_iter_val_cnn_sentiment aggregate_contexts batch_iter_backward_train_cnn eliminate_tags_corpus batches_pos_neg_endings batch_iter_val_cnn Cnn_lstm_sentiment CNN_ngrams transform batch_iter FFNN SiameseLSTM load_rt load_subj load_cr shuffle_data load_data load_mpqa compute_labels compute_nb eval_nested_kfold is_number eval_kfold evaluate load_data feats build_encoder fflayer l2norm param_init_fflayer adam load_params init_params i2t zipp build_model trainer itemlist get_layer _p norm_weight init_tparams evaluate unzip linear validate_options t2i train_model evaluate encode_labels prepare_model load_data prepare_labels evaluate eval_kfold prepare_data load_data build_dict process_text tokenize compute_ratio init_params_bi build_encoder param_init_gru load_model encode load_params init_params load_tables gru_layer Encoder ortho_weight preprocess get_layer _p word_features nn_words norm_weight init_tparams build_encoder_bi nn prepare_data HomogeneousData gru_layer param_init_fflayer get_layer param_init_gru fflayer init_params build_sampler build_model adam gen_sample run_sampler load_model trainer zipp tanh norm_weight init_tparams relu unzip linear concatenate ortho_weight itemlist _p load_params build_dictionary load_dictionary save_dictionary grouper prepare_data HomogeneousData gru_layer param_init_fflayer get_layer param_init_gru fflayer init_params build_encoder build_encoder_w2v build_model adam train_regressor get_embeddings load_googlenews_vectors load_model lookup_table preprocess encode apply_regressor trainer zipp tanh norm_weight init_tparams concatenate unzip linear ortho_weight itemlist _p load_params build_dictionary load_dictionary save_dictionary combine_story lemmatize generate_vocabulary merge_vocab combine_sentences load_vocabulary get_indexes_from_words generate_vocab_pos count_words word_cleaning get_answers get_wordnet_pos filter_words pad_endings get_words_from_indexes get_index_from_tag check_for_unk load_pos_vocabulary generate_vocab_pos_upenn load_data get_context_sentence random_negative_endings Negative_endings combine_matrix_cols open_csv_asmatrix pos_tagging_text load_train_val_datasets_pos_tagged sentences_to_sentiments train_verifier full_sentence_story preprocess pos_tag_dataset eliminate_id endings_to_sentiments generate_binary_verifiers get_predicted_labels _setup_argparser get_latest_model initialize_negative_endings get_submission_filename get_verifiers_difference sentiment_analysis load_sentiment sentence_sentiment cosine_distance siamese exponent_neg_manhattan_distance embedding cosine_dist_output_shape initialize_negative_endings eliminate_tags_corpus eliminate_tags_in_val_endings embedding batches_backwards_neg_endings pad_restructure_valset pad_restructure_trainset eliminate_tags_in_contexts full_stories_together batch_iter_train_cnn batch_iter_val_cnn_sentiment aggregate_contexts batch_iter_backward_train_cnn eliminate_tags_corpus batches_pos_neg_endings batch_iter_val_cnn Cnn_lstm_sentiment CNN_ngrams transform batch_iter FFNN SiameseLSTM load_rt load_subj load_cr shuffle_data load_data load_mpqa compute_labels compute_nb eval_nested_kfold is_number eval_kfold evaluate load_data feats build_encoder fflayer l2norm param_init_fflayer adam load_params init_params i2t zipp build_model trainer itemlist get_layer _p norm_weight init_tparams evaluate unzip linear validate_options t2i train_model evaluate encode_labels prepare_model load_data prepare_labels evaluate eval_kfold prepare_data load_data build_dict process_text tokenize compute_ratio init_params_bi build_encoder param_init_gru load_model encode load_params init_params load_tables gru_layer Encoder ortho_weight preprocess get_layer _p word_features nn_words norm_weight init_tparams build_encoder_bi nn prepare_data HomogeneousData gru_layer param_init_fflayer get_layer param_init_gru fflayer init_params build_sampler build_model adam gen_sample run_sampler load_model trainer zipp tanh norm_weight init_tparams relu unzip linear concatenate ortho_weight itemlist _p load_params build_dictionary load_dictionary save_dictionary grouper prepare_data HomogeneousData gru_layer param_init_fflayer get_layer param_init_gru fflayer init_params build_encoder build_encoder_w2v build_model adam train_regressor get_embeddings load_googlenews_vectors load_model lookup_table preprocess encode apply_regressor trainer zipp tanh norm_weight init_tparams concatenate unzip linear ortho_weight itemlist _p load_params build_dictionary load_dictionary save_dictionary cosine_similarity test_cnet_numbatch load_cnet_numbatch combine_story lemmatize generate_vocabulary merge_vocab combine_sentences load_vocabulary get_indexes_from_words generate_vocab_pos count_words word_cleaning get_answers get_wordnet_pos filter_words pad_endings load_cnet_numbatch get_words_from_indexes get_index_from_tag check_for_unk load_pos_vocabulary generate_vocab_pos_upenn load_data get_context_sentence random_negative_endings Negative_endings combine_matrix_cols open_csv_asmatrix pos_tagging_text load_train_val_datasets_pos_tagged sentences_to_sentiments train_verifier full_sentence_story preprocess pos_tag_dataset eliminate_id endings_to_sentiments generate_binary_verifiers get_predicted_labels _setup_argparser get_latest_model initialize_negative_endings get_submission_filename get_verifiers_difference sentiment_analysis load_sentiment sentence_sentiment cosine_distance siamese exponent_neg_manhattan_distance embedding cosine_dist_output_shape initialize_negative_endings eliminate_tags_corpus eliminate_tags_in_val_endings embedding batches_backwards_neg_endings pad_restructure_valset pad_restructure_trainset eliminate_tags_in_contexts full_stories_together batch_iter_train_cnn batch_iter_val_cnn_sentiment aggregate_contexts batch_iter_backward_train_cnn eliminate_tags_corpus batches_pos_neg_endings batch_iter_val_cnn Cnn_bilstm_attn_sentiment Cnn_lstm_sentiment CNN_ngrams transform batch_iter FFNN SiameseLSTM cosine_similarity test_cnet_numbatch load_cnet_numbatch combine_story lemmatize generate_vocabulary merge_vocab combine_sentences load_vocabulary get_indexes_from_words generate_vocab_pos count_words word_cleaning get_answers get_wordnet_pos filter_words pad_endings load_cnet_numbatch get_words_from_indexes get_index_from_tag check_for_unk load_pos_vocabulary generate_vocab_pos_upenn load_data get_context_sentence random_negative_endings Negative_endings combine_matrix_cols open_csv_asmatrix pos_tagging_text load_train_val_datasets_pos_tagged sentences_to_sentiments train_verifier full_sentence_story preprocess pos_tag_dataset eliminate_id endings_to_sentiments generate_binary_verifiers get_predicted_labels _setup_argparser get_latest_model initialize_negative_endings get_submission_filename get_verifiers_difference sentiment_analysis load_sentiment sentence_sentiment cosine_distance siamese exponent_neg_manhattan_distance embedding cosine_dist_output_shape initialize_negative_endings eliminate_tags_corpus eliminate_tags_in_val_endings embedding batches_backwards_neg_endings pad_restructure_valset pad_restructure_trainset eliminate_tags_in_contexts full_stories_together batch_iter_train_cnn batch_iter_val_cnn_sentiment aggregate_contexts batch_iter_backward_train_cnn eliminate_tags_corpus batches_pos_neg_endings batch_iter_val_cnn Cnn_bilstm_attn_sentiment Cnn_lstm_sentiment CNN_ngrams transform batch_iter FFNN SiameseLSTM load_rt load_subj load_cr shuffle_data load_data load_mpqa compute_labels compute_nb eval_nested_kfold is_number eval_kfold evaluate load_data feats build_encoder fflayer l2norm param_init_fflayer adam load_params init_params i2t zipp build_model trainer itemlist get_layer _p norm_weight init_tparams evaluate unzip linear validate_options t2i train_model evaluate encode_labels prepare_model load_data prepare_labels evaluate eval_kfold prepare_data load_data build_dict process_text tokenize compute_ratio init_params_bi build_encoder param_init_gru load_model encode load_params init_params load_tables gru_layer Encoder ortho_weight preprocess get_layer _p word_features nn_words norm_weight init_tparams build_encoder_bi nn prepare_data HomogeneousData gru_layer param_init_fflayer get_layer param_init_gru fflayer init_params build_sampler build_model adam gen_sample run_sampler load_model trainer zipp tanh norm_weight init_tparams relu unzip linear concatenate ortho_weight itemlist _p load_params build_dictionary load_dictionary save_dictionary grouper prepare_data HomogeneousData gru_layer param_init_fflayer get_layer param_init_gru fflayer init_params build_encoder build_encoder_w2v build_model adam train_regressor get_embeddings load_googlenews_vectors load_model lookup_table preprocess encode apply_regressor trainer zipp tanh norm_weight init_tparams concatenate unzip linear ortho_weight itemlist _p load_params build_dictionary load_dictionary save_dictionary cosine_similarity test_cnet_numbatch load_cnet_numbatch combine_story lemmatize generate_vocabulary merge_vocab combine_sentences load_vocabulary get_indexes_from_words generate_vocab_pos count_words word_cleaning get_answers get_wordnet_pos filter_words pad_endings load_cnet_numbatch get_words_from_indexes get_index_from_tag check_for_unk load_pos_vocabulary generate_vocab_pos_upenn load_data get_context_sentence random_negative_endings Negative_endings combine_matrix_cols open_csv_asmatrix pos_tagging_text load_train_val_datasets_pos_tagged sentences_to_sentiments train_verifier full_sentence_story preprocess pos_tag_dataset eliminate_id endings_to_sentiments generate_binary_verifiers get_predicted_labels _setup_argparser get_latest_model initialize_negative_endings get_submission_filename get_verifiers_difference sentiment_analysis load_sentiment sentence_sentiment cosine_distance siamese exponent_neg_manhattan_distance embedding cosine_dist_output_shape initialize_negative_endings eliminate_tags_corpus eliminate_tags_in_val_endings embedding batches_backwards_neg_endings pad_restructure_valset pad_restructure_trainset eliminate_tags_in_contexts full_stories_together batch_iter_train_cnn batch_iter_val_cnn_sentiment aggregate_contexts batch_iter_backward_train_cnn eliminate_tags_corpus batches_pos_neg_endings batch_iter_val_cnn Cnn_bilstm_attn_sentiment Cnn_lstm_sentiment CNN_ngrams transform batch_iter FFNN SiameseLSTM cosine_similarity test_cnet_numbatch load_cnet_numbatch combine_story lemmatize generate_vocabulary merge_vocab combine_sentences load_vocabulary get_indexes_from_words generate_vocab_pos count_words word_cleaning get_answers get_wordnet_pos filter_words pad_endings load_cnet_numbatch get_words_from_indexes get_index_from_tag check_for_unk load_pos_vocabulary generate_vocab_pos_upenn load_data get_context_sentence random_negative_endings Negative_endings combine_matrix_cols open_csv_asmatrix pos_tagging_text load_train_val_datasets_pos_tagged sentences_to_sentiments train_verifier full_sentence_story preprocess pos_tag_dataset eliminate_id endings_to_sentiments generate_binary_verifiers get_predicted_labels _setup_argparser get_latest_model initialize_negative_endings get_submission_filename get_verifiers_difference sentiment_analysis load_sentiment sentence_sentiment cosine_distance siamese exponent_neg_manhattan_distance embedding cosine_dist_output_shape initialize_negative_endings eliminate_tags_corpus eliminate_tags_in_val_endings embedding batches_backwards_neg_endings pad_restructure_valset pad_restructure_trainset eliminate_tags_in_contexts full_stories_together batch_iter_train_cnn batch_iter_val_cnn_sentiment aggregate_contexts batch_iter_backward_train_cnn eliminate_tags_corpus batches_pos_neg_endings batch_iter_val_cnn Cnn_bilstm_attn_sentiment Cnn_lstm_sentiment CNN_ngrams transform batch_iter FFNN SiameseLSTM combine_story lemmatize generate_vocabulary merge_vocab combine_sentences load_vocabulary get_indexes_from_words generate_vocab_pos count_words word_cleaning get_answers get_wordnet_pos filter_words pad_endings load_cnet_numbatch get_words_from_indexes get_index_from_tag check_for_unk load_pos_vocabulary generate_vocab_pos_upenn load_data get_context_sentence random_negative_endings Negative_endings combine_matrix_cols open_csv_asmatrix pos_tagging_text load_train_val_datasets_pos_tagged sentences_to_sentiments train_verifier full_sentence_story preprocess pos_tag_dataset eliminate_id endings_to_sentiments generate_binary_verifiers get_predicted_labels _setup_argparser get_latest_model initialize_negative_endings get_submission_filename get_verifiers_difference sentiment_analysis load_sentiment sentence_sentiment cosine_distance siamese exponent_neg_manhattan_distance embedding cosine_dist_output_shape initialize_negative_endings eliminate_tags_corpus eliminate_tags_in_val_endings embedding batches_backwards_neg_endings pad_restructure_valset pad_restructure_trainset eliminate_tags_in_contexts full_stories_together batch_iter_train_cnn batch_iter_val_cnn_sentiment aggregate_contexts batch_iter_backward_train_cnn eliminate_tags_corpus batches_pos_neg_endings batch_iter_val_cnn Cnn_lstm_sentiment CNN_ngrams transform batch_iter FFNN SiameseLSTM load_rt load_subj load_cr shuffle_data load_data load_mpqa compute_labels compute_nb eval_nested_kfold is_number eval_kfold evaluate load_data feats build_encoder fflayer l2norm param_init_fflayer adam load_params init_params i2t zipp build_model trainer itemlist get_layer _p norm_weight init_tparams evaluate unzip linear validate_options t2i train_model evaluate encode_labels prepare_model load_data prepare_labels evaluate eval_kfold prepare_data load_data build_dict process_text tokenize compute_ratio init_params_bi build_encoder param_init_gru load_model encode load_params init_params load_tables gru_layer Encoder ortho_weight preprocess get_layer _p word_features nn_words norm_weight init_tparams build_encoder_bi nn prepare_data HomogeneousData gru_layer param_init_fflayer get_layer param_init_gru fflayer init_params build_sampler build_model adam gen_sample run_sampler load_model trainer zipp tanh norm_weight init_tparams relu unzip linear concatenate ortho_weight itemlist _p load_params build_dictionary load_dictionary save_dictionary grouper prepare_data HomogeneousData gru_layer param_init_fflayer get_layer param_init_gru fflayer init_params build_encoder build_encoder_w2v build_model adam train_regressor get_embeddings load_googlenews_vectors load_model lookup_table preprocess encode apply_regressor trainer zipp tanh norm_weight init_tparams concatenate unzip linear ortho_weight itemlist _p load_params build_dictionary load_dictionary save_dictionary load_data values tuple lemmatize ndenumerate shape append download empty WordNetLemmatizer startswith update print Counter update print dict count_words keys enumerate print list print dict isfile keys append shape empty ndenumerate isinstance isinstance print ndenumerate shape append empty print extend shape empty range combine_sentences format print extend shape empty range shape empty ndenumerate load list print tolist set dict enumerate load list set dict download keys enumerate update DataFrame choice empty ravel range len word_tokenize iterrows asarray format print load_data save download DataFrame head len load_vocabulary print reshape filter_words check_for_unk pad_endings delete load_pos_vocabulary shape generate_vocabulary append word_cleaning merge_vocab range len print asarray read_csv print preprocess append get_answers append range len join sentence_sentiment print extend around append join sentence_sentiment print around append append range len print map load_data DataFrame head len parse_known_args add_argument ArgumentParser join format model print exit dirname abspath max int time join str model get_latest_model filter_corpus_tags Negative_endings load_vocabulary append len print asarray list asarray polarity_scores SentimentIntensityAnalyzer values join sorted iterrows print load_data polarity_scores SentimentIntensityAnalyzer download DataFrame get items fit_on_texts asarray list print pad_sequences texts_to_sequences word_index close dict split open zeros Tokenizer len format embedding model print Embedding Sequential Adam add Model summary LSTM Input compile l2_normalize print append append deepcopy list print aggregate_contexts append len print append words_substitution_approach range len print backwards_words_substitution_approach append range len print append append eliminate_tags_in_val_endings eliminate_tags_in_contexts full_stories_together range len print eliminate_tags_in_contexts full_stories_together range batches_pos_neg_endings len batches_backwards_neg_endings print eliminate_tags_in_contexts full_stories_together range len asarray format get_index_from_tag print tolist save append range asarray format get_index_from_tag print reshape shape save append range len eliminate_tags_in_val_endings print eliminate_tags_in_contexts sentences_to_sentiments full_stories_together endings_to_sentiments range len concatenate print reshape sentiment_analysis load_data tile encode empty range values len reshape permutation range len load_rt print load_subj load_cr shuffle_data encode load_mpqa compute_labels zeros len shuffle RandomState arange len score print hstack len fit LogisticRegression mean load_data append compute_nb argmax KFold build_dict process_text compute_ratio str f1 eval_kfold print score LogisticRegression load_data encode predict fit join word_tokenize close open append split float zeros set range len append print fit len LogisticRegression mean f1 argmax array predict KFold items set_value OrderedDict items get_value OrderedDict OrderedDict shared items load items sqrt rand norm_weight astype sqrt sum matrix sum l2norm tile matrix l2norm list function sqr get_value float32 sqrt zip append shared values warn i2t function init_tparams load_params build_encoder t2i init_params f_emb function arange build_encoder open f_grad_shared f_update load_params init_params range i2t dump RandomState build_model grad shuffle f_emb time init_tparams savez print unzip validate_options random_integers t2i scalar len median reshape flatten floor append zeros range len median T dot shape floor zeros array range len train_model arange encode_labels shuffle mse prepare_model dot predict_proba Sequential add Dense Activation compile str arange print set_weights prepare_model dot predict_proba get_weights fit floor astype range enumerate prepare_labels prepare_data array set score len range split update tokenize Counter list set dict keys log len list lil_matrix sort set tokenize enumerate load_tables function init_tparams print load_params build_encoder_bi init_params_bi build_encoder init_params load list strip close OrderedDict zip append open int list defaultdict print ones len preprocess append zeros keys range enumerate load word_tokenize tokenize encode flatten print enumerate list norm zeros keys range len list print flatten keys enumerate list list warn norm_weight OrderedDict norm_weight tensor3 matrix tensor3 concatenate svd randn uniform ortho_weight norm_weight astype ortho_weight concatenate dot scan alloc encode len astype zip append max enumerate _step RandomStreams set_subtensor zeros_like reshape dict shape flatten softmax vector switch function print softmax alloc matrix argmax argmax astype f_next copy flatten f_init zip append range array log enumerate len items RandomStreams build_sampler dict append reshape join gen_sample load_dictionary build_sampler list defaultdict append shared gen_sample switch HomogeneousData sqrt keys enumerate minimum items norm_weight reshape prepare_data float32 dict set_subtensor ndim zeros sum range list OrderedDict keys enumerate values split arange dict RandomStreams reshape dict matrix RandomStreams tensor3 load_googlenews_vectors lookup_table build_encoder_w2v load_word2vec_format train_regressor get_embeddings range apply_regressor OrderedDict flatten range list defaultdict fit OrderedDict zeros keys LinearRegression OrderedDict astype keys enumerate grouper sqrt sum print read_csv print cosine_similarity as_matrix shape | # Applying CommonSense to ROC Stories The baseline implementation of the ROC Story Cloze Test was adapted from: https://github.com/robertah/nlu_project_2 Common Sense word embeddings from concept net were used instead of regular glove embeddings along with various deep learning techniques: Additional Experiments on Newsgroup20 dataset referenced from : https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html a. CNN-LSTM and CNN-BiLSTM models: Experiments done on this model include: * "CNN-LSTM (baseline) [15k samples]" * "CNN-LSTM (with concept-net) [15k samples]" * "CNN-LSTM (with concept-net + lastsentence)" | 3,618 |
shayanray/Vehicle-Speed-Prediction-from-dashboard-camera | ['scene parsing', 'semantic segmentation'] | ['Semantic Understanding of Scenes through the ADE20K Dataset'] | src/scripts/extract_vgg16_relu6.py src/scripts/extract_vgg16_relu6_test.py src/scripts/extract_test_images_from_mp4video.py write_image_to_disk hook get_vgg_feature load_crop_extract_save hook get_vgg_feature load_crop_extract_save join read str imwrite CAP_PROP_POS_FRAMES print writerow set append model_vgg16 list print get_vgg_feature rfind dict shape stack mean savemat append numpy crop open | # Using Computer Vision and Deep Learning techniques to Predict Vehicle Speed From Dashcam Videos.... # Challenges/Motivation: • Geometry unforgiving • Single camera lacks the sense of depth • Input: Dashcam Video (datasets from highway and sub-urban driving) • Processing: Convert video to images (frame by frame); find discernible features between two successive image frames using various techniques to ascertain speed. Obviously, it is a Regression problem. • Output: Speed at every image frame • Evaluation Metric(s): MSE(Mean Square Error) a.k.a L2 loss | 3,619 |
sheim/vibly | ['gaussian processes'] | ['A Learnable Safety Measure'] | demos/measure_learning/run_learning_examples.py viability/viability.py models/slip.py demos/computeQ_slip.py plotting/visualise_viability.py measure/estimate_measure.py plotting/guinea_plotters.py demos/TAC11/progressive/slice_plot.py models/spaceship5.py models/satellite.py demos/TAC11/neg_proxy_clean/negproxy_plot.py demos/TAC11/pos_proxy/posproxy_plot.py demos/TAC11/parsimonious/plot_parsimoniousP.py demos/TAC11/viability/VIS_viability.py demos/damping_study/generate_new_perturbations.py demos/computeQ_spaceship4.py measure/active_sampling.py demos/TAC11/computeQ_satellite.py models/spaceship.py models/spaceship4.py demos/TAC11/neg_proxy_clean/plot_split_negproxy.py plotting/animation.py control/control.py demos/TAC11/pos_proxy/VIS_posproxy.py plotting/corl_plotters.py demos/TAC11/parsimonious/plot_parsimonious.py control/__init__.py demos/TAC11/progressive/VIS_progressive_penalty.py models/nslip.py demos/TAC11/viability/viability_plot.py demos/TAC11/wonky/VIS_visual.py demos/TAC11/gamma/plot_gamma.py demos/TAC11/wonky/wacky_plot.py demos/damping_study/paper_plots.py models/ardyn.py demos/TAC11/parsimonious/VIS_basic.py models/acrobot.py models/hovership.py demos/computeQ_daslip.py demos/computeQ_hovership.py demos/TAC11/neg_proxy_clean/VIS_negproxy.py demos/measure_learning/slip_optimistic.py demos/measure_learning/hovership_unviable_start.py models/daslip.py demos/measure_learning/hovership_default.py demos/nslip_demo.py demos/damping_study/compute_measure_damping.py demos/TAC11/gamma/DISC_binsearch.py models/parslip.py plotting/value_plotters.py plotting/single_trials.py demos/measure_learning/slip_default.py demos/TAC11/pos_proxy/plot_split_posproxy.py setup.py demos/measure_learning/slip_prior.py demos/daslip_demo.py viability/__init__.py demos/measure_learning/hovership_4d.py demos/measure_learning/slip_cautious.py demos/slip_demo.py Q_value_iteration get_step_trajectories compute_viability get_step_trajectories round_up_to_odd run_demo run_demo run_demo run_demo run_demo run_demo run_demo proxy_reward_position penalty L2diverge proxy_penalty_speed actuator_cost proxy_reward_position penalty target proxy_penalty_speed actuator_cost penalty target parsimonious_reward proxy_penalty_speed actuator_cost proxy_reward_position penalty L2wacky actuator_cost wacky smooth_l2 independent_l2 proxy_reward_position penalty L2diverge L2wacky actuator_cost wacky smooth_l2 independent_l2 penalty proxy_reward_position penalty target L2wacky proxy_penalty_speed actuator_cost MeasureLearner linear_interpolation MeasureEstimation wind check_failure p_map xp2s gravitational mass_matrix coriolis gravity sa2xp p_map xp2s sa2xp check_failure compute_leg_length check_failure create_force_trajectory poincare_map get_slip_trajectory sa2xp_y_xdot_aoa compute_leg_force compute_potential_kinetic_work_total create_open_loop_trajectories reset_leg compute_total_energy sa2xp_y_xdot_timedaoa compute_leg_forces map2s_y_xdot_aoa map2s_energy_normalizedheight_aoa feasible compute_spring_length mapSA2xp_energy_normalizedheight_aoa sa2xp_amp step xp2s_y_xdot p_map xp2s sa2xp check_failure check_failure p_map compute_total_energy map2x map2s reset_leg feasible step sa2xp compute_damping_coefficient compute_leg_length check_failure create_force_trajectory poincare_map sa2xp_y_xdot_aoa create_open_loop_trajectories reset_leg compute_total_energy sa2xp_y_xdot_timedaoa compute_spring_velocity map2s_y_xdot_aoa map2s_energy_normalizedheight_aoa feasible compute_spring_length mapSA2xp_energy_normalizedheight_aoa sa2xp_amp step xp2s_y_xdot p_map xp2s sa2xp check_failure compute_spring_length check_failure p_map compute_total_energy xp2s s2x find_limit_cycle reset_leg feasible step sa2xp p_map xp2s sa2xp check_failure p_map xp2s sa2xp check_failure wind check_failure p_map xp2s gravity sa2xp animation_visualisation create_plot_callback frame_image plot_Q_S create_set_colormap waterfall_plot interp_measure plot_ground_perturbations get_perturbation_indices compute_measure_postep add_title get_max_measure poincare_plot com_visualisation full_visualisation get_vmap set_size value_function reward_function shiftedColorMap visualise_old visualise visualise_no_dict is_outside_2D get_grid_indices map_S2Q compute_QV compute_Q_2D get_feasibility_mask project_Q2S_2D is_outside parcompute_Q_map compute_QV_2D bin2grid get_state_from_ravel digitize_s compute_Q_map project_Q2S parcompute_Q_mapC zeros_like print tuple ndenumerate flatten project_Q2S get_grid_indices unravel_index neighbor_option abs range len list linspace append reset_leg step poincare_map pi linspace project_Q2S open str imshow title savefig compute_QV sum dump format sa2xp_y_xdot_timedaoa map_S2Q size close copy parcompute_Q_map print figure xp2s_y_xdot makedirs load str seed atleast_2d T print close shape MeasureLearner run unravel_index randint argmax array init_estimation open create_plot_callback reshape choice meshgrid ravel hypot hypot allclose hypot hypot hypot check_failure gravitational mass_matrix coriolis array cos atleast_1d min max atleast_2d check_failure print shape zeros step enumerate solve_ivp reset_leg t_events concatenate less_equal isclose compute_spring_length t zeros range len reset_leg compute_total_energy step concatenate compute_total_energy concatenate print create_force_trajectory copy find_limit_cycle reset_leg step max cos sin compute_spring_length zeros range compute_spring_length print copy sqrt feasible copy sqrt feasible copy sqrt reset_leg compute_spring_length compute_leg_force interp solve_ivp min copy print shape zeros step enumerate cos sqrt sqrt hypot arccos print sqrt reset_leg map2x compute_damping_coefficient arctan2 compute_damping_coefficient compute_spring_velocity zeros size range arange eps compute_total_energy print p_map pi sqrt linspace reset_leg feasible abs range print sqrt reset_leg y arange subplots interp1d axhline save FuncAnimation t_events set_aspect show x_interpolate set_xlabel scatter Writer y_interpolate plot set_xlim foot_y_interpolate foot_x_interpolate t set_ylabel array set_ylim len array zeros fill_betweenx tuple add_subplot GridSpec logical_not set_visible tick_params set_xlabel scatter contourf add_gridspec meshgrid update plot set_xlim contour set_ylabel figure set_ylim str title round get_grid_indices isclose enumerate down_cmap zeros_like abs str interp_measure argmin ndenumerate scatter reset_leg digitize_s range up_cmap cmap plot copy get_cmap norm print add_title isclose xp2s_y_xdot len T concatenate reshape min colorbar set_linewidth shape Normalize add_collection3d tick_params max range LineCollection set_array max down_cmap up_cmap interp_measure copy get_perturbation_indices imshow scatter add_title digitize_s get_cmap abs isclose range xp2s_y_xdot len t_events y plot print xlabel ylabel t scatter axhline argmax len t_events subplot y plot print xlabel axvline ylabel t title scatter figure legend append xticks argmax range set_size_inches right left bottom gca float top cmap register_cmap hstack LinearSegmentedColormap linspace zip append astype imshow CenteredNorm get_cmap contour astype set_edgecolor imshow contourf collections CenteredNorm get_cmap contour from_list int vstack show T subplots set_title plot set_xlabel min cos colorbar imshow set_ylabel set_xticks nan zeros fill_between abs max show T subplots set_title plot set_xlabel min cos colorbar imshow set_ylabel set_xticks nan zeros fill_between abs max subplot T plot xlabel min cos ylabel colorbar imshow xticks title nan figure zeros fill_between abs max product p print p_map xp2s zeros enumerate sa2xp len zeros enumerate is_outside_2D nditer project_Q2S_2D copy array_equal zeros enumerate size len atleast_1d zeros enumerate digitize argmin atleast_1d zeros abs enumerate len tuple xp2s str list p map append digitize_s prod sa2xp product p_map size copy atleast_1d enumerate print reshape zeros array tuple ndim any range len zeros_like astype ndenumerate copy is_outside shape project_Q2S zeros list tuple size map get_grid_indices unravel_index digitize_s list product enumerate list zeros_like astype ndenumerate shape get_grid_indices unravel_index zeros list product size map array zeros feasible prod sa2xp enumerate starmap tuple xp2s Pool str list map append digitize_s prod product size close copy atleast_1d enumerate print reshape zeros array starmap tuple xp2s Pool str list map append digitize_s prod product size close copy atleast_1d enumerate print reshape zeros array | sheim/vibly | 3,620 |
shenwzh3/RGAT-ABSA | ['sentiment analysis', 'aspect based sentiment analysis', 'graph attention'] | ['Relational Graph Attention Network for Aspect-based Sentiment Analysis'] | model_utils.py run.py model_gcn.py trainer.py data_preprocess_twitter.py tree.py datasets.py data_preprocess_semeval.py model.py read_sentence_depparsed load_and_cache_vocabs build_dep_tag_vocab my_collate_bert _default_unk_index load_datasets_and_vocabs my_collate_pure_bert get_dataset get_rolled_and_unrolled_data ASBA_Depparsed_Dataset my_collate my_collate_elmo build_pos_tag_vocab load_glove_embedding reshape_dependency_tree_new build_text_vocab reshape_dependency_tree xml2txt dependencies2format get_dependencies text2docs main parse_args syntaxInfo2json dependencies2format get_dependencies text2docs read_file main parse_args syntaxInfo2json get_sentence Aspect_Text_GAT_only Aspect_Bert_GAT Aspect_Text_GAT_ours rnn_zero_state Pure_Bert Rel_GAT mask_logits GCN GAT DepparseMultiHeadAttention RelationAttention Highway mask_logits LinearAttention DotprodAttention main parse_args set_seed check_args simple_accuracy get_input_from_batch evaluate_badcase set_seed evaluate get_collate_fn acc_and_f1 compute_metrics get_bert_optimizer train tree_to_dist tree_to_rel_adj head_rel_to_tree tree_to_adj Tree inputs_to_deprel_adj head_to_tree inputs_to_tree_reps load_and_cache_vocabs dataset_name asarray get_dataset get_rolled_and_unrolled_data from_numpy ASBA_Depparsed_Dataset info len read_sentence_depparsed list len info deepcopy str append range enumerate append range enumerate word_tokenize defaultdict print len index lower info append reshape_dependency_tree_new range enumerate split build_dep_tag_vocab join dataset_name format embedding_dim glove_dir embedding_type output_dir info build_text_vocab build_pos_tag_vocab load_glove_embedding exists makedirs join fromstring strip uniform append zeros getline update items sorted list defaultdict sort Counter append max update items sorted list defaultdict sort Counter append max update items sorted list defaultdict sort Counter append max list pad_sequence sort zip tensor list pad_sequence sort batch_to_ids zip tensor list pad_sequence sort zip tensor list pad_sequence sort zip tensor add_argument ArgumentParser print replace list print tqdm append range predict len append enumerate text2docs print TreebankWordTokenizer replace xml2txt join replace print get_dependencies from_path data_path model_path parse_args syntaxInfo2json append print get_sentence replace len index dict split append enumerate read_file Variable zeros seed manual_seed_all manual_seed vars info from_pretrained load_datasets_and_vocabs device max check_args str basicConfig sorted set_seed Aspect_Bert_GAT gat_bert to Pure_Bert cuda_id bert_model_dir Aspect_Text_GAT_only Aspect_Text_GAT_ours info keys pure_bert gat_our train len pure_bert embedding_type pure_bert embedding_type AdamW gradient_accumulation_steps get_collate_fn model tuple clip_grad_norm_ zero_grad DataLoader max_grad_norm get_input_from_batch list set_seed Adam logging_steps append cross_entropy SummaryWriter format close num_train_epochs info trange per_gpu_train_batch_size max_steps enumerate int items evaluate backward add_scalar RandomSampler parameters get_bert_optimizer step len argmax eval_batch_size update join get_collate_fn tuple per_gpu_eval_batch_size DataLoader eval compute_metrics output_dir info append SequentialSampler numpy len int join decode get_collate_fn tuple eval DataLoader append SequentialSampler numpy simple_accuracy f1_score int intersection_update add_child tolist len reversed add difference set append range enumerate zeros T children ones dist from_numpy numpy max concatenate from_numpy numpy max concatenate add_child tolist range len zeros T children rel | shenwzh3/RGAT-ABSA | 3,621 |
shifaspv/SE-FFTNet-tensorflow-implemenatation | ['speech enhancement'] | ['SEGAN: Speech Enhancement Generative Adversarial Network', 'A non-causal FFTNet architecture for speech enhancement'] | src/lib/audio_conditions_io.py src/model1.py src/lib/__init__.py src/lib/model_io.py src/generate.py src/train.py src/lib/ops.py src/lib/util.py data/generate_wave_id_list.py src/lib/precision.py src/lib/optimizers.py get_getter FFTNEt get_var_maybe_avg get_weight_variable get_bias_variable read_filelist AudioConditionsReader wav_to_float get_subsequence_with_speech_indices load_noisy_audio_label_and_speaker_id extract_subsequence_with_speech align_audio_samples_to_label_samples load_clean_noisy_audio_and_label get_info read_model_id get_model_id restore_variables write_model_id setup_logger get_configuration save_variables int_shape concat_relu conv get_learning_rate get_optimizer load_wav compute_receptive_field_length one_hot_encode linear_to_ulaw get_subdict_from_dict l1_l2_loss float_to_uint8 write_wav uint8_to_float binary_encode tf_uint8_to_float get_condition_input_encode_func get_subsequence_with_speech_indices normalize pretty_json_dump compute_feature_dim ensure_keys_in_dict preemphasis ulaw_to_linear read_wav rms tf_float_to_uint8 tf_linear_to_ulaw ensure_sample_rate tf_ulaw_to_linear one_hot_decode wav_to_float dir_contains_files snr_db average get_variable as_numpy_dtype astype int min mean append abs max range len get_subsequence_with_speech_indices rstrip readlines close append open join load_wav join load_wav rstrip len join load_wav rstrip join config add_argument model_id output_filename noisy_speech_filename ArgumentParser parse_args setFormatter getLogger addHandler Formatter setLevel FileHandler close write_model_id exists str close join read_model_id write_model_id join makedirs save str join str restore global_variables get_checkpoint_state print model_checkpoint_path tables_initializer Saver run readlines close open convolution lower lower sum asarray flip log max astype abs log sign abs log sign min max astype float abs sign abs sign array isinstance array isinstance astype all print dump dumps open read wav_to_float as_numpy_dtype astype read_wav ensure_sample_rate array abs max listdir | ## SE-FFTNet: A non-causal FFTNet architecture for speech enhancement SE-FFTNet <a href="https://www.isca-speech.org/archive/Interspeech_2019/pdfs/2622.pdf"> [1] </a> is an end-to-end speech enhancement model that, in contrast to the SE-WaveNet <a href="https://arxiv.org/abs/1706.07162"> [2] </a> SEGAN <a href="https://arxiv.org/abs/1703.09452"> [3] </a> models, employs a different dilation pattern to extract features, which has been shown important to model the statistical dissimilarity between the speech and noise effectively. The model uses wider dilation at the begining layers which then decreases towards the depth. Through this new dilation pattern, the model has achieved a far better enhancement compared to the SE-WaveNet wile having very lesser number of parameters. Few samples from the trained model are displayed <a href="https://www.csd.uoc.gr/~shifaspv/IS2019-demo">here</a>. ## Implemented On Python - 3.6.8 <br> Tensorflow - 1.14.0 <br> We required few more very common Python packages, check the ```required.txt``` file and install if you don't have. ## Data set | 3,622 |
shifaspv/gruCNN-speech-enhancement-tensorflow | ['speech enhancement'] | ['A fully recurrent feature extraction for single channel speech enhancement'] | src/lib/audio_conditions_io.py src/model3.py src/lib/__init__.py src/lib/model_io.py src/generate.py src/train.py src/lib/ops.py src/lib/util.py data/generate_wave_id_list.py src/lib/precision.py src/lib/optimizers.py get_getter get_var_maybe_avg get_weight_variable gruCNN_SE get_bias_variable read_filelist feature_norm AudioConditionsReader wav_to_float get_subsequence_with_speech_indices load_noisy_audio_label_and_speaker_id extract_subsequence_with_speech align_audio_samples_to_label_samples load_clean_noisy_audio_and_label get_info read_model_id get_model_id restore_variables write_model_id setup_logger get_configuration save_variables conv2D concat_relu conv int_shape get_learning_rate get_optimizer load_wav compute_receptive_field_length one_hot_encode linear_to_ulaw get_subdict_from_dict l1_l2_loss float_to_uint8 write_wav uint8_to_float binary_encode tf_uint8_to_float get_condition_input_encode_func get_subsequence_with_speech_indices normalize pretty_json_dump compute_feature_dim ensure_keys_in_dict preemphasis ulaw_to_linear read_wav rms tf_float_to_uint8 tf_linear_to_ulaw ensure_sample_rate tf_ulaw_to_linear one_hot_decode wav_to_float dir_contains_files snr_db average get_variable as_numpy_dtype astype int min mean append abs max range len get_subsequence_with_speech_indices rstrip readlines close append open join load_wav join load_wav rstrip len join load_wav rstrip join config add_argument model_id output_filename noisy_speech_filename ArgumentParser parse_args setFormatter getLogger addHandler Formatter setLevel FileHandler close write_model_id exists str close join read_model_id write_model_id join makedirs save str join str restore global_variables get_checkpoint_state print model_checkpoint_path tables_initializer Saver run readlines close open convolution convolution lower lower sum asarray flip log max astype abs log sign abs log sign min max astype float abs sign abs sign array isinstance array isinstance astype all print dump dumps open read wav_to_float as_numpy_dtype astype read_wav ensure_sample_rate array abs max listdir | # gruCNN-SE: A fully recurrent feature extraction for speech enhancement This is a Tensorflow implementation of the ```gruCNN-SE``` architecture suggested in <a href="https://arxiv.org/abs/2006.05233"> this paper</a>, where we build a recurrent feature extraction approach to model speech recurrency at the feature extraction stage itself. This is advantageous as the model can capture the locall recurrency pattern of the speech, contrasting to the traditional recurrency modelling <a href="https://www.microsoft.com/en-us/research/uploads/prod/2018/02/ZhaoZararTashevLee_ICASSP_2018.pdf">[1]</a>, <a href="https://web.cse.ohio-state.edu/~wang.77/papers/Tan-Wang1.interspeech18.pdf">[2]</a> , where the recurrency being modelled independently from the front-end feature extraction. This is an inital attempt towards building higher models motivated from this startegy. Few samples from the trained model are displayed <a href="https://www.csd.uoc.gr/~shifaspv/IEEE_Letter-demo">here</a>, along with the other models. ## Brief description of the model architecture  <br> As can be seen in the figure, the ```gruCNN-SE``` is a feature domain enhancement model that takes noisy spectra as the input and extracts features recurrenctly over time. To better understand the exact layer wise details please refer to our paper <a href="https://arxiv.org/pdf/2006.05233.pdf">at here</a> . ## Implemented On Python - 3.6.8 <br> | 3,623 |
shimo-lab/kadingir | ['information retrieval', 'word embeddings'] | ['Cross-Lingual Word Representations via Spectral Graph Embeddings'] | tools/concat_europarl_corpora.py | kadingir ===================================================== This is an open source implementation of >Oshikiri, T., Fukui, K., Shimodaira, H. (2016). Cross-Lingual Word Representations via Spectral Graph Embeddings. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. (To appear) ## Contents * `src/` : Source codes (C++ & Rcpp version) * `cpp/` : Source codes (C++ only version) * `experiments/` : Code used in the experiments * `tools/` ## Implemented methods | 3,624 |
shirgur/ACDRNet | ['semantic segmentation'] | ['End to End Trainable Active Contours via Differentiable Rendering'] | train.py data/transforms.py data/generate_cityscapes_instances.py utils/summaries.py models/loss_functions.py utils/topology.py backbones/resnet.py data/make_hdf5.py models/networks.py utils/metrices.py data/cityscapes_instances.py backbones/unet.py data/buildings.py utils/saver.py train_epoch val_epoch conv1x1 resnext50_32x4d ResNet resnet50 Decoder resnext101_32x8d Bottleneck resnet152 conv3x3 _resnet resnet34 resnet18 BasicBlock resnet101 Decoder Encoder DownBlock EmbeddingBlock UpBlock BuildingsDataset CityscapesInstances CityscapesInstances_comp ToPILImage CenterCrop RandomRotation ToTensor RandomApply RandomCrop RandomChoice RandomAffine RandomTransforms Lambda_image TenCrop RandomRotationFromSet Resize RandomResizedCrop RandomHorizontalFlip FiveCrop Pad RandomPerspective NormalizeInstance Lambda Compose RandomVerticalFlip Normalize RandomOrder RandomAffineFromSet ColorJitter curvature_loss dist_loss CircleNet db_eval_boundary get_ap_scores get_f1_scores WCov_metric seg2bmap get_iou FBound_metric Saver TensorboardSummary get_circle model zero_grad set_description unsqueeze get_iou checkname exp iter append to sum lmd_balloon format byte get_ap_scores mean lmd_curve item enumerate visualize_image backward add_scalar tqdm gt lmd_dist train step len model set_description interpolate get_iou checkname exp squeeze len tolist iter to eval_rate format byte get_ap_scores mean eval item enumerate visualize_image tqdm gt mse_loss add_scalar ResNet load_state_dict load_state_dict_from_url roll roll append reshape f1_score zip append reshape zip average_precision_score squeeze sum count_nonzero float disk seg2bmap float sum binary_dilation bool zeros_like astype floor zeros float range copy repeat int32 type array | # ACDRNet Official PyTorch implementation of "End to End Trainable Active Contours via Differentiable Rendering" ([link](https://openreview.net/pdf?id=rkxawlHKDr)) ## Prerequisites - python 3.6 - pytorch 1.1 - torchvision - neural_renderer - numpy - scipy - scikit-learn | 3,625 |
shirgur/UMIS | ['semantic segmentation'] | ['Unsupervised Microvascular Image Segmentation Using an Active Contours Mimicking Neural Network'] | utils/summaries.py train_sup.py utils/__init__.py networks/loss_functions.py src/setup.py networks/resnet.py networks/segmentation.py morphpoollayer.py train_unsup.py utils/saver.py data/datasets.py utils/metrices.py networks/utils.py MorphPoolFunction MorphSkel3D MorphPool3D range_normalize smart_padding Single_Image_Eval Directory_Image_Train level_set euler_lagrange conv3x3x3 get_fine_tuning_parameters ResNet downsample_basic_block resnet50 Bottleneck resnet152 resnet34 resnet200 resnet18 resnet10 BasicBlock resnet101 VessNN SegmentNet3D_Resnet DeepVess norm_ip GradXYZ norm_range batch_jaccard_index_and_dice_coefficient batch_intersection_union batch_precision_recall batch_pix_accuracy otsu_threshold gaussian_threshold f1_score batch_sens_spec Saver TensorboardSummary mean std int pad floor shape list lmd2 mean shape pow mse_loss sum lmd1 range len list mean pow shape range len data isinstance FloatTensor Variable zero_ avg_pool3d cuda cat append format range named_parameters ResNet ResNet ResNet ResNet ResNet ResNet ResNet clamp zeros gaussian_filter shape cumsum astype ravel histogram argmax sum spacing numpy float64 dtype astype mean histogram numpy nan_to_num sum float numpy nan_to_num sum float numpy nan_to_num sum float numpy nan_to_num batch_precision_recall | shirgur/UMIS | 3,626 |
shirgur/UnsupervisedDepthFromFocus | ['depth estimation', 'monocular depth estimation'] | ['Single Image Depth Estimation Trained via Depth from Defocus Cues'] | Utils/Datasets.py train.py Models/resnet.py Utils/GaussPSFLayer.py Models/__init__.py ext/setup.py Utils/Losses.py Utils/custom_transforms.py Utils/__init__.py Dense_ASPP_SA ResNet SelfAttention Bottleneck ResNet50 ASPP_module SwapChannels RandomScaleCrop RandomHue ConvertFromInts RandomSaturation Compose RandomMirror ConvertColor RandomContrast ToTensor ClipRGB Augmentation RandomBrightness SubtractMeans RandomLightingNoise PhotometricDistort Make3DDataset KITTIDataset NYUDataset GaussPSFFunction GaussPSF create_window_avg create_window Rec_Loss gaussian _ssim SSIM AVERAGE ResNet Tensor contiguous unsqueeze contiguous unsqueeze pow conv2d | # Single Image Depth Estimation Trained via Depth from Defocus Cues Official implementation of "Single Image Depth Estimation Trained via Depth from Defocus Cues" ([arxiv](https://arxiv.org/)). The implementation is based on the architectures of [DeepLabV3+](https://github.com/jfzhang95/pytorch-deeplab-xception) and [Self-Attention](https://github.com/heykeetae/Self-Attention-GAN). ## Prerequisites - Python 3.6 - Pytorch 0.4 - Numpy - Scipy - OpenCV - Path | 3,627 |
shivaat/VMWE-Identification | ['word embeddings'] | ['SHOMA at Parseme Shared Task on Automatic Identification of VMWEs: Neural Multiword Expression Tagging with High Generalisation'] | bin/bmc_munkres/munkres.py MTL/evaluation.py bin/average_of_evaluations.py SHOMA/evaluation.py MTL/models/tag_models.py SHOMA/script.py MTL/corpus.py bin/validate_cupt.py bin/tsvlib.py SHOMA/layers.py SHOMA/preprocess.py MTL/corpus_reader.py MTL/train_test.py MTL/models/layers.py MTL/preprocessing.py SHOMA/test_run.py bin/parsemetsv2cupt.py MTL/main_multi.py bin/evaluate.py Block parse_statline Main parse_blocks uniq error tokbased_pairing pairing2t MatchCounter Statistics mwe2t Main ParsemeBipartiteGraph mwes2t SeenInfo Main excepthook iter_tsv_sentences warn interpret_color_request mwe_code_to_id_categ global_last_lineno FrozenCounter TSVToken MWEInfo TSVSentence Main Munkres make_cost_matrix print_matrix Token VMWE Corpus Sentence Corpus_reader labels2Parsemetsv run_model Data Train_Test GraphLayer GraphPool Highway GraphAveragePool GraphConv GraphAttention SpectralGraphConvolution GraphMaxPool roll Alpha_Weights Tagger labels2Parsemetsv _backward ChainCRF chain_crf_loss sparse_chain_crf_loss free_energy free_energy0 create_custom_objects viterbi_decode path_energy _forward batch_gather path_energy0 add_boundary_energy wordShape Script split format exit groups add set exit warn sum permutations int split format name strip exit global_last_lineno startswith append TSVToken next TSVSentence enumerate split append print enumerate split print format staticmethod append str print write max len Data print Tagger test load_data Train_Test cross_validation train Constant range _context _context_handle _post_execution_callbacks inputs _apply_op_helper device_name record_gradient TFE_Py_FastPathExecute add_boundary_energy one_hot reshape floatx cast gather sum add_boundary_energy path_energy0 argmax cast concatenate greater floatx cast expand_dims _backward add_boundary_energy zeros_like _forward add_boundary_energy _forward concatenate floatx cast expand_dims rnn flatten shape arange reverse cast rnn zeros isdigit any isupper | shivaat/VMWE-Identification | 3,628 |
shivaverma/Score-Time-Detection | ['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'] | main.py app.py upload_file hello_world resizeNormalize create_result predict BidirectionalLSTM strLabelConverter load_image crop CRNN join save load resizeNormalize max decode data view model Variable print convert transformer eval load_state_dict strLabelConverter IntTensor is_available cuda CRNN print shape IMREAD_GRAYSCALE resize imread print shape imwrite load_image crop predict | shivaverma/Score-Time-Detection | 3,629 |
shiyinzhang/Inside-Outside-Guidance | ['instance segmentation', 'interactive segmentation', 'semantic segmentation'] | ['Interactive Object Segmentation With Inside-Outside Guidance'] | networks/sync_batchnorm/replicate.py dataloaders/custom_transforms.py dataloaders/helpers.py networks/refinementnetwork.py dataloaders/combine_dbs.py train_refine.py evaluation/evaluation.py train.py networks/loss.py networks/FineNet.py mypath.py test_refine.py evaluation/eval.py networks/sync_batchnorm/unittest.py networks/sync_batchnorm/batchnorm.py networks/CoarseNet.py test.py dataloaders/pascal.py networks/sync_batchnorm/comm.py dataloaders/sbd.py eval.py networks/mainnetwork.py networks/backbone/__init__.py networks/backbone/resnet.py networks/sync_batchnorm/__init__.py Path CombineDBs ScaleNRotate FixedResize CropFromMask ToTensor ConcatInputs IOGPoints ToImage RandomHorizontalFlip overlay_masks get_bbox crop2fullmask fixed_resize generate_param_report crop_from_mask iog_points tens2image cstm_normalize make_gt crop_from_bbox make_gaussian overlay_mask getPositon VOCSegmentation SBDSegmentation jaccard CoarseNet FineNet Bottleneck class_cross_entropy_loss PSPModule SegmentationNetwork Network iou_cal PSPModule generate_distance_map SegmentationNetwork make_gaussian Network getPositon ResNet50 ResNet ResNet101 Bottleneck build_backbone _sum_ft SynchronizedBatchNorm2d _unsqueeze_ft _SynchronizedBatchNorm SynchronizedBatchNorm1d SynchronizedBatchNorm3d SyncMaster FutureResult SlavePipe execute_replication_callbacks CallbackContext DataParallelWithCallback patch_replication_callback TorchTestCase as_numpy squeeze numpy dtype list resize tuple astype map shape zip zeros load join drawContours ones astype float32 ndim copy shape logical_or dirname append zeros bool range load join uint8 drawContours ndarray isinstance ones astype float32 copy shape dirname zeros bool mat divmod argmax shape getPositon distance_transform_edt where shape zeros find_point inf transpose min where flip max list tuple squeeze map tile zip zeros int all isinstance tuple min range INTER_CUBIC resize append INTER_NEAREST float round max zeros get_bbox crop_from_bbox resize arange astype maximum zeros make_gaussian array range min max str list items write close open bool astype zeros_like mul exp float sum log load data list items update models_dir from_numpy load_state_dict SegmentationNetwork zeros state_dict print transpose distance_transform_edt logical_and maximum astype from_numpy zeros sum make_gaussian getPositon int exp transpose squeeze logical_and tens2image shape logical_or generate_distance_map cpu zeros sum cuda range bitwise_xor ResNet ResNet list hasattr __data_parallel_replicate__ modules enumerate len replicate data isinstance | shiyinzhang/Inside-Outside-Guidance | 3,630 |
shizhouxing/DialogueDiscourseParsing | ['discourse parsing', 'link prediction'] | ['A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues'] | baseline/ilp.py libs/rnn_cell_impl.py NonStructured_Encoder.py Model.py libs/__init__.py Agent.py libs/dropout.py baseline/main.py data_pre.py Structured_Encoder.py baseline/utils.py utils.py main.py baseline/Sentence_Encoder.py Agent process_dialogue process_file get_summary_sum Model NonStructured_Encoder Structured_Encoder get_batches init_grad update_buffer load_data preview_data build_vocab pretty_data load_scip_output mk_zimpl_input dump_scores_to_dat_files get_batches padding get_batched_data_test test_mst test_ilp Sentence_Encoder load_data build_vocab dropout _like_rnncell _enumerated_map_structure DropoutWrapper int readline remove print open range append len sorted print parse_speaker append range enumerate len readline sorted print len close enumerate match loads sub open range append find sorted list print close map dim_embed_word word_vector open zeros array enumerate append split print zeros len enumerate sorted append bool range len join format len join list values append zeros enumerate len append range load_pairs len append int padding shuffle zeros max get_batched_data_test infer append zeros range enumerate len join scip_path get_batched_data_test num_relations infer system enumerate load_scip_output dump_scores_to_dat_files append zeros range mk_zimpl_input len | # Dialogue Discourse Parsing Code for our paper: Zhouxing Shi and Minlie Huang. A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues. In AAAI, 2019. ``` @inproceedings{shi2019deep, title={A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues}, author={Shi, Zhouxing and Huang, Minlie}, booktitle={AAAI}, year={2019} } | 3,631 |
shizuo-kaji/StyleTransfer | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | train_gogh.py postprocess load_dicom gram_matrix Updater main load_image get transpose array clip shape float32 reshape batch_matmul ImplicitVRLittleEndian astype float32 center_crop rescale read_file RescaleIntercept array clip LANCZOS size new convert transpose resize array open CT_base LANCZOS CT_A_scale observe_lr LogReport Trainer SerialIterator ArgumentParser resize print_runtime_info out run fromarray Parameter use setup Adam available input load_image parse_args load_dicom asarray cupy astype ProgressBar PlotReport tile image_size PrintReport CT_B_scale init_image print style add_argument convert float32 extend VGG16Layers Updater CT_range to_gpu array makedirs | An implementation of 'A Neural Algorithm of Artistic Style' (http://arxiv.org/abs/1508.06576) ============= This is an implementation of 'A Neural Algorithm of Artistic Style' (http://arxiv.org/abs/1508.06576). The code is largely based on - https://github.com/pfnet-research/chainer-gogh - https://github.com/yusuketomoto/chainer-fast-neuralstyle The script generates a stylised image from an input image and a style image. ## Demo on Google Colaboratory You can try the demo on a browser [Google Colaboratory](https://colab.research.google.com/drive/1ioGT6LE4KKU4Ttx_e0-j1REOAiu32iLo) | 3,632 |
shlurbee/dmrs-text-generation-naacl2019 | ['text generation'] | ['Neural Text Generation from Rich Semantic Representations'] | preprocessing.py scripts/remove_overlap.py scripts/eval_sentences.py scripts/property_stats.py generate.py replacements.py replace_rare.py scripts/eval_compare.py postprocessing.py gw-to-mrs/gw_to_sentences.py read_profile get_test_profiles run_debug process run postprocess get_tgt_filename _adjust_span_boundaries get_src_filename preprocess_penman combine_attributes get_anon_filename load_vocab anonymize_graph create_parallel_files get_orig_filename replace_rare_tokens _normalize_sentence PenmanToLinearCodec preprocess_sentence build_vocab load_serialized_from_file build_replacement_map_most_common extract_sentences get_overlap_filename get_overlapping_lines compare_lines compare_files main _make_counters report print_parallel_file_instructions find_overlapping_lines apply_blacklist serialize format print write grammar generate flush loads_one join dict append ItsdbProfile load open remove format replace relation insert strip Triple index attributes append source join remove format sorted relation insert Triple index append variables decode combine_attributes anonymize_graph encode codec _adjust_span_boundaries sorted format replace endswith write sub _normalize_sentence tokenize enumerate sub len format write load_serialized_from_file most_common Counter get_tgt_filename get_src_filename format get_anon_filename write load_vocab copyfile items list write HTMLParser list get_overlap_filename readlines strip write close zip open compare_lines list zip items list format print float sum values loads_one items TestSuite list format print PROFILE var_sort _make_counters report variables print format set readlines set print | # dmrs-text-generation Generating text from DMRS (minimal recursion semantics) representation. (See https://www.aclweb.org/anthology/N19-1235) Quick Start: (1) Create and activate a virtual env, if desired ``` > virtualenv --python=python3 env > source env/bin/activate ``` (2) Clone this repo, then from the root dir run `sh setup.sh` to install dependencies and get data. ``` | 3,633 |
shmanubhav/LunarLander-ApproxQLearning | ['stochastic optimization'] | ['Adam: A Method for Stochastic Optimization'] | lunar_flyer.py q_learning.py heuristic LunarLander LunarLanderContinuous demo_heuristic_lander ContactDetector getPrediction abs array clip continuous seed format heuristic print render reset step zeros reshape predict concatenate | ## LunarLander Approximate Q-Learning #### What is this project? This is an attempt to solve the LunarLander-v2 ENV defined in OpenGym AI, using Deep Q-Learning. We create a double layered neural network with 8 observation vectors and 4 possible actions in each state. We use Approximate Q-Learning to create an optimal solution to allow the Lunar Satellite to land gently in the marked helipad to maximize rewards. We compare between two Keras optimizers: Stochastic Gradient Descent (https://github.com/keras-team/keras/blob/master/keras/optimizers.py#L164) and Adam Stochastic Optimization. (https://arxiv.org/abs/1412.6980v8) #### How to get started with this project? To install our python dependancies in this project we are using pipenv. | 3,634 |
shonxg/SPI_Optimizer | ['stochastic optimization'] | ['SPI-Optimizer: an integral-Separated PI Controller for Stochastic Optimization'] | experiment/experiment_code/optimizers/PIDACC.py utils/progress/progress/bar.py utils/logger.py 2D_function/arrow_pic/draw_lag_1picmom.py 2D_function/arrow_pic/draw_lag_TRIpicnag.py utils/progress/progress/helpers.py utils/progress/progress/__init__.py 2D_function/arrow_pic/draw_lag_GOLDpicm99 (another copy).py 2D_function/arrow_pic/draw_lag_ROSpicm99 (copy).py experiment/experiment_code/models/cliquenet.py experiment/experiment_code/models/cifar/preresnet.py 2D_function/arrow_pic/draw_lag_1picnag.py 2D_function/arrow_pic/draw_lag_GOLDpicnag (another copy).py experiment/experiment_code/models/cifar/vgg.py experiment/experiment_code/models/cifar/__init__.py utils/progress/progress/spinner.py utils/eval.py experiment/experiment_code/sgdsp99.py utils/misc.py experiment/experiment_code/models/cifar/alexnet.py utils/progress/setup.py experiment/experiment_pic/draw_alexnetc100_200.py experiment/experiment_code/train_momentsp99.py experiment/experiment_code/mnist_pid.py experiment/experiment_code/optimizers/PIDOPT.py experiment/experiment_pic/draw_resnet56_c10.py 2D_function/arrow_pic/draw_lag_1picsgd.py experiment/experiment_pic/draw_mnist.py experiment/experiment_code/mnist_gds.py experiment/experiment_code/mnist_sp99.py experiment/experiment_code/optimizers/neumann.py experiment/experiment_pic/draw_alexnetc100.py 2D_function/arrow_pic/draw_lag_ROSpicsgd (copy).py experiment/experiment_code/models/cifar/densenet.py utils/__init__.py experiment/experiment_pic/draw_wrn_c100.py experiment/experiment_code/gds.py experiment/experiment_code/train_nag.py 2D_function/arrow_pic/draw_lag_TRIpicm99.py utils/progress/test_progress.py experiment/experiment_code/models/cifar/wrn.py experiment/experiment_pic/draw_alexnetc10.py experiment/experiment_code/models/cifar/cliquenet.py experiment/experiment_code/models/cifar/resnext.py experiment/experiment_code/optimizers/pd.py 2D_function/arrow_pic/draw_lag_TRIpicmom.py utils/progress/progress/counter.py 2D_function/arrow_pic/draw_lag_ROSpicmom (copy).py 2D_function/arrow_pic/draw_lag_GOLDpicsgd (another copy).py utils/visualize.py experiment/experiment_code/train_gds.py 2D_function/loss_pic/draw_loss.py experiment/experiment_pic/draw_resnet56_c100.py 2D_function/arrow_pic/draw_lag_TRIpicsgd.py 2D_function/arrow_pic/draw_lag_ROSpicnag (copy).py experiment/experiment_code/mnist_nag.py experiment/experiment_code/models/cifar/resnet.py 2D_function/arrow_pic/draw_lag_GOLDpicmom (another copy).py experiment/experiment_code/sgd.py experiment/experiment_code/models/cifar/preresnet44.py experiment/experiment_code/train_moment.py experiment/experiment_code/models/cifar/utils.py experiment/experiment_code/optimizers/pid.py experiment/experiment_code/mnist_moment.py 2D_function/arrow_pic/draw_lag_1picm99.py g momentum fn lossfn m99 g nesterov sgd momentum fn lossfn m99 g nesterov momentum fn lossfn g nesterov sgd momentum fn lossfn m99 g momentum fn lossfn m99 g nesterov sgd momentum fn lossfn m99 g nesterov momentum fn lossfn g nesterov sgd momentum fn lossfn m99 g momentum fn lossfn m99 g nesterov sgd momentum fn lossfn m99 g nesterov momentum fn lossfn g nesterov sgd momentum fn lossfn m99 g momentum fn lossfn m99 g nesterov sgd momentum fn lossfn m99 g nesterov momentum fn lossfn g nesterov sgd momentum fn lossfn m99 newtons draw_segments_on_img g nesterov nm map_xy_to_img_coords sgd momentum fn lossfn NE generate_background_cost map_cost_to_color frcg m99 m99jf dampnm SGD Net Net SGD SGD cliquenet AlexNet alexnet cliquenet densenet Transition DenseNet Bottleneck BasicBlock preresnet PreResNet Bottleneck conv3x3 BasicBlock CifarPreResNet ResNetBasicblock preresnet44 ResNet Bottleneck conv3x3 resnet BasicBlock ResNeXtBottleneck resnext CifarResNeXt clique_block compress transition global_pool attention vgg19 VGG vgg16_bn vgg19_bn vgg11_bn make_layers vgg11 vgg13 vgg13_bn vgg16 wrn BasicBlock NetworkBlock WideResNet Neumann PIDOptimizer PIDOptimizer PIDACC PIDOPT main excel_table_byname open_excel main excel_table_byname open_excel main excel_table_byname open_excel main excel_table_byname open_excel main excel_table_byname open_excel main excel_table_byname open_excel main excel_table_byname open_excel accuracy plot_overlap savefig Logger LoggerMonitor init_params AverageMeter mkdir_p get_mean_and_std make_image show_mask_single show_mask gauss colorize show_batch sleep FillingSquaresBar FillingCirclesBar IncrementalBar ChargingBar ShadyBar PixelBar Bar Countdown Stack Counter Pie SigIntMixin WriteMixin WritelnMixin PieSpinner MoonSpinner Spinner PixelSpinner LineSpinner Progress Infinite norm str g zeros_like print copy lossfn append array range str g zeros_like print copy sign lossfn append range array clip str g zeros_like print copy lossfn append array range str g zeros_like print copy lossfn append array range log2 str g zeros_like print copy lossfn append array range str print copy dot gfun array append str hess print solve copy gfun array append str hess print solve copy gfun array append append array copy g copy fn append abs array min fn linspace meshgrid round max uint8 COLOR_HSV2BGR astype shape cvtColor line zip AlexNet CifarPreResNet CifarResNeXt 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 WideResNet open_workbook nrows open_excel sheet_by_name row_values append range show plot xlabel FormatStrFormatter ylabel MultipleLocator ylim title set_major_formatter legend xlim excel_table_byname axvline axhline col_values ncols topk size t eq mul_ expand_as append sum max asarray arange plot numbers enumerate len print DataLoader div_ zeros range len normal constant isinstance kaiming_normal Conv2d bias modules BatchNorm2d weight Linear makedirs numpy range zeros unsqueeze gauss show make_image imshow make_grid make_image subplot make_grid size clone axis upsampling imshow expand_as range make_image subplot make_grid size clone axis upsampling imshow expand_as cpu range len | # SPI-Optimizer: an integral-Separated PI Controller for Stochastic Optimization
Paper: https://arxiv.org/abs/1812.11305v1
### In this program,
## 1.Environment:
| 3,635 |
shouvikmani/edge-colorizer | ['style transfer'] | ['Artistic Enhancement and Style Transfer of Image Edges using Directional Pseudo-coloring'] | image-quality-assessment/src/handlers/data_generator.py image-quality-assessment/src/tests/test_data_generator.py image-quality-assessment/src/tests/test_augmentation_utils.py image-quality-assessment/src/handlers/model_builder.py image-quality-assessment/src/utils/losses.py image-quality-assessment/src/utils/keras_utils.py image-quality-assessment/data/TID2013/get_labels.py image-quality-assessment/contrib/tf_serving/tfs_sample_client.py image-quality-assessment/src/handlers/config_loader.py image-quality-assessment/mkdocs/autogen.py image-quality-assessment/src/utils/utils.py image-quality-assessment/contrib/tf_serving/save_tfs_model.py image-quality-assessment/src/evaluater/predict.py image-quality-assessment/src/handlers/samples_loader.py image-quality-assessment/src/trainer/train.py main normalize_labels calc_mean_score get_image_quality_predictions get_dataframe get_max_entropy_distribution get_features main parse_raw_data get_func_comments get_comments_str to_md delete_space extract_comments md_parse_line_break remove_next_line parse_func_string change_args_to_dict parse_func_args skip_space_line main predict image_dir_to_json image_file_to_json load_config TrainDataGenerator TestDataGenerator Nima load_samples TestUtils TestTrainDataGenerator train TensorBoardBatch earth_movers_distance random_crop normalize_labels save_json random_horizontal_flip calc_mean_score load_json load_image ensure_dir_exists Nima set_learning_phase print build load_weights array normalize_labels make_tensor_proto preprocess_input CopyFrom Predict print PredictRequest dumps insecure_channel calc_mean_score load_image expand_dims round PredictionServiceStub float_val arange MinDivergenceModel get_features array fit read_csv append iterrows get_max_entropy_distribution get_dataframe format save_json parse_raw_data append join strip split join split match delete_space strip match startswith split remove_next_line change_args_to_dict skip_space_line replace items list isinstance kwarg parse_func_string to_md get_docstring parse to_md get_docstring parse_func_string get_func_comments join get_comments_str replace makedirs write close walk open dirname glob join append image_file_to_json TestDataGenerator dumps nima_model calc_mean_score isfile preprocessing_function image_dir_to_json predict enumerate load_json Nima TensorBoardBatch join preprocessing_function layers clear_session TestDataGenerator fit_generator build TrainDataGenerator load_weights lower summary ModelCheckpoint train_test_split compile cumsum sqrt mean square randint swapaxes makedirs | # edge-colorizer Edge Colorizer is a program that detects edges in an image and colors them in a deliberate and coherent manner. It uses an image processing pipeline that consists of a series of convolutions and other matrix operations to blur an image, detect edges, and color the edges pixel-by-pixel. The final result is an image that resembles a skeleton of the original image, colored with bright, neon colors and contrasted against a black background. Results: <img src="results/0.jpg" width="400"> <img src="results/5.jpg" width="400"> <img src="results/4.jpg" width="400"> | 3,636 |
shrebox/Personified-Chatbot-I-am-Kalam | ['response generation'] | ['A Persona-Based Neural Conversation Model'] | code/preprocess/S_ques_ans_scrapper.py code/preprocess/S_goodreadsscrapper.py code/evaluation/sent2vec_my.py code/preprocess/shrebox/shrebox_abdulkalam_childrenques_scraper.py code/seq2seq/data/__init__.py dataset/quotes/check_json.py code/preprocess/S_extract_answers.py code/preprocess/S_tag.py code/evaluation/pipeline.py code/preprocess/shrebox/quoteExtractor.py code/seq2seq/data/kalam/data.py code/IR-IE/sent2vec_my.py code/seq2seq/main.py cleantext rogue2_bleu sentence_similarity sentence_similarity cleantext get_quote get_qna jaccard sentence_similarilty_wmd rogue2_bleu author_url quotes_from_url book_url get_ques_ans cleantext tag_quotes tag_qna author_url quotes_from_url book_url initial_setup train create_model check_rate decode split_dataset index_ load_data filter_unkown filter_dataown zero_padown word_tokenize bigrams set sub compile items list sorted load_model summarize Sent2vecModel cosine item str eval input set lower word_tokenize split join encode rstrip text BeautifulSoup find_all append urljoin text print search_books BeautifulSoup find_all append str urljoin text find_author BeautifulSoup find_all append range replace text strip BeautifulSoup find_all append outputs reset_default_graph Session run all_params split_dataset remove_pad_sequences pad_sequences tolist placeholder sequences_add_end_id sequences_get_mask append input inference range update create_model close shuffle load_and_assign_npz eval softmax check_rate minimize sequences_add_start_id print cross_entropy_seq_with_mask tqdm load_data minibatches global_variables_initializer save_npz len range check_rate split_dataset remove_pad_sequences tolist load_data load append len append range len FreqDist chain most_common dict append zip len append zeros array range len | [](https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.13140%2FRG.2.2.28964.09602?_sg%5B0%5D=ApGORUVG1cBpNrnt9rvmz-ph0V9Q1S-B0MNYPCFbHB_CIqf9M4-2aqvNXMKavH-5plON5qiVr3nw4ots-C1J88exnw.ieGbmNeyS6_ywMiraavaTI9s-uUHq6x6S6AXlePwTKqf6VCcbbdeh5nirtz6xeJVCu7udFxrw7bJ-b3HqXMQbA) <!---Readme for @ https://github.com/shrebox/I-am-Kalam--->  If you end up using this code or the data, please cite our paper: ``` @unknown{unknown, author = {Arya, Shreyash and Uberoi, Anannya and Dhawan, Sarthika and Chakraborty, Tanmoy}, year = {2019}, month = {02}, pages = {}, | 3,637 |
shriramsb/rationalizing-neural-predictions | ['sentiment analysis'] | ['Rationalizing Neural Predictions'] | attention_model/AttnEncoder.py generator_encoder_model/Encoder.py generator_encoder_model/TempEncoder.py generator_encoder_model/Generator.py attention_model/main.py attention_model/clusterAttnWeights.py generator_encoder_model/main.py AttnEncoder clusterAttnWeights train Encoder Generator showPlot get_index convert_to_float asMinutes getAccuracy trainIters timeSince train get_list_of_indices TempEncoder mean sort sum range backward zero_grad MSELoss mean attn_encoder float encoderLoss set_major_locator subplots plot MultipleLocator figure floor time initHidden step cost range sample encoder logProb detach showPlot Variable print stepLR Adam extend getAccuracy from_numpy parameters empty array append train step max range len max encoderLoss Variable initHidden extend sample from_numpy MSELoss append encoder empty array range len split append split | # rationalizing-neural-predictions This project is related to "Interpretable Learning" aims to predict the ratings a user would have given to a product based on his textual reviews. In addition to predicting, justifications also have to be provided by the model in terms of excerpts from the text. This project experiments on two methods. First method uses attention weights to find the areas where model focuses on the text. Highly focused text are given justification for prediction. Second method is an implementation of the paper on the same title (https://arxiv.org/abs/1606.04155). | 3,638 |
shrubb/box-convolutions | ['semantic segmentation'] | ['Deep Neural Networks with Box Convolutions'] | box_convolution/box_convolution_module.py examples/Cityscapes/convnet-runtime-benchmark.py box_convolution/test.py examples/Cityscapes/models/ERFNet.py examples/Cityscapes/models/ENet.py examples/Cityscapes/datasets.py setup.py examples/mnist.py box_convolution/__init__.py examples/Cityscapes/train.py box_convolution/box_convolution_function.py parallelCCompile reparametrize BoxConvolutionFunction BoxConv2d test_integral_image test_box_convolution_module main Net train test remove_bn_and_dropout set_boxconv_to_nonexact Cityscapes validate AverageMeter save_checkpoint adjust_learning_rate compute_IoU main train ENet Bottleneck BoxOnlyENet BottleneckBoxConv ENetMinus Upsampler BoxENet Downsampler BoxERFNet BottleneckBoxConv Upsampler ERFNet NonBottleneck1D Downsampler _get_cc_args cpu_count _setup_compile map dtype list rand integral_image integral_image_reference tqdm randint to range list reparametrize box_convolution_reference backward box_convolution_wrapper rand random clone choice tqdm requires_grad_ uniform gradcheck zero_ randint to range model zero_grad numpy dataset addWeighted squeeze pad format param_groups draw_boxes nll_loss item enumerate backward print write repeat step len format print eval dataset len MNIST manual_seed train Adam test parameters DataLoader device is_available to range str named_children __setattr__ Sequential type isinstance data validate save_checkpoint cuda max seed load_state_dict iter run_name parse_args SummaryWriter format ModelWithLogSoftmax start_epoch resume zip add_scalars load join evaluate print Cityscapes isfile epochs update time criterion AverageMeter numel adjust_learning_rate iter compute_IoU cpu zeros cuda print AverageMeter numel eval compute_IoU cpu zeros copyfile save list param_groups map lr_decay lr item tensor split | shrubb/box-convolutions | 3,639 |
shruti-jadon/Siamese-Network-for-One-shot-Learning | ['one shot learning'] | ['Improving Siamese Networks for One Shot Learning using Kernel Based Activation functions'] | Model/train_mnist.py Model/contrastive.py Model/Activation/__init__.py Model/test_contrastive.py Model/net.py Model/intracluster_score.py Model/Activation/kafnets.py ContrastiveLoss SiameseNetwork run_tests TestContrastive create_pairs plot_mnist plot_loss create_iterator main Dataset KAF KAF2D seed manual_seed_all add_argument parse_known_args ArgumentParser manual_seed is_available main reshape min randrange append array range Dataset create_pairs savefig legend range plot clear plot draw savefig legend cla model batchsize SiameseNetwork SGD plot_loss DataLoader ArgumentParser ContrastiveLoss cuda create_iterator load_state_dict parse_args epoch train_plot testing_plots MNIST load train_model print add_argument parameters numpy | shruti-jadon/Siamese-Network-for-One-shot-Learning | 3,640 |
shubhomoydas/ad_examples | ['active learning', 'anomaly detection'] | ['GLAD: GLocalized Anomaly Detection via Human-in-the-Loop Learning', 'Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability', 'Incorporating Feedback into Tree-based Anomaly Detection', 'Active Anomaly Detection via Ensembles'] | ad_examples/dnn/gan.py ad_examples/aad/plot_class_diversity.py ad_examples/timeseries/timeseries_customRNN.py ad_examples/timeseries/activity_word2vec.py ad_examples/ad/spectral_outlier.py ad_examples/aad/test_hyperplane_angles.py ad_examples/graph/gcn_test_support.py ad_examples/classifier/perceptron.py ad_examples/common/timeseries_datasets.py ad_examples/dnn/gan_test_support.py ad_examples/aad/data_stream.py ad_examples/glad/glad_support.py ad_examples/aad/forest_aad_support.py ad_examples/aad/preprocess_electricity_dataset.py ad_examples/common/nn_utils.py ad_examples/timeseries/timeseries_shingles.py ad_examples/loda/loda.py ad_examples/glad/glad_batch.py ad_examples/aad/anomaly_vs_classifier.py ad_examples/aad/simple_aad.py ad_examples/aad/demo_aad.py ad_examples/classifier/svm.py ad_examples/aad/test_concept_drift_classifier.py ad_examples/glad/plot_glad_results.py ad_examples/timeseries/timeseries_regression.py ad_examples/dnn/iso_gan_test_support.py setup.py ad_examples/ad/kde_outlier.py ad_examples/glad/glad_test_support.py datasets/KaggleFirstInternationalCompetitionOfTimeSeriesForecasting/sample_prediction.py ad_examples/aad/multiview_forest.py ad_examples/common/metrics.py ad_examples/common/expressions.py ad_examples/aad/plot_aad_results.py ad_examples/aad/loda_support.py ad_examples/aad/aad_base.py ad_examples/aad/forest_aad_detector.py ad_examples/timeseries/timeseries_arima.py ad_examples/common/test_sgd_optimization.py ad_examples/dnn/ad_autoencoder.py ad_examples/glad/glad_vs_aad.py ad_examples/aad/aad_globals.py ad_examples/aad/anomaly_dataset_support.py ad_examples/classifier/test_svm.py ad_examples/ad/pseudo_anom_outlier.py ad_examples/aad/aad_batch.py ad_examples/dnn/test_iso_gan.py ad_examples/aad/query_model_euclidean.py ad_examples/common/data_plotter.py ad_examples/ad/gmm_outlier.py ad_examples/timeseries/timeseries_rnn.py ad_examples/aad/query_model_other.py ad_examples/aad/random_split_trees.py ad_examples/aad/aad_test_support.py ad_examples/ad/outlier_effect.py ad_examples/common/expressions_tutorial.py ad_examples/aad/analyze_rules.py ad_examples/aad/aad_support.py ad_examples/timeseries/word2vec_custom.py ad_examples/aad/plot_anomalies_rectangle.py ad_examples/dnn/autoencoder.py ad_examples/aad/test_tree_properties.py ad_examples/common/gen_samples.py ad_examples/timeseries/activity_model.py ad_examples/aad/aad_ruleset_support.py ad_examples/aad/test_tree_detectors.py ad_examples/dnn/test_gan.py ad_examples/aad/classifier_trees.py ad_examples/aad/test_data_gen.py ad_examples/common/utils.py ad_examples/timeseries/simulate_timeseries.py ad_examples/aad/preprocess_weather_dataset.py ad_examples/common/sgd_optimization.py ad_examples/graph/simple_gcn.py ad_examples/aad/loda_aad.py ad_examples/aad/forest_description.py ad_examples/bayesian_ruleset/bayesian_ruleset.py ad_examples/aad/query_model.py ad_examples/loda/test_loda.py ad_examples/glad/afss.py ad_examples/aad/aad_loss.py ad_examples/aad/precomputed_aad.py ad_examples/percept/percept.py ad_examples/dnn/iso_gan.py ad_examples/dnn/dnn_classifier.py ad_examples/aad/test_concept_drift.py ad_examples/aad/test_hard_data.py ad_examples/timeseries/word2vec.py ad_examples/glad/test_glad.py ad_examples/ad/ad_outlier.py ad_examples/graph/test_gcn.py ad_examples/timeseries/casas.py ad_examples/ad/pca_reconstruct.py ad_examples/aad/aad_stream.py ad_examples/aad/test_rulesets.py package_files estimate_qtau Aad Budget MetricsStructure get_aad_metrics_structure get_budget_topK AadEventListener Ensemble aad_batch AadListenerForRules SampleData load_all_samples get_aad_command_args load_samples get_first_vals_not_marked get_aad_option_list AadOpts get_anomalies_at_top get_first_val_not_marked aad_loss_gradient_linear aad_loss_linear get_rulesets prepare_conjunctive_rulesets aad_stream prepare_stream_anomaly_detector prepare_aad_model StreamingAnomalyDetector train_aad_model load_aad_model load_aad_metrics save_aad_model summarize_aad_metrics get_linear_score_variance get_score_ranges save_aad_summary SequentialResults get_closest_indexes get_score_variances write_baseline_query_indexes write_sparsemat_to_file save_aad_metrics get_aad_model write_sequential_results_to_csv get_queried_indexes summarize_ensemble_num_seen check_random_vector_angle plot_score_contours plot_queries plot_dataset_2D plot_qval_hist plot_top_regions plot_selected_regions debug_qvals evaluate_forest_original plot_query_diversity plot_aad_2D plot_forest_contours_2D plot_tsne_queries plot_model_baseline_contours_2D plot_anomalous_2D test_ilp aad_unit_tests_battery swap_metadata load_summary plot_blank_image found_precomputed_summaries plot_rule_lengths aggregate_rules_data analyze_rules print_readable_rules summarize_values plot_scores write_all_summaries load_all_rule_data load_rules plot_num_rules analyze_rules_dataset accumulate_values string_agg_scores get_result_defs ResultDefs train_classifier plot_dataset train_anomaly_detector get_debug_args plot_regions get_auc plot_decision_tree_descriptions DecisionTreeAadWrapper RandomForestAadWrapper ClassifierForest IdServer StreamingSupport DataStream get_rearranging_indexes describe_instances detect_anomalies_and_describe get_debug_args RegionData AadForest is_forest_detector transform_features is_in_region BayesianRulesetsDescriber get_instances_for_description get_region_memberships CompactDescriber get_most_anomalous_subspace_indexes get_regions_for_description MinimumVolumeCoverDescriber get_compact_regions InstancesDescriber get_region_indexes_for_instances get_region_volumes AadLoda HistogramPDFs ModelManager get_avg_auc_for_samples get_hpdfs_for_samples PyDataModelManager get_avg_precs_for_samples CsvModelManager IForestMultiview IForestMultiviewTree process_results plot_results plot_diversity_all get_n_intermediate get_result_names get_x_tau plot_anomalies_ifor plot_anomalies_rect process_results get_num_discovered_classes_per_batch iter_by_window plot_results get_num_discovered_classes plot_class_discovery get_result_names test_precomputed_scores AadPrecomputed get_start_row_in_arff Query QueryTop QueryTopRandom QueryRandom QueryQuantile filter_by_euclidean_distance get_mean_euclidean_distance DistanceCache QueryTopDiverseByEuclideanDistance get_min_euclidean_distance QueryTopDiverseSubspace ArrTree RandomSplitTree HPDByInverseCDF rsforest_decision HSTree HSSplitter IForest hstree_decision rsforest_fit RSForest SplitContext Node HSTrees get_tree_partitions hstree_fit RSTree StackRecord RandomTreeBuilder SplitRecord RSForestSplitter RandomSplitForest SimpleActive test_kl_data_drift get_iforest_model test_kl_data_drift_classifier np_mat_to_str plot_dataset get_angles plot_angle_hist test_hyperplane_angles plot_rule_annotations test_aad_rules get_debug_args plot_selected_regions compute_n_found test_tree_detectors plot_labeled_value_hist plot_value_hist get_debug_args test_node_values make_ellipses g_MAD f_MAD plot_samples_pca transform_2D_data get_artificial_2D_data_uniform LabelDiffusion euclidean_dist get_confusion find_lt accumulate log_betabin sanity_check_bayesian_ruleset BayesianRuleset test_bayesian_ruleset Perceptron BinaryLinearSVMClassifier PairwiseLinearSVMClassifier Classifier MultiClassLinearSVMClassifier plot_rect_region plot_sidebar DataPlotter Predicate CmpGr ConjunctiveRule get_feature_meta_default Term convert_conjunctive_rule_to_feature_ranges evaluate_ruleset load_strings_from_file PredicateContext get_rule_satisfaction_matrix test_rule_apis CmpLr Var Atom convert_conjunctive_rules_to_feature_ranges check_if_at_least_one_rule_satisfied DType get_max_len_in_rules CmpGE CmpEq stack BinaryPredicate And convert_feature_ranges_to_rules traverse_predicate_conjunctions Expression Literal NumericContinuous UnaryPredicate conjunctive_predicate_to_list Factor CmpLE convert_conjunctive_rules_to_strings convert_strings_to_conjunctive_rules FeatureMetadata string_to_predicate save_strings_to_file Not evaluate_instances_for_predicate RuleParser Cmp Or dataframe_to_numpy load_data get_demo_samples plot_samples_and_lines get_sample_defs get_synthetic_samples normalize_and_center_by_feature_range load_donut_data interpolate_2D_line_by_slope_and_intercept generate_dependent_normal_samples read_anomaly_dataset load_face_data plot_sample get_hard_samples get_sphere_samples MVNParams interpolate_2D_line_by_point_and_vec AnomalyDataOpts fn_precision fn_auc dnn_layer AutoencoderAnomalyDetector leaky_relu dnn_construct PCA_TF MLPRegressor_TF get_train_batches Autoencoder DenseDNN sgdRMSPropNestorov avg_loss_check sgdAdam debug_log_sgd_losses sgd sgdMomentum get_sgd_batch get_num_batches sgdRMSProp generate_data g f get_loss_grad get_univariate_timeseries_data invert_difference_series_old invert_difference_series log_transform_series TsFileDef DiffScale prepare_tseries difference_series TSeries inverse_log_transform_series DTClassifier ecdf dataframe_to_matrix difftime InstanceList save Timer nrow constr_optim set_seed exception_to_string SKLClassifier RFClassifier rnorm rank rbind append normalize LogisticRegressionClassifier get_command_args read_resource_csv configure_logger append_instance_lists get_sample_feature_ranges rep SVMClassifier read_resource sample power get_option_list matrix dir_create load ncol cbind runif order matrix_rank get_random_item SetList quantile pnorm read_csv read_data_as_matrix autoencoder_ad autoencoder_visualize GanOpts Listener get_nn_layer set_random_seeds GAN get_gan_option_list get_cluster_labels fit_gmm GanListener read_dataset get_gan_sample_defs plot_log_likelihood test_samples plot_sample_hist plot_1D_gan_samples plot_2D_gan_samples get_normal_samples GanOpts Listener get_nn_layer set_random_seeds GAN get_train_batches get_gan_option_list get_cluster_labels fit_gmm GanListener read_dataset get_gan_sample_defs plot_log_likelihood test_samples plot_sample_hist plot_1D_gan_samples plot_2D_gan_samples get_normal_samples test_gan test_ano_gan get_gan_layer_defs get_iso_model test_ano_gan test_gan get_gan_layer_defs suppression_layer GladOpts partition_instances get_unlabeled_batches AFSS get_glad_option_list get_afss_batches construct_network get_glad_command_args afss_active_learn_ensemble glad_active_learn set_random_seeds set_results_dir get_afss_model GLADRelevanceDescriber SequentialResults AnomalyEnsemble to_2D_matrix AnomalyEnsembleLoda GLADEnsembleLimeExplainer test_get_afss_batches plot_glad_relevance_regions plot_dataset get_grid plot_weighted_scores prepare_loda_model_with_w plot_afss_scores prepare_loda_ensemble plot_scores test_loda get_top_ranked_instances plot_ensemble_scores populate_aad_opts aad_active_learn aad_active_learn_ensemble AadWithExisingEnsemble get_precomputed_aad_args process_glad_results get_glad_result_defs get_results get_glad_result_names test_glad get_synth_graph_adjacency read_graph_dataset plot_nodes test_create_gcn_default read_dataset test_tensorflow_array_differentiation plot_model_diagnostics test_edge_sample read_datasets_for_illustration plot_labels_with_modified_node plot_edges find_insts gradients_to_arrow_texts read_face_dataset_with_labels test_neighbor_gradients nodes_to_arrow_texts test_marked_nodes get_target_and_attack_nodes sample_indexes plot_graph test_robust_training_helper plot_arrow_texts read_synth_graph_dataset_with_labels SimpleGCNAttack get_nn_layer set_random_seeds euclidean_dist AdversarialUpdater sign SimpleGCN get_gcn_option_list GraphAdjacency GcnOpts EnsembleGCN NoopSampleUpdater SampleUpdater create_gcn_default test_gcn build_proj_hist get_all_hist_pdfs get_neg_ll_all_hist loda get_bin_for_equal_hist LodaModel pdf_hist get_zero_var_features LodaResult Loda ProjectionVectorsHistograms get_original_proj histogram_r_mod histogram_r pdf_hist_equal_bins HistogramR get_random_proj get_best_proj get_neg_ll get_param_sig Oracle plot_learning plot_original_feature_tsne ActivityRNN Casas write_sensor_data_as_document maybe_download_casas SimTs MA1 generate_synthetic_activity_data Sinusoidal read_activity_data write_to_file rolling_forecast_ARIMA time_lag_diff plot_lag_difference forecast_and_report_anomalies fit_ARIMA TsRNNCustom find_anomalies_with_regression TsRNN read_ts find_anomalies_with_shingles Word2vec CustomWord2vec join extend isfile append listdir int topK min maxbudget tau round budget topK min dot get_random_weights get_budget_topK quantile append zeros float max range MetricsStructure append zeros precision_k range len get_score read_data_as_matrix getLogger cumsum all_regions SequentialResults set_multi_run_options debug_qvals decision_function write_sequential_results_to_csv Timer get_queried_indexes summarize_ensemble_num_seen aad_unit_tests_battery str list aad_learn_ensemble_weights_with_budget len write_baseline_query_indexes randseed evaluate_forest_original transform_to_ensemble_features savetxt rbind output_all_data append sum budget fid get_aad_command_args RandomState message debug reruns get_runidxs init_weights get_alad_metrics_name_prefix datafile resultsdir get_aad_model zeros Ensemble join T get_uniform_weights str_opts is_forest_detector detector_type linesep argsort streaming AadOpts AadListenerForRules plot_tsne_queries configure_logger fit add_argument ArgumentParser parse_args argv get_aad_option_list nan range len append range len order zeros sum range len ndarray read_csv array append join load_samples dot max range len max list ncol dot rep append zeros sum array range len set_confusion_matrix convert_strings_to_conjunctive_rules where_satisfied convert_feature_ranges_to_rules sum enumerate BayesianRulesetsDescriber get_top_regions CompactDescriber prepare_conjunctive_rulesets describe get_feature_meta_default convert_conjunctive_rules_to_strings array RandomState fid runidx reruns randseed init_weights get_aad_model fit load_aad_model str list debug len is_forest_detector detector_type all_regions w modelfile train_aad_model stream_window update_model_from_buffer pretrain allow_stream_update update_weights_with_no_feedback get_sample_feature_ranges read_next_from_stream prepare_aad_model get_next_from_stream StreamingAnomalyDetector max_buffer init_query_state x move_buffer_to_unlabeled getLogger cumsum update_weights_with_no_feedback SequentialResults set_multi_run_options write_sequential_results_to_csv Timer max_buffer run_feedback seed move_buffer_to_unlabeled ones len randseed rbind append fid get_aad_command_args message debug allow_stream_update DataStream get_runidxs resultsdir matrix zeros IdServer max_windows update_model_from_buffer str_opts prepare_stream_anomaly_detector AadOpts get_next_from_stream configure_logger array read_data_as_matrix AadForest is_forest_detector detector_type AadLoda AadPrecomputed metrics cumsum labels rbind zeros range queried len save list min dot quantile append max range var str todense list debug dot shape sum array str T list arange debug add zeros matrix sum get_linear_score_variance message debug min get_closest_indexes SetList set Timer zeros array range enumerate len cumsum join debug get_alad_metrics_name_prefix savetxt resultsdir zeros enumerate stream_window join num_seen_baseline copy get_alad_metrics_name_prefix aucs stream_window_baseline savetxt true_queried_indexes resultsdir num_not_seen true_queried_indexes_baseline num_seen cumsum zeros queried len join ndarray isinstance write linesep close savetxt range flush open dump close open load close open save print get_metrics_path load get_next_plot close plot_points DataPlotter filedir join asarray plot_queries debug len resultsdir dataset read_csv queried plot_top_regions plot_query_diversity linspace vstack Timer plot_forest_contours_2D transform_to_ensemble_features evaluate_forest_original savetxt plot_aad_2D meshgrid budget d message debug write_sparsemat_to_file plot_anomalous_2D plot_dataset_2D join T print is_forest_detector detector_type plot_model_baseline_contours_2D len str list arccos debug pi dot get_random_weights zeros range len arange close bar histogram append get_next_plot DataPlotter estimate_qtau get_uniform_weights arccos topK plot_qval_hist debug min runidx pi dot get_budget_topK quantile float max range get_score Timer transform_to_ensemble_features shape contourf get_next_plot queried plot_points message debug close start matrix enumerate plot_sidebar reshape argsort array DataPlotter get_score plot_points plot_sidebar message debug reshape close w transform_to_ensemble_features start shape argsort contourf linspace Timer meshgrid get_next_plot DataPlotter get_score T d cumsum reshape ones argsort dot decision_function len get_score plot_points plot_sidebar ones cumsum print reshape matrix close dot argsort transform_to_ensemble_features shape contourf get_next_plot get_num_members DataPlotter plot_points plot_sidebar message reshape debug close argsort start decision_function shape contourf Timer get_next_plot matrix DataPlotter plot_points message debug is_forest_detector detector_type close regions_in_forest start plot_rect_region region Timer get_next_plot DataPlotter enumerate plot_points close plot_rect_region title region get_next_plot DataPlotter n_estimators debug is_forest_detector detector_type plot_selected_regions argsort start Timer get_instances_for_description message n_estimators debug is_forest_detector detector_type plot_selected_regions get_sample_feature_ranges start get_regions_for_description get_compact_regions Timer get_region_volumes get_score describe_n_top describe_volume_p get_regions_for_description Timer filter_by_euclidean_distance QueryTopDiverseSubspace plot_selected_regions get_region_volumes get_region_memberships message n_estimators debug filter_by_diversity w get_sample_feature_ranges start is_forest_detector detector_type argsort get_compact_regions zeros len T print matrix sum ilp join load_strings_from_file budget debug convert_strings_to_conjunctive_rules evaluate_ruleset dict mean rule_output_interval isfile append range len append items list sorted ndarray reshape vstack keys str debug extend dict summarize_values accumulate_values append sorted keys debug aggregate_rules_data get_alad_metrics_name_prefix set_multi_run_options get_runidxs load_rules append join savetxt resultsdir join debug asmatrix dict resultsdir read_csv join debug resultsdir plot xlabel ylabel ylim title legend append get_next_plot plot xlabel ylabel ylim title legend append get_next_plot max plot xlabel ylabel title legend append get_next_plot text xlim ylim get_next_plot xticks yticks get swap_metadata join get_feature_meta_default debug dataset load_all_rule_data read_data_as_matrix get swap_metadata load_summary plot2D plot_blank_image get_feature_meta_default debug plot_rule_lengths plot_scores write_all_summaries plot_num_rules dataset load_all_rule_data read_data_as_matrix replace print close print_readable_rules analyze_rules_dataset datafile resultsdir dataset DataPlotter budget get_score fn_auc cbind plot_rect_region region get_score plot_dataset linspace randseed transform_to_ensemble_features shape contourf update_weights meshgrid get_next_plot range plot_points RandomState debug describe_instances close copy w init_weights get_aad_model n_pretrain plot_sidebar reshape fit argsort get_auc array DataPlotter len plot_points DataPlotter plot_sidebar RandomForestAadWrapper plot_dataset reshape debug describe_instances close argsort shape clf contourf linspace meshgrid get_next_plot predict_prob_for_class fit DecisionTreeAadWrapper debug describe_instances plot_dataset debug close scatter plot_regions legend get_next_plot DataPlotter arange bayesian_rules get_region_memberships join CompactDescriber describe debug MinimumVolumeCoverDescriber append zeros sum BayesianRulesetsDescriber get_score cumsum get_initial_query_state update_query_state qtype str list ones randseed transform_to_ensemble_features order_by_score update_weights append RandomState debug describe_instances w init_weights get_aad_model print extend argsort array get_next_query fit range len zeros range enumerate is_in_region multiply d w multiply array get_region_ids len region zeros prod range enumerate len array queried update list set argsort array enumerate get_region_ids len list reshape len vstack region append zeros array enumerate is_in_region get_region_memberships reshape debug array matrix ilp HistogramPDFs get_all_hist_pdfs fmat append range len fn_auc dot append lbls empty range len fn_precision lbls dot zeros empty range len astype len minimum arange errorbar plot xlabel debug close ylabel ylim get_n_intermediate legend get_next_plot xlim max enumerate DataPlotter len max list num_anoms debug dataset plot_results dir_create get_results append get_result_defs get_result_names enumerate len arange plot xlabel len close ylabel ylim legend get_next_plot xlim enumerate DataPlotter dir_create dot argsort interpolate_2D_line_by_slope_and_intercept pi axhline get_sphere_samples get_x_tau xticks yticks set_aspect list ones axvline shape ylim scatter legend append get_next_plot normalize plot close interpolate_2D_line_by_point_and_vec xlim enumerate join dot array DataPlotter len interpolate_2D_line_by_slope_and_intercept vstack get_x_tau xticks yticks set_aspect list ones shape uniform ylim scatter legend append normalize get_next_plot mu mcorr plot close MVNParams interpolate_2D_line_by_point_and_vec xlim enumerate join dvar arctan reshape generate_dependent_normal_samples dot zeros array DataPlotter arange plot xlabel debug close ylabel ylim legend get_next_plot xlim enumerate DataPlotter len add set zeros range enumerate len len list set vstack append iter_by_window array range len get_original_labels arange cumsum axhline str std get_window_indexes ylabel ylim legend get_next_plot plot close mean sqrt plot_class_discovery xlim xlabel get_num_discovered_classes_per_batch get_queried DataPlotter get_score read_data_as_matrix getLogger cumsum str aad_learn_ensemble_weights_with_budget list len AadPrecomputed randseed transform_to_ensemble_features queried RandomState get_aad_command_args debug init_weights datafile resultsdir Ensemble get_uniform_weights str_opts AadOpts configure_logger fit add_dist get_dist sum len get_dist add_dist min sum Inf list extend delete get_mean_euclidean_distance argsort DistanceCache append zeros array enumerate get_min_euclidean_distance len ones int sum var ecdf x_cdf check_random_state arange min shuffle HSTree fit decision_function Timer RSTree check_random_state arange min shuffle fit decision_function Timer init_weights AadForest fit getLogger dataset max get_trees_to_replace seed list ones read_anomaly_dataset ylabel ylim update_model_from_stream_buffer scatter legend append get_next_plot range get_command_args get_iforest_model n_estimators plot debug close DataStream read_next_from_stream xlim IdServer int add_samples xlabel text get_node_sample_distributions get_KL_divergence_distribution configure_logger DataPlotter len RandomForestAadWrapper getLogger dataset RF get_trees_to_replace read_anomaly_dataset get_command_args n_estimators debug DataStream w read_next_from_stream IdServer int fit get_node_sample_distributions get_KL_divergence_distribution configure_logger len ylim unique xlim range arccos pi dot zeros range str list arange xlabel debug rc ylabel bar histogram get_next_plot get_score read_data_as_matrix all_regions pi seed str list len randseed transform_to_ensemble_features append RandomState get_aad_command_args arccos debug close w get_runidxs init_weights datafile resultsdir get_aad_model init power plot_angle_hist dir_create get_uniform_weights get_angles str_opts is_forest_detector detector_type argsort dot streaming get_auc AadOpts Perceptron configure_logger array DataPlotter fit xlabel ylabel scatter legend join text xlim ylim get_next_plot xticks yticks describe get_feature_meta_default evaluate_ruleset plot_rule_annotations dataset load_strings_from_file str get_rulesets read_anomaly_dataset plot_selected_regions names convert_conjunctive_rules_to_feature_ranges get BayesianRulesetsDescriber asarray CompactDescriber prepare_conjunctive_rulesets debug close detect_anomalies_and_describe resultsdir join print convert_conjunctive_rules_to_strings save_strings_to_file array DataPlotter argsort cumsum len get_score dataset str list transpose read_anomaly_dataset randseed transform_to_ensemble_features append RandomState debug hstack copy init_weights get_aad_model init enumerate get_uniform_weights fn_auc str_opts AadOpts zeros fit min bar histogram get_next_plot max str list arange debug bar histogram get_next_plot get_score read_data_as_matrix all_regions plot_labeled_value_hist seed plot_value_hist len randseed transform_to_ensemble_features d RandomState get_aad_command_args debug close init_weights datafile resultsdir get_aad_model init str_opts is_forest_detector detector_type streaming AadOpts configure_logger DataPlotter fit norm arctan2 Ellipse set_clip_box n_components pi add_artist eigh sqrt eye range diag bbox mean abs dot dot multiply transpose mean plot_points xlabel close ylabel get_next_plot DataPlotter hstack hstack sqrt sum sum array len isinstance lgamma print zip append next func iter bisect_left str precompute greedy_init screen_rules set_parameters debug compute_prob shape propose BayesianRuleset bayesian_pattern_based join asarray get_feature_meta_default print convert_strings_to_conjunctive_rules read_anomaly_dataset predicted_rules BayesianRuleset fit min Rectangle add_patch plot cbind ones add_patch Rectangle len compile parse compile isinstance append p2 p1 PredicateContext traverse_predicate_conjunctions items list join parse isfinite append val featuredefs predicates varindex dict is_continuous enumerate append parse zeros where_satisfied enumerate zeros where_satisfied precision_recall_fscore_support check_if_at_least_one_rule_satisfied Factor FeatureMetadata unique append range evaluate str asarray parse get_feature_meta_default print set_confusion_matrix where_satisfied read_anomaly_dataset evaluate_instances_for_predicate RuleParser compile StringIO read_csv zeros range isinstance mvn normal rvs fill_diagonal reshape diag copy dot sqrt range ones hstack uniform vstack append zeros list mcorr dvar generate_dependent_normal_samples extend rbind get_sample_defs zeros range mu len list get_synthetic_samples max read_resource_csv debug min shape zeros array AnomalyDataOpts read_resource_csv dataframe_to_matrix read_data_as_matrix mean min max max read_resource_csv debug min shape array load_face_data get_synthetic_samples load_donut_data plot_points plot xlabel close ylabel ylim scatter legend get_next_plot xlim DataPlotter enumerate plot_points xlabel close ylabel get_next_plot DataPlotter nrow float sum range minimum list cumsum maximum range extend rank nrow float sum max zeros len list min arange shuffle range min arange mean debug Timer inf arange debug f grad shuffle copy debug_log_sgd_losses dot mean isnan copyto get_sgd_batch get_num_batches zeros range arange Timer copyto multiply f get_num_batches range inf debug grad shuffle copy mean sqrt get_sgd_batch debug_log_sgd_losses isnan dot zeros len Timer inf arange debug f grad shuffle copy debug_log_sgd_losses mean isnan dot copyto get_sgd_batch get_num_batches zeros range len arange Timer copyto multiply f get_num_batches range inf debug grad shuffle copy mean sqrt get_sgd_batch debug_log_sgd_losses isnan dot zeros len arange Timer copyto multiply f get_num_batches range inf debug grad shuffle copy mean sqrt get_sgd_batch debug_log_sgd_losses isnan dot zeros len normal sort dot uniform zeros range mean dot mean multiply transpose path read_resource_csv log exp shape range zeros shape range zeros shape range zeros add_argument ArgumentParser parse_args get_option_list argv seed shape float int isinstance list Ranking ranks argsort FRACTIONAL COMPETITION empty array shuffle min max extend isinstance csr_matrix isinstance timedelta dot sqrt randint x_transformed y ids vstack rbind append get_data print BytesIO read_csv read_resource zeros array range startcol datafile read_csv labelindex dump open open makedirs basicConfig getLogger minimize debug nrow array range plot_points PCA_TF DiffScale close Autoencoder title transform get_next_plot dataset fit_transform DataPlotter fit AutoencoderAnomalyDetector debug fn_auc hstack decision_function max fit seed set_random_seed infty bic append GaussianMixture range fit debug n_components predict fit_gmm add_argument ArgumentParser list mcorr dvar get_gan_sample_defs reshape generate_dependent_normal_samples append zeros mu enumerate load_donut_data ones read_anomaly_dataset load_face_data append zeros dataset get_normal_samples arange plot xlabel ylabel scatter histogram unique legend enumerate ones close plot_sample_hist get_next_plot zeros DataPlotter plot_points make_ellipses close ylim get_next_plot xlim DataPlotter errorbar plot close get_next_plot DataPlotter str read_dataset debug close shape plot_sample_hist get_cluster_labels plot_2D_gan_samples get_next_plot zeros DataPlotter min max str list get_anomaly_score append get_next_plot range plot_points plot debug subtract close enumerate join text min argsort zeros array n_ano_gan_test DataPlotter len ano_gan read_dataset get_gan_layer_defs conditional plot_2D_gan_samples str list get_gen_output_samples len get_gen_input_samples sum range plot plot_log_likelihood debug GAN init_session unique plot_1D_gan_samples join test_ano_gan save_session eye get_cluster_labels lls fit reshape randseed decision_function IsolationForest fit get_iso_model Timer shape message iso_gan list ones min arange shuffle range arange ones debug min shuffle argsort repeat vstack range len add_argument ArgumentParser parse_args get_glad_option_list argv cumsum init_network SequentialResults Timer str list close_session ones get_weighted_scores update_afss range budget plot_glad_relevance_regions message get_afss_model debug explain plot_weighted_scores plot_afss_scores get_scores extend argsort get_first_vals_not_marked array GLADEnsembleLimeExplainer SequentialResults Timer dataset set_random_seeds read_anomaly_dataset randseed afss_active_learn_ensemble results_dir append range message set_results_dir debug reruns prepare_loda_ensemble merge dir_create m write_to_csv str max_afss_epochs debug afss_l2_lambda extend AFSS append afss_nodes len list interpolate_2D_line_by_point_and_vec plot array keys range meshgrid linspace reshape get_grid colorbar shape decision_function set_ylabel contourf fn_score_transform plot_dataset debug close plot_scores get_scores DataPlotter get_next_plot get_ensemble_type arange plot_dataset reshape get_grid close colorbar shape decision_function set_ylabel contourf get_scores range DataPlotter get_next_plot str list plot_dataset debug get_grid reshape close colorbar get_weighted_scores argsort shape set_ylabel contourf get_scores DataPlotter get_next_plot describe plot_dataset GLADRelevanceDescriber close plot_rect_region title get_member_relevance_scores_ranks get_projections get_next_plot DataPlotter enumerate build_proj_hist LodaModel arange LodaResult Loda ProjectionVectorsHistograms get_neg_ll_all_hist str message debug transpose prepare_loda_model_with_w dot sqrt shape Loda Timer sum diag fit argsort decision_function str list partition_instances debug read_anomaly_dataset prepare_loda_ensemble get_top_ranked_instances get_afss_batches get_scores dataset set_results_dir plot read_anomaly_dataset plot_ensemble_scores prepare_loda_ensemble results_dir get_top_ranked_instances dataset dir_create reruns afss_tau budget get_score cumsum get_initial_query_state SequentialResults update_query_state qtype str list ones transform_to_ensemble_features order_by_score update_weights append debug w init_weights AadWithExisingEnsemble extend argsort array get_next_query set_random_seeds message debug SequentialResults randseed aad_active_learn_ensemble Timer m ensemble_type write_to_csv enumerate merge mean subtract get_per_run_results std get_glad_result_defs list num_anoms get_glad_result_names debug plot_results set dir_create get_results append dataset max enumerate values len init_network get_top_ranked_instances dataset str set_random_seeds close_session read_anomaly_dataset get_weighted_scores randseed shape results_dir plot_ensemble_scores update_afss range set_results_dir plot get_afss_model debug plot_weighted_scores plot_afss_scores prepare_loda_ensemble get_scores dir_create log_probability_ranges get_demo_samples vstack all where array array shuffle int check_random_state arange check_random_state read_face_dataset_with_labels debug where sample_indexes read_synth_graph_dataset_with_labels len get_synth_graph_adjacency GraphAdjacency read_dataset build_adjacency read_graph_dataset dataset append append get text arrow plot_points plot_arrow_texts enumerate range plot get_next_plot title plot_nodes plot_edges append debug find_insts str Variable debug concat matmul placeholder zeros get_target_and_attack_nodes debug close read_datasets_for_illustration plot_graph unique get_opts_name_prefix dataset DataPlotter len get_target_and_attack_nodes sample_edges debug close read_datasets_for_illustration plot_graph GraphAdjacency unique n_neighbors get_opts_name_prefix dataset range DataPlotter len fit_x fit_A copy plot_graph array gradients_to_arrow_texts DataPlotter plot_labels_with_modified_node fit_y fit_x close extend fit_A nodes_to_arrow_texts plot_graph modify_gcn_and_predict gcn array predict create_gcn_default debug get_f1_score read_datasets_for_illustration max fit plot_model_diagnostics Timer get_opts_name_prefix suggest_nodes max read_datasets_for_illustration SimpleGCNAttack get_f1_score sample_neighbors append message debug GraphAdjacency n_vulnerable enumerate fit fit_A dict get_top_uncertain_nodes create_gcn_default str create_gcn_default debug len get_f1_score AdversarialUpdater read_datasets_for_illustration restore_values select_and_update_nodes max fit n_sub_layers n_layers SimpleGCN append EnsembleGCN range ensemble add_argument ArgumentParser SimpleGCNAttack create_gcn_default get_target_and_attack_nodes close_session print debug fit get_f1_score suggest_nodes get_opts_name_prefix shape find_minimum_modification plot_model_diagnostics append zeros dataset max read_datasets_for_illustration argmax int arange debug HistogramR isfinite range histogram zeros sum log len int list message debug HistogramR isfinite range histogram Timer append zeros sum array log len trunc array density len density breaks zeros max range get_bin_for_equal_hist len int arange randn min extend delete sqrt floor sample zeros sum range len ncol dot append range histogram_r_mod dot zeros pdf_hist_equal_bins pdf_hist dot zeros range len get_all_hist_pdfs vfunc vectorize log ncol get_random_proj ones mean append nrow abs get_neg_ll zeros Inf append zeros ncol range Timer ncol hists message arange debug w get_neg_ll_all_hist get_best_proj nrow get_original_proj max append ncol range interpolate_2D_line_by_slope_and_intercept get_x_tau xticks yticks set_aspect str list title ylim scatter tau legend fixed_tau append get_next_plot plot debug w tau_relative interpolate_2D_line_by_point_and_vec xlim enumerate text dot array get_batches plot_points TSNE reshape debug algo close get_next_plot fit_transform DataPlotter join urlretrieve exists stat insert transpose hstack savetxt len rvs arange get_samples write_to_file argmax str list axvline title append get_next_plot range plot debug set_xlim close multinomial zeros array DataPlotter len asarray read_csv zeros len range shape to_datetime arange range len get_univariate_timeseries_data arange plot close time_lag_diff title get_next_plot dataset array DataPlotter len arange str list axvline log_transform time_lag_diff title fit_ARIMA get_next_plot pacf get_univariate_timeseries_data log_transform_series plot debug close tight_layout ARIMA_order int print rolling_forecast_ARIMA acf array DataPlotter len arange inverse_transform abs get_batches str list DiffScale len axvline SVR prepare_tseries append get_next_plot MLPRegressor_SK fit_transform range plot debug hstack set_xlim close scale int RandomForestRegressor invert_difference_series reshape MLPRegressor_TF difference_series DataPlotter fit arange TSeries max get_shingles str OneClassSVM samples get_next_plot AutoencoderAnomalyDetector log_transform_series plot LocalOutlierFactor debug set_xlim close get_sample_feature_ranges IsolationForest reshape difference_series DataPlotter fit list get_univariate_timeseries_data read_resource_csv asarray print array append keys | Python libraries required: -------------------------- six (1.15.0) numpy (1.18.4) scipy (1.4.1) scikit-learn (0.23.0) cvxopt (1.1.9) pandas (0.22.0) ranking (0.3.1) statsmodels (0.9.0) | 3,641 |
shuix007/HMGNN | ['formation energy'] | ['Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties'] | Preprocess.py Model/torch_layers/mlp.py Model/torch_layers/rbf.py Data.py Util.py Model/dgl_layers/interaction_layer.py Model/__init__.py DataLoader.py Model/torch_layers/normalization.py Model/dgl_layers/__init__.py Model/torch_layers/__init__.py Model/torch_layers/activation.py main.py Model/HMGNN.py Model/torch_layers/initializer.py setxor my_float Molecule DataLoader train trainIter parse_id load_badmoleculars preprocess train_val_test_split lr_scheduler save_model_state evaluate post_op_process load_model_state evaluate_gap FussionModule DistGraphInputModule LineGraphInputModule OutputModule sum_hetero_nodes HMGNN HoConv shifted_softplus GlorotOrthogonal DenseLayer ResLayer HeteroGraphNorm RBF AngleRBF DistRBF ShrinkDistRBF append len model batch_size backward squeeze post_op_process zero_grad l1_loss step join time lr_scheduler save_model_state evaluate print train write close save to range next_random_batch open find listdir array shuffle setdiff1d append Molecule zfill tqdm save load load_state_dict param_groups clamp_ eval eval batch_size batch_num_nodes from_numpy repeat device to var size orthogonal_ sqrt mul_ | # Heterogeneous Molecular Graph Neural Network (HMGNN) This is an implementation of the Heterogeneous Molecular Graph Neural Network (HMGNN) proposed in the paper: **[Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties](https://arxiv.org/abs/2009.12710)** by Zeren Shui, George Karypis To appear in ICDM 2020. ## Requirement We implemented this software using the Deep Graph Library (DGL) with PyTorch backend. To run this code, you need ``` ase tqdm | 3,642 |
shuuchen/frelu.pytorch | ['scene generation', 'semantic segmentation'] | ['Funnel Activation for Visual Recognition'] | train.py resnet.py frelu.py FReLU conv1x1 resnext50_32x4d BasicBlockFReLU ResNet resnet50 wide_resnet50_2 resnext101_32x8d Bottleneck resnet152 wide_resnet101_2 conv3x3 _resnet BottleneckFReLU resnet34 resnet50_frelu resnet18 BasicBlock resnet101 validate AverageMeter accuracy save_checkpoint ProgressMeter adjust_learning_rate main_worker main train ResNet load_state_dict load_state_dict_from_url 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 pretrained DataParallel DistributedDataParallel ImageFolder DataLoader adjust_learning_rate arch save_checkpoint features cuda max 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 ImageNet 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 | # frelu.pytorch An unofficial pytorch implementation of funnel activation https://arxiv.org/pdf/2007.11824.pdf. Official implementation can be found [here](https://github.com/megvii-model/FunnelAct). <img src="https://github.com/shuuchen/frelu.pytorch/blob/master/images/frelu.png" width="480" height="220" /> ## Requirements ``` pip install -r requirements.txt ``` ## Usage * Simply replace nn.ReLU with FReLU(num_channels), details can be found [here](https://github.com/shuuchen/frelu.pytorch/blob/master/resnet.py). ```python | 3,643 |
shyamupa/biling-survey | ['word embeddings'] | ['Cross-lingual Models of Word Embeddings: An Empirical Comparison'] | wsim/eval-word-vectors/wordsim.py wsim/eval-word-vectors/stat_signf.py wsim/eval-word-vectors/all_wordsim.py wsim/qvec/qvec_cca2.py wsim/eval-word-vectors/corrstats.py bldict/sensmap.py wsim/eval-word-vectors/ranking.py wsim/eval-word-vectors/read_write.py load_sensemap independent_corr dependent_corr rz_ci rho_rxy_rxz cosine_sim assign_ranks correlation euclidean spearmans_rho read_word_vectors compute_XY compute_vs_gold ReadVectorMatrix NormCenterMatrix ReadOracleMatrix ComputeCCA main combine_dicts GetVocab lower pow ppf atanh float pow pow sqrt rho_rxy_rxz cdf abs pow sqrt cdf abs log append len sum enumerate iter zip sum values len list sum keys len endswith write lower split open zeros float enumerate len float lower cosine_sim split cosine_sim lower split file_vocab set update items list sorted print set verbose loads open zeros keys combine_dicts enumerate split decode sorted endswith split open float enumerate append len mean normalize minimum svd T NormCenterMatrix min maximum dot qr ReadVectorMatrix print ReadOracleMatrix set verbose in_vectors ComputeCCA split GetVocab | Data and scripts for reproducing the results from the [ACL 2016 paper](http://arxiv.org/abs/1604.00425). The vectors used for the experiments can be found [here](http://bilbo.cs.illinois.edu/~upadhya3/embedding-release.zip). ## Running Monolingual Evaluation ### Running QVec Simply run ``` python qvec_cca2.py --in_vectors ~/mydir/en1.vectors ``` ### Running Word Similarity with Steigler's p-value Run the following over a pair of models for which you want to compute whether the difference is significant, | 3,644 |
siat-nlp/MAMS-for-ABSA | ['sentiment analysis', 'aspect based sentiment analysis'] | ['A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis'] | data_process/vocab.py src/module/attention/multi_head_attention.py train/eval.py train/make_optimizer.py src/module/attention/attention.py src/aspect_category_model/capsnet.py src/module/attention/bilinear_attention.py src/module/utils/loss.py train/make_data.py train/train.py train.py data_process/dataset.py src/module/attention/tanh_bilinear_attention.py src/module/attention/tanh_concat_attention.py src/module/utils/constants.py src/aspect_category_model/bert_capsnet.py data_process/data_process.py train/make_aspect_term_model.py src/module/utils/sentence_clip.py src/aspect_category_model/recurrent_capsnet.py src/module/attention/concat_attention.py train/test.py src/aspect_term_model/recurrent_capsnet.py src/module/attention/dot_attention.py src/aspect_term_model/capsnet.py train/make_aspect_category_model.py test.py preprocess.py src/aspect_term_model/bert_capsnet.py src/module/utils/squash.py data_process/utils.py src/module/attention/mlp_attention.py src/module/attention/no_query_attention.py src/module/attention/scaled_dot_attention.py ABSADataset data_process save_term_data load_sentiment_matrix parse_sentence_category check tokenizer analyze_term category_filter analyze_category save_category_data load_glove parse_sentence_term build_vocab Vocab BertCapsuleNetwork CapsuleNetwork RecurrentCapsuleNetwork BertCapsuleNetwork CapsuleNetwork RecurrentCapsuleNetwork Attention BilinearAttention ConcatAttention DotAttention MlpAttention MultiHeadAttention NoQueryAttention ScaledDotAttention TanhBilinearAttention TanhConcatAttention CrossEntropyLoss_LSR CapsuleLoss SmoothCrossEntropy sentence_clip squash eval make_recurrent_capsule_network make_embedding make_model make_bert_capsule_network make_recurrent_capsule_network make_embedding make_model make_bert_capsule_network make_category_data make_term_test_data make_distill_data make_term_data make_category_test_data make_optimizer test train join save_term_data load_sentiment_matrix parse_sentence_category makedirs len category_filter save_category_data save load_glove parse_sentence_term build_vocab get parse text lower getroot append find get parse text lower getroot append find append set Vocab tokenizer add_list g asarray savez convert_tokens_to_ids makedirs len extend dirname append range tokenize max_length split g asarray savez convert_tokens_to_ids makedirs len extend dirname append range tokenize max_length split len tokenizer append sum max split len tokenizer append sum max split uniform load set sqrt zeros std open sum item sqrt sum from_pretrained join load_sentiment safe_load BertCapsuleNetwork open join load_sentiment Embedding make_embedding safe_load RecurrentCapsuleNetwork open load join Embedding copy_ safe_load tensor open join DataLoader ABSADataset join DataLoader ABSADataset join DataLoader ABSADataset join DataLoader ABSADataset join DataLoader ABSADataset parameters load join make_model make_term_test_data print eval load_state_dict cuda make_category_test_data model clip_grad_norm_ zero_grad save argmax cuda make_optimizer dirname CapsuleLoss make_term_data range state_dict make_model size eval enumerate make_category_data join time criterion backward print parameters step makedirs | siat-nlp/MAMS-for-ABSA | 3,645 |
siavashBigdeli/DDE | ['density estimation', 'denoising'] | ['Learning Generative Models using Denoising Density Estimators'] | utils.py sample_2d_data normal concatenate cos shuffle pi random_integers sqrt stack uniform floor sin array | siavashBigdeli/DDE | 3,646 |
siavashk/pycpd | ['density estimation'] | ['Point-Set Registration: Coherent Point Drift'] | examples/fish_rigid_3D.py examples/fish_affine_3D.py pycpd/affine_registration.py testing/rigid_test.py testing/deformable_test.py pycpd/emregistration.py examples/fish_deformable_2D.py examples/fish_deformable_3D.py testing/affine_test.py examples/fish_rigid_2D.py examples/fish_deformable_3D_register_with_subset_of_points.py pycpd/rigid_registration.py examples/fish_affine_2D.py examples/fish_deformable_3D_lowrank.py examples/bunny_rigid_3D.py pycpd/utility.py pycpd/deformable_registration.py setup.py pycpd/__init__.py readme main visualize main visualize main visualize main visualize main visualize main visualize main visualize main visualize main visualize AffineRegistration gaussian_kernel low_rank_eigen DeformableRegistration EMRegistration initialize_sigma2 lowrankQS RigidRegistration is_positive_semi_definite test_2D test_3D test_2D test_3D test_3D_low_rank test_2D test_3D format text2D pause draw scatter legend cla show partial RigidRegistration loadtxt add_subplot figure register text AffineRegistration add_axes ones vstack zeros DeformableRegistration transform_point_cloud scatter legend sum square eigh list shape eigh list AffineRegistration loadtxt dot tile assert_array_almost_equal register array AffineRegistration ones loadtxt dot vstack tile assert_array_almost_equal zeros register array DeformableRegistration DeformableRegistration transform_point_cloud ones loadtxt randint DeformableRegistration vstack assert_array_almost_equal zeros register RigidRegistration pi assert_almost_equal RigidRegistration pi assert_almost_equal | siavashk/pycpd | 3,647 |
siddhant-doshi/Dr-COVID | ['link prediction'] | ['Dr-COVID: Graph Neural Networks for SARS-CoV-2 Drug Repurposing'] | source_code.py | # Drug_repurposing We design a generic graph neural network (GNN) based drug repurposing model, called GDRnet. Database used - DRKG - https://github.com/gnn4dr/DRKG Covid_clinical_drugs - List of drugs in clinical trials for COVID-19 as per ICTRP (https://www.who.int/ictrp/). Using_our_model - A demo of how we can predict drugs for diseases with GDRnet. | 3,648 |
siddhantjain/PointCloudAnnotationTool | ['few shot learning'] | ['Few-Shot Point Cloud Region Annotation with Human in the Loop'] | third_party/cvui/example/src/multiple-files/Class2.py third_party/cvui/example/src/row-column/row-column.py third_party/cvui/example/src/sparkline/sparkline.py third_party/cvui/example/src/mouse/mouse.py third_party/cvui/example/src/multiple-windows-complex-mouse/multiple-windows-complex-mouse.py third_party/cvui/example/src/nested-rows-columns/nested-rows-columns.py src/Python/src/pointnet_part_seg.py third_party/cvui/example/src/trackbar-complex/trackbar-complex.py third_party/cvui/cvui.py third_party/cvui/example/src/image-button/image-button.py third_party/cvui/example/src/mouse-complex/mouse-complex.py third_party/cvui/example/src/multiple-files/multiple-files.py third_party/cvui/example/src/main-app/main-app.py third_party/cvui/example/src/complext-layout/complex-layout.py third_party/cvui/example/src/canny/canny.py third_party/cvui/example/src/multiple-windows-complex-dynamic/multiple-windows-complex-dynamic.py src/Python/src/provider.py third_party/cvui/example/src/mouse-complex-buttons/mouse-complex-buttons.py third_party/cvui/example/src/ui-enhanced-canny/ui-enhanced-canny.py third_party/cvui/python/package/setup.py third_party/cvui/example/src/button-shortcut/button-shortcut.py third_party/cvui/example/src/multiple-windows-complex/multiple-windows-complex.py src/Python/src/tf_util.py third_party/cvui/example/src/trackbar/trackbar.py third_party/cvui/example/src/ui-enhanced-window-component/EnhancedWindow.py third_party/cvui/example/src/hello-world/hello-world.py src/Python/src/ConvertH5.py third_party/cvui/example/src/ui-enhanced-window-component/ui-enhanced-window-component.py src/Python/src/FineTune.py third_party/cvui/example/src/multiple-windows/multiple-windows.py third_party/cvui/example/src/on-image/on-image.py third_party/cvui/example/src/multiple-files/Class1.py third_party/cvui/example/src/interaction-area/interaction-area.py third_party/cvui/python/package/cvui/__init__.py third_party/cvui/example/src/trackbar-sparkline/trackbar-sparkline.py pc_normalize parse_arguments pc_augment_to_point_num main convertToH5 convert_label_to_one_hot printout placeholder_inputs train calculatePairWiseDistanceEnergies get_loss get_last_layers get_loss_finetune get_transform_K get_transform get_model rotate_point_cloud load_h5_data_label_seg loadDataFile getDataFiles load_h5 rotate_point_cloud_by_angle shuffle_data jitter_point_cloud loadDataFile_with_seg batch_norm_template batch_norm_for_conv1d conv2d_transpose dropout fully_connected conv3d batch_norm_for_conv2d batch_norm_for_fc avg_pool2d conv2d conv1d avg_pool3d max_pool3d max_pool2d _variable_with_weight_decay batch_norm_for_conv3d _variable_on_cpu context endColumn window lastKeyPressed Mouse MouseButton Point counter image checkbox watch Label mouse button printf rect TrackbarParams sparkline Size imshow space iarea Context update Block beginColumn trackbar init main text Internal Render _handleMouse Rect beginRow endRow main main main group main main main main main main main Class1 Class2 main compact main window main main isWindowOpen openWindow closeWindow main main main main load main main main main main EnhancedWindow main array concatenate mean sqrt sum max list print len File close realpath dirname create_dataset open expand_dims array range append split add_argument ArgumentParser fname convertToH5 numclasses parse_arguments print write int32 float32 placeholder zeros range var exp divide repeat tile value fully_connected reshape conv2d max_pool2d value reshape fully_connected conv2d max_pool2d expand_dims dropout reshape fully_connected concat matmul conv2d tile max_pool2d expand_dims conv2d reshape value constant transpose matmul sparse_softmax_cross_entropy_with_logits reduce_mean eye argmax l2_loss zeros_like boolean_mask where argmax log l2_loss multiply squeeze transpose reduce_sum matmul cast expand_dims ones_like value subtract sparse_softmax_cross_entropy_with_logits softmax tile equal constant reshape reduce_mean eye Print arange shuffle len reshape cos pi dot shape uniform sin zeros array range reshape cos dot shape sin zeros array range shape clip randn File File multiply add_to_collection xavier_initializer _variable_on_cpu l2_loss truncated_normal_initializer range watch isString range setMouseCallback reset namedWindow Context update isString y isinstance print topBlock screen x y topBlock screen x y ndarray isinstance topBlock screen x y ndarray isinstance topBlock screen x y ndarray isinstance text isString topBlock screen x y ndarray isinstance topBlock screen x y ndarray isinstance TrackbarParams topBlock screen x y ndarray isinstance topBlock screen x y ndarray isinstance topBlock screen x y ndarray isinstance topBlock screen x begin y where topBlock x end begin y where topBlock x end Size updateLayoutFlow topBlock getContext error waitKey delayWaitKey reset range update uint8 button text imshow init zeros Canny trackbar window COLOR_BGR2GRAY COLOR_GRAY2BGR IMREAD_COLOR shape checkbox imread cvtColor rect y window text Point x group printf print rect y height width Rect iarea x VERSION counter mouse UP CLICK DOWN IS_DOWN Point abs str LEFT_BUTTON Class1 Class2 renderInfo renderMessage update context uint8 button printf print imshow zeros context uint8 button printf print imshow zeros compact context endColumn image watch sparkline uniform space append TRACKBAR_DISCRETE range beginColumn namedWindow beginRow endRow watch namedWindow waitKey destroyWindow closeWindow openWindow COLOR_HSV2BGR COLOR_BGR2HSV merge split readline print exit open float append split load TRACKBAR_HIDE_SEGMENT_LABELS EnhancedWindow begin end | # PointCloudAnnotationTool A tool written in C++ to help annotate point clouds. The idea is to have a desktop tool that can help annotate/segment point clouds in order to build a dataset for 3D computer vision problems. This is a first draft version of the tool, but it works (sort of). Demo of the tool: https://www.youtube.com/watch?v=lmQCoYmulUo ## Build and Installation Most of the tool is written in C++, but a part of workflow involves training a neural network, which is still in python. So, currently the build/install workflow is little hack-y. But, I hope to engineer the entire tool more elegantly, sometime in the future (collaborators welcome!). For now, here is what you need to do: ### Pre-requisites 1. You need to install PCL and OpenCV on your machine (both of these excellent libraries have their own platform specific installtion guides, so I would recommend checking those out.) 2. Create a virtual environment and install Tensorflow on it. | 3,649 |
sidneyp/skull-stripper | ['data augmentation'] | ['A deep learning based tool for automatic brain extraction from functional magnetic resonance images in rodents'] | train.py run.py logger.py dataset.py elastic_transform.py utils.py loss.py to_img tensor_2_numpy_image load_nifty MouseMRIDS cvt1to3channels load_dataset get_elastic_transforms elastic_transform show_result draw_grid Logger DiceLoss main normalize_image main gray2rgb resize_sample outline pad_sample crop_sample normalize_volume log_images load get_fdata squeeze moveaxis uint8 tensor_2_numpy_image astype join uint8 format basename int glob sort load_nifty min len split append loadmat array range enumerate warpAffine RandomState getAffineTransform arange min astype float32 rand shape meshgrid gaussian_filter line range elastic_transform concatenate get_elastic_transforms imshow figure image unsqueeze cuda fromarray str normalize_image result strftime load_state_dict cvt1to3channels normalize imsave range Compose astype load join uint8 print weights loadmat numpy makedirs log_loss_summary batch_size model mask_path zero_grad log_recall_summary DataLoader log_prediction_summary DiceLoss Logger resize log_f1score_summary save max StepLR step Adam validation_portion shape permute load_dataset append state_dict log_accuracy_summary format inf loss_f mean src_path lr item is_available BCELoss backward accuracy_f parameters MouseMRIDS cpu epochs min max nonzero pad min max shape resize percentile rescale_intensity mean std gray2rgb squeeze outline append numpy range shape empty abs max round nonzero zip | # skull-stripper Segmentation of mouse brain fMRI ## Depedencies * Python 3.6.8 * Torch 1.3.1 * Torchvision 0.4.2 * Tensorboard 1.14.0 * TensorboardX 1.4 * Numpy 1.17.2 * Matplotlib 3.1.1 | 3,650 |
sigmaquadro/VolatilityEstimator | ['time series'] | ['Jumping VaR: Order Statistics Volatility Estimator for Jumps Classification and Market Risk Modeling'] | volatility_estimator_functions.py plot_local_vol_order_stat plot_local_vol _kSmallestCDF local_volatility local_vol_order_stats _thrLocalVol _getkJumpProb zeros std arange legend figure plot deepcopy astype _thrLocalVol double range _getkJumpProb int arange _kSmallestCDF astype argsort double zeros sum sqrt arange cdf betainc double arange plot sort min astype figure legend cdf sum std | sigmaquadro/VolatilityEstimator | 3,651 |
sigsep/open-unmix-pytorch | ['music source separation'] | ['Open-Unmix - A Reference Implementation for Music Source Separation'] | hubconf.py openunmix/data.py tests/test_model.py scripts/train.py tests/test_augmentations.py openunmix/evaluate.py openunmix/__init__.py tests/test_io.py tests/test_transforms.py tests/test_utils.py tests/test_wiener.py tests/test_datasets.py openunmix/cli.py openunmix/transforms.py tests/test_regression.py tests/test_jit.py openunmix/utils.py tests/create_vectors.py openunmix/model.py setup.py openunmix/predict.py openunmix/filtering.py separate _augment_channelswap load_datasets SourceFolderDataset Compose _augment_force_stereo _augment_gain MUSDBDataset VariableSourcesTrackFolderDataset UnmixDataset AlignedDataset FixedSourcesTrackFolderDataset aug_from_str load_audio load_info separate_and_evaluate _mul _inv wiener atan2 _invert _norm _mul_add _conj _covariance expectation_maximization OpenUnmix Separator separate make_filterbanks AsteroidISTFT TorchISTFT AsteroidSTFT TorchSTFT ComplexNorm bandwidth_to_max_bin load_separator EarlyStopping AverageMeter load_target_models preprocess save_checkpoint umxse_spec umxse umxhq_spec umxhq umxl umx_spec umx umxl_spec main train valid get_statistics main test_forcestereo test_channelswap audio nb_timesteps test_gain nb_channels test_sourcefolder test_trackfolder_var test_trackfolder_fix test_musdb torch_backend dur test_loadwav info torch_backend test_onnx TestModels hidden_size test_model_loading test_shape nb_channels nb_bins nb_frames nb_samples spectrogram unidirectional test_estimate_and_evaluate test_spectrogram mus method nfft audio nb_timesteps nb_channels test_stft hop nb_samples method test_average_meter test_early_stopping dtype test_dtype residual target nb_channels iterations test_wiener nb_sources nb_bins nb_frames mix softmask write_stems model set_audio_backend outdir audio_backend verbose read_stems Path ArgumentParser device save tensor str list load_separator stem input freeze parse_args to mkdir ext items T with_suffix print add_argument tqdm load_audio str num_channels sample_rate num_frames info load int load_info rand repeat_interleave source_augmentations SourceFolderDataset target_dir stem add_argument Compose VariableSourcesTrackFolderDataset AlignedDataset FixedSourcesTrackFolderDataset aug_from_str parse_args MUSDBDataset as_tensor T load_separator rate sample_rate audio eval_mus_track separator preprocess freeze to to_dict save_estimates tensor atan asin Size zeros Size zeros zeros_like _norm zeros_like _mul _inv empty_like arange zeros_like _invert _covariance tensor abs cartesian_prod expand sum range cat mean sqrt int requires_grad min clone _mul_add as_tensor cos shape sin zeros as_tensor max cat cartesian_prod arange _conj _mul_add zeros sample_rate separator preprocess to_dict Parameter AsteroidISTFT TorchISTFT hann_window AsteroidSTFT TorchSTFT from_torch_args linspace join save load isinstance glob to print getvalue getattr load_state_dict expanduser OpenUnmix next StringIO load_target_models hub_loader getattr expanduser to exists to transpose repeat_interleave warn shape device resampler as_tensor bandwidth_to_max_bin to eval load_state_dict load_state_dict_from_url OpenUnmix to umxse_spec bandwidth_to_max_bin to eval load_state_dict load_state_dict_from_url OpenUnmix umxhq_spec to bandwidth_to_max_bin to eval load_state_dict load_state_dict_from_url OpenUnmix umx_spec to bandwidth_to_max_bin to eval load_state_dict load_state_dict_from_url OpenUnmix to umxl_spec update backward size AverageMeter zero_grad tqdm set_description unmix set_postfix item encoder step mse_loss eval AverageMeter deepcopy list scale_ isinstance SourceFolderDataset squeeze partial_fit maximum tqdm set_description permute to StandardScaler max range len valid model set_audio_backend target audio_backend DataLoader ReduceLROnPlateau set_description Path ArgumentParser device abspath save_checkpoint seed make_filterbanks step Adam parse_known_args dirname load_state_dict set_postfix append expanduser to nfft bandwidth_to_max_bin mkdir manual_seed trange checkpoint load join time bandwidth load_datasets get_statistics print sample_rate add_argument EarlyStopping output parameters Repo train epochs T STFT stft DB spec Spectrogram save tensor _augment_gain _augment_channelswap _augment_force_stereo MUSDBDataset set_audio_backend FixedSourcesTrackFolderDataset VariableSourcesTrackFolderDataset set_audio_backend len set_audio_backend range SourceFolderDataset set_audio_backend param set_audio_backend load_audio separator to rand export eval OpenUnmix unmix rand model_fn model json index loads array separate_and_evaluate load make_filterbanks rate Sequential audio preprocess permute as_tensor ComplexNorm stft make_filterbanks istft detach update AverageMeter EarlyStopping step wiener to wiener | # _Open-Unmix_ for PyTorch [](https://joss.theoj.org/papers/571753bc54c5d6dd36382c3d801de41d) [](https://colab.research.google.com/drive/1mijF0zGWxN-KaxTnd0q6hayAlrID5fEQ) [](https://paperswithcode.com/sota/music-source-separation-on-musdb18?p=open-unmix-a-reference-implementation-for) [](https://github.com/sigsep/open-unmix-pytorch/actions?query=workflow%3ACI+branch%3Amaster+event%3Apush) [](https://pypi.python.org/pypi/openunmix) [](https://pypi.python.org/pypi/openunmix) This repository contains the PyTorch (1.8+) implementation of __Open-Unmix__, a deep neural network reference implementation for music source separation, applicable for researchers, audio engineers and artists. __Open-Unmix__ provides ready-to-use models that allow users to separate pop music into four stems: __vocals__, __drums__, __bass__ and the remaining __other__ instruments. The models were pre-trained on the freely available [MUSDB18](https://sigsep.github.io/datasets/musdb.html) dataset. See details at [apply pre-trained model](#getting-started). ## ⭐️ News - 03/07/2021: We added `umxl`, a model that was trained on extra data which significantly improves the performance, especially generalization. | 3,652 |
silverbulletmdc/PVEN | ['vehicle re identification'] | ['Parsing-based View-aware Embedding Network for Vehicle Re-Identification'] | vehicle_reid_pytorch/data/__init__.py vehicle_reid_pytorch/data/samplers/triplet_sampler.py examples/parsing_reid/model.py vehicle_reid_pytorch/data/transforms/pad_to_mul.py examples/parsing/veri776_poly2mask.py vehicle_reid_pytorch/utils/__init__.py vehicle_reid_pytorch/models/backbones/senet.py vehicle_reid_pytorch/metrics/rerank.py vehicle_reid_pytorch/data/transforms/__init__.py vehicle_reid_pytorch/models/blocks.py vehicle_reid_pytorch/loss/tuplet_loss.py examples/parsing_reid/math_tools.py examples/parsing/dataset.py vehicle_reid_pytorch/utils/iotools.py vehicle_reid_pytorch/models/aaver.py vehicle_reid_pytorch/models/__init__.py vehicle_reid_pytorch/metrics/eval_reid.py examples/parsing_reid/main.py examples/parsing/generate_masks.py vehicle_reid_pytorch/utils/visualize.py vehicle_reid_pytorch/models/ram.py examples/preprocess_data/generate_pkl.py vehicle_reid_pytorch/utils/pytorch_tools.py vehicle_reid_pytorch/utils/tools.py vehicle_reid_pytorch/metrics/R1_mAP.py vehicle_reid_pytorch/data/datasets/bases.py vehicle_reid_pytorch/data/transforms/random_erasing.py vehicle_reid_pytorch/data/datasets/__init__.py vehicle_reid_pytorch/loss/triplet_loss.py vehicle_reid_pytorch/loss/__init__.py examples/preprocess_data/preprocess_veriwild2.py vehicle_reid_pytorch/loss/test_tuplet_loss.py vehicle_reid_pytorch/models/backbones/resnet.py vehicle_reid_pytorch/utils/math.py vehicle_reid_pytorch/models/baseline.py vehicle_reid_pytorch/data/transforms/resize_with_kp.py vehicle_reid_pytorch/utils/path.py vehicle_reid_pytorch/loss/center_loss.py vehicle_reid_pytorch/data/datasets/common.py vehicle_reid_pytorch/data/samplers/__init__.py vehicle_reid_pytorch/metrics/__init__.py vehicle_reid_pytorch/models/backbones/resnet_ibn.py setup.py vehicle_reid_pytorch/data/demo_transforms.py examples/parsing/train_parsing.py VehicleReIDParsingDataset get_training_albumentations get_validation_augmentation get_preprocessing pad_image_to_multiplys_of to_tensor VeRi3kParsingDataset predict BCEDiceLoss get_metas_dirty poly2mask clk eval_ build_model make_config eval eval_vehicle_id_ train drop_linear clck_dist Clck_R1_mAP ParsingTripletLoss ParsingReidModel build_model main veri776 vehicleid veriwild veriwild2 get_training_albumentations get_preprocessing to_tensor get_validation_augmentations make_basic_dataset relabel ReIDDataset ReIDMetaDataset get_imagedata_info CommonReIDDataset RandomIdentitySampler SimilarIdentitySampler test_similarity_sampler KPSampler AlbuPadImageToMultipliesOf pad_image_to_shape RandomErasing AlbuRandomErasing ResizeWithKp MultiScale CenterLoss test_tuplet_loss test_generate_tuplets hard_example_mining euclidean_dist CrossEntropyLabelSmooth TripletLoss normalize generate_tuplets _tuplet_loss TupletLoss worker eval_func eval_func_mp get_expectation_of_AP calc_AP eval_func_th build_metric R1_mAP CMC10Times re_ranking AAVER weights_init_classifier weights_init_kaiming Baseline conv_block skip_layer Residual Identity RAM ResNet conv3x3 BasicBlock Bottleneck resnet152_ibn_a resnet50_ibn_a Bottleneck_IBN ResNet_IBN IBN resnet101_ibn_a SENet SEResNetBottleneck SEBottleneck SEResNeXtBottleneck Bottleneck SEModule load_checkpoint get_host_ip save_checkpoint merge_configs read_any_img read_rgb_image euclidean_dist AverageMeter near_convex pad_image_size_to_multiples_of perspective_transform mkdir_p WarmupMultiStepLR featuremap_perspective_transform make_optimizer make_warmup_scheduler iter_x setup_logger tb_log flat_cfg TimeCounter Session _flat_cfg render_mask_to_img render_bboxes_to_img generate_html_table reid_html_table time_it visualize_img visualize_reid get_heatmap render_keypoints_to_img str uint8 list imwrite astype tqdm unsqueeze Path mkdir_p round range len int fillPoly astype zip zeros array CfgNode last_stride name neck pretrain_choice pretrain_path neck_feat ParsingReidModel warmup_method num_query_imgs batch_size model make_config triplet_loss zero_grad SGD DataLoader weight_decay DataParallel save_checkpoint output_dir device warmup_iters logfile base_lr make_optimizer loglevel list name milestones train_size step len epochs tuplet_loss pad getattr to valid_size range SummaryWriter eval_ debug momentum upper center_lr mean CrossEntropyLabelSmooth make_warmup_scheduler mkdir_p info item pkl_path gamma tuplet_s center_loss enumerate ParsingTripletLoss id_epsilon time items isinstance add_scalar backward load_checkpoint bias_lr_factor pt_loss num_train_ids parameters meta_dataset num_instances TripletLoss TupletLoss merge_configs tuplet_beta Tensor warmup_factor make_basic_dataset num_query_imgs make_config DataParallel DataLoader output_dir device list train_size pad load_state_dict append to valid_size eval_ info model_path pkl_path load items makedirs merge_configs eval_vehicle_id_ make_basic_dataset compute print eval reset save info reid_html_table Clck_R1_mAP compute resplit_for_vehicleid mean eval info append range Clck_R1_mAP load list keys save shape mm range view pretrain_model str name absolute groups abspath append iterdir compile makedirs items list makedirs abspath append split items list makedirs abspath append to_dict read_csv split split transpose gallery get_training_albumentations query ReIDDataset CommonReIDDataset get_preprocessing train get_validation_augmentations append len set int list sorted copy add set list print RandomIdentitySampler SimilarIdentitySampler rand zip range len tuple list zeros extend partial RandomErasing print generate_tuplets print tuplet_loss TupletLoss randn expand_as new expand t sqrt cpu cuda addmm_ data ne ones_like zeros_like view size min squeeze expand t eq gather max arange stack append randint tensor range view cos shape sum acos invert list format asarray print cumsum astype float32 tqdm argsort shape mean append bool sum range time format print astype float32 tqdm mean shape imap append sum Pool invert astype argsort calc_AP bool invert list format asarray print cumsum astype float32 tqdm argsort shape mean append bool sum range cumsum sum asarray arange print choice mean append zeros range R1_mAP CMC10Times zeros_like float16 max exp transpose append to sum range cat concatenate size astype mean unique addmm_ minimum print t int32 zeros numpy len affine bias kaiming_normal_ weight __name__ constant_ bias normal_ weight __name__ constant_ ResNet_IBN load_url load_state_dict ResNet_IBN load_url load_state_dict ResNet_IBN load_url load_state_dict read_rgb_image imread convert cvtColor COLOR_BGR2RGB load items list isinstance glob map keys load_state_dict append Tensor to max values items list save module state_dict merge_from_file merge_from_list split SOCK_DGRAM socket connect AF_INET view arccos cross empty any sum clip warpPerspective astype float32 getPerspectiveTransform zeros list map makedirs named_parameters WarmupMultiStepLR arange device getPerspectiveTransform view identity from_numpy shape new_tensor permute meshgrid append to ones_like grid_sample zip float bmm repeat numpy setFormatter join getLogger addHandler StreamHandler upper Formatter getattr setLevel FileHandler items list isinstance debug item Tensor add_scalar items list isinstance iter_x isinstance _flat_cfg show subplot items yticks imshow title figure ceil xticks enumerate len uint8 COLOR_BGR2RGB reshape applyColorMap min astype resize COLORMAP_JET sum max cvtColor subplot yticks Compose subplots_adjust add_patch imshow title figure Rectangle xticks range len array range copy circle copy rectangle copy list write append keys enumerate argsort calc_AP generate_html_table abspath append array range enumerate | silverbulletmdc/PVEN | 3,653 |
silversparro/wav2letter.pytorch | ['speech recognition'] | ['Wav2Letter: an End-to-End ConvNet-based Speech Recognition System'] | train.py data/an4.py data/__init__.py multiproc.py data/data_loader.py test.py data/librispeech.py noise_inject.py decoder.py data/distributed.py data/ted.py data/voxforge.py transcribe.py data/common_voice.py data/utils.py model.py data/merge_manifests.py GreedyDecoder Decoder BeamCTCDecoder InferenceBatchSoftmax stft Cov1dBlock _get_stft_kernels WaveToLetter PCEN cerCalc AverageMeter werCalc poly_lr_scheduler to_np decode_results _convert_audio_to_wav _format_data _format_files main _process_transcript main convert_to_wav AudioParser pcen2 get_audio_length load_randomly_augmented_audio NoiseInjection AudioDataLoader _collate_fn SpectrogramParser SpectrogramDataset normalize_tf_data split_normalize_with_librosa load_audioW2l2 BucketingSampler DistributedBucketingSampler audio_with_sox augment_audio_with_sox load_audio DistributedDataParallel main _preprocess_transcript _process_file get_utterances_from_stm prepare_dir cut_utterance filter_short_utterances _preprocess_transcript main order_and_prune_files create_manifest _get_recordings_dir prepare_sample Parameter int fromfunction ones float list len set dict zip range split float update top_paths min get_meta offsets append range len target_dir _convert_audio_to_wav _format_files makedirs upper remove print target_dir extractall create_manifest max_duration min_duration _format_data rmtree open download makedirs join format print dirname makedirs join basename format tar_path close split convert_to_wav mean squeeze read astype int16 read astype lfilter exp zeros_like print split normalize enumerate FloatTensor extend copy_ shape pad IntTensor append zeros float range len check_output uniform augment_audio_with_sox call join replace split exists list walk _process_file items call join get_utterances_from_stm format list str cut_utterance tqdm filter listdir enumerate makedirs prepare_dir print order_and_prune_files print sort join exists join read close set Request urlopen makedirs | silversparro/wav2letter.pytorch | 3,654 |
simonlousky/alteredAugmentor | ['data augmentation', 'image augmentation'] | ['Augmentor: An Image Augmentation Library for Machine Learning'] | Augmentor/ImageSource.py tests/test_ground_truth_by_class.py tests/test_multi_threading.py tests/test_datapipeline.py tests/util_funcs.py tests/test_pipeline_add_operations.py tests/test_rotate.py tests/test_generators.py tests/test_user_operation_parameter_input.py docs/conf.py tests/test_load.py Augmentor/Pipeline.py tests/test_ground_truth_augmentation.py Augmentor/Operations.py tests/test_resize.py tests/test_torch_transform.py tests/test_gaussian.py Augmentor/__init__.py tests/test_random_color_brightness_contrast.py tests/test_distortion.py Augmentor/ImageUtilities.py setup.py ImageSource parse_user_parameter extract_paths_and_extensions scan_directory scan scan_directory_with_classes AugmentorImage scan_dataframe Rotate RandomColor Custom Zoom RandomBrightness RandomErasing CropRandom ZoomRandom Greyscale RotateRange Invert HistogramEqualisation CropPercentage Operation Skew Resize HSVShifting RotateStandard Crop ZoomGroundTruth Shear Flip GaussianDistortion RandomContrast Scale Distort BlackAndWhite DataPipeline Pipeline DataFramePipeline test_sample_with_masks test_sample_with_no_masks test_in_memory_distortions test_add_gaussian_to_pipeline test_create_gaussian_distortion_object test_image_generator_function test_generator_with_array_data test_generator test_generator_image_scan test_keras_generator_from_disk test_zoom_ground_truth_temporary_class_without_ground_truth_images test_loading_ground_truth_images test_zoom_ground_truth_temporary_class test_skew_ground_truth test_distort_gaussian_ground_truth test_rotate_ground_truth test_black_and_white_ground_truth test_crop_percentage_ground_truth test_flip_ground_truth test_random_erasing_ground_truth test_greyscale_operation_ground_truth test_scale_ground_truth test_distort_ground_truth test_hsv_shift_ground_truth create_temporary_data test_invert_operation_ground_truth test_shear_ground_truth test_rotate_ground_truth_multiple_passes test_crop_ground_truth test_zoom_ground_truth test_histogram_equalisation_ground_truth test_zoom_random_ground_truth test_flip_ground_truth_multiple_passes test_rotate_range_ground_truth test_rotate_standard_ground_truth destroy_temporary_data test_crop_random_ground_truth test_dataframe_initialise_with_ten_images test_initialise_with_no_parameters test_initialise_with_missing_folder test_initialise_with_ten_images test_class_image_scan test_initialise_with_nondefault_output_directory test_initialise_with_subfolders test_initialise_with_empty_folder test_simple_multi_threading_example test_all_operations_multi_thread test_multi_threading_override test_add_rotate_operation test_random_contrast_in_memory test_random_color_in_memory test_random_brightness_in_memory test_resize_save_to_disk test_resize_in_memory test_rotate_images_270 test_rotate_images_180 rotate_images test_rotate_images_custom_temp_files test_rotate_images_90 test_torch_transform test_user_param_parsing create_colour_temp_image create_greyscale_temp_image create_sub_folders Real isinstance dirname splitext join sorted basename isdir glob scan_directory AugmentorImage abspath append zeros list categories get_values codes len AugmentorImage Categorical abspath zip append zeros values enumerate glob join extend abspath join isdir glob scan_directory warn append BytesIO new mkdtemp write close getvalue NamedTemporaryFile rotate sample rmtree save DataPipeline append range flush len save DataPipeline fromarray list new mkdtemp getvalue NamedTemporaryFile rotate append range glob close zip sample flush join BytesIO print write rmtree zoom_random len create_colour_temp_image close Distort rmtree perform_operation create_greyscale_temp_image append open GaussianDistortion gaussian_distortion mkdtemp Pipeline save new mkdtemp getvalue NamedTemporaryFile rotate append next range close flip_left_right Pipeline flush image_generator BytesIO write rmtree flip_top_bottom len save new mkdtemp getvalue NamedTemporaryFile rotate append next range close flip_left_right Pipeline flush BytesIO write rmtree flip_top_bottom keras_generator randint len fromarray reshape astype keras_generator_from_array range rotate randint next Pipeline zeros len BytesIO new mkdtemp write close getvalue NamedTemporaryFile rmtree keras_generator save append augmentor_images next Pipeline range flush len fromarray join uint8 name rand mkdtemp close choice NamedTemporaryFile rmtree keras_generator save append randint next Pipeline range fromarray join uint8 augmentor_images rand mkdtemp rmtree abspath save append randint ground_truth Pipeline range fromarray join uint8 add_operation glob rand mkdtemp sample ZoomGroundTruth rmtree abspath save append randint Pipeline range fromarray join uint8 add_operation glob rand mkdtemp sample ZoomGroundTruth rmtree abspath save append randint ground_truth Pipeline range fromarray join uint8 rand mkdtemp abspath save append randint range rmtree fromarray join uint8 glob rand mkdtemp sample rmtree histogram_equalisation abspath save append randint ground_truth Pipeline range fromarray join uint8 greyscale glob rand mkdtemp sample rmtree histogram_equalisation abspath save append randint ground_truth Pipeline range fromarray join uint8 invert greyscale glob rand mkdtemp sample rmtree histogram_equalisation abspath save append randint ground_truth Pipeline range fromarray join uint8 greyscale glob rand mkdtemp sample rmtree abspath save append randint ground_truth Pipeline range black_and_white fromarray join uint8 skew greyscale glob rand mkdtemp sample rmtree abspath save append randint ground_truth Pipeline range fromarray join uint8 greyscale glob rand mkdtemp sample rotate_without_crop rmtree abspath save append randint ground_truth Pipeline range join rotate90 glob destroy_temporary_data ground_truth sample create_temporary_data Pipeline join rotate180 rotate90 glob rotate_random_90 destroy_temporary_data ground_truth rotate270 sample create_temporary_data Pipeline join glob rotate destroy_temporary_data ground_truth sample create_temporary_data Pipeline join glob destroy_temporary_data ground_truth sample flip_left_right create_temporary_data Pipeline join glob flip_random destroy_temporary_data flip_top_bottom ground_truth sample flip_left_right create_temporary_data Pipeline crop_by_size join glob destroy_temporary_data ground_truth sample create_temporary_data Pipeline join glob destroy_temporary_data ground_truth crop_centre sample create_temporary_data Pipeline crop_random join CropRandom add_operation glob destroy_temporary_data ground_truth sample create_temporary_data Pipeline shear join glob destroy_temporary_data ground_truth sample create_temporary_data Pipeline join glob destroy_temporary_data ground_truth scale sample create_temporary_data Pipeline join glob destroy_temporary_data ground_truth random_distortion sample create_temporary_data Pipeline join glob destroy_temporary_data ground_truth sample gaussian_distortion create_temporary_data Pipeline join zoom glob destroy_temporary_data ground_truth sample create_temporary_data Pipeline join glob destroy_temporary_data ground_truth sample zoom_random create_temporary_data Pipeline join add_operation glob sample destroy_temporary_data ground_truth HSVShifting create_temporary_data Pipeline join glob destroy_temporary_data ground_truth random_erasing sample create_temporary_data Pipeline Pipeline mkdtemp Pipeline mkdtemp Pipeline fromarray join uint8 list create_sub_folders isdir name glob rand mkdtemp close NamedTemporaryFile scan_directory_with_classes rmtree save append keys values BytesIO new len mkdtemp write close getvalue NamedTemporaryFile image_path rmtree save append Pipeline range flush open importorskip save DataFrame open DataFramePipeline new mkdtemp getvalue NamedTemporaryFile image_path append range close flush BytesIO write dict rmtree len fromarray join uint8 name rand mkdtemp len close choice NamedTemporaryFile rmtree mkdir save append randint range run join name glob new mkdtemp close sample NamedTemporaryFile rmtree open save resize append Pipeline range len save resize name new mkdtemp rotate NamedTemporaryFile append range glob close sample flip_left_right Pipeline join flip_random rmtree flip_top_bottom len join name glob new mkdtemp close sample NamedTemporaryFile rmtree open save resize append Pipeline range len Pipeline rotate create_colour_temp_image RandomColor close rmtree perform_operation create_greyscale_temp_image append open create_colour_temp_image close RandomContrast rmtree perform_operation create_greyscale_temp_image append open create_colour_temp_image close rmtree perform_operation create_greyscale_temp_image append RandomBrightness open name new mkdtemp close NamedTemporaryFile Resize perform_operation save join name glob new mkdtemp close sample NamedTemporaryFile rmtree open save resize append Pipeline range len join Rotate str new perform_operation save rotate_images rotate_images rotate_images Rotate name new mkdtemp close NamedTemporaryFile rmtree perform_operation save fromarray greyscale uint8 zoom rotate_random_90 Compose importorskip zeros Pipeline operations parse_user_parameter fromarray uint8 name rand mkdtemp NamedTemporaryFile save fromarray uint8 name rand mkdtemp NamedTemporaryFile save fromarray uint8 name rand mkdtemp NamedTemporaryFile save append range |  Augmentor is an image augmentation library in Python for machine learning. It aims to be a standalone library that is platform and framework independent, which is more convenient, allows for finer grained control over augmentation, and implements the most real-world relevant augmentation techniques. It employs a stochastic approach using building blocks that allow for operations to be pieced together in a pipeline. [](https://pypi.python.org/pypi/Augmentor) [](https://pypi.python.org/pypi/Augmentor) [](https://augmentor.readthedocs.io/en/master/?badge=master) [](https://travis-ci.org/mdbloice/Augmentor) [](LICENSE.md) [](http://www.repostatus.org/#active) [](https://mybinder.org/v2/gh/4QuantOSS/Augmentor/master) ## Installation | 3,655 |
sinaahmadi/wergor | ['transliteration'] | ['A Rule-based Kurdish Text Transliteration System'] | Wergor.py Wergor | # Wergor transliterator # Transliteration system for Kurdish [Wergor](https://github.com/sinaahmadi/wergor) is a transliteration system for Sorani Kurdish Latin-based and Arabic-based orthographies. In this first version, we have used a rule-based method. It is the result of a research project published in the ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) and can be downloaded here: [https://dl.acm.org/citation.cfm?id=3278623](https://dl.acm.org/citation.cfm?id=3278623) ### Usage The script can be used in command-line or directly by importing the `Wergor class` in your codes. #### Command-line usage ~~~ python Wergor.py -<mode> <input file>.txt ~~~ Currently `-arabic2latin` and `-latin2arabic` are the only defined modes. The input file should be in `.txt` format. By running the script, the output file is automatically created in the same directory with "_transliterated" added to the name of the input file. | 3,656 |
sipposip/ensemble-neural-network-weather-forecasts | ['weather forecasting'] | ['Ensemble methods for neural network-based weather forecasts'] | era5_compute_normalization_weigths.py era5_ensemble_make_and_eval_forecasts_netens.py gefs_reforecast_eval.py era5_select_best_members.py analyze_and_plot_era5.py era5_ensemble_make_and_eval_forecasts_dropout.py download_era5_z500.py era5_ensemble_make_and_eval_forecasts.py train_era5_2.5deg_weynetal_batch.py gefs_plot.py era5_ensemble_precompute_jacobians_and_svecs.py savefig read_e5_data read_e5_data compute_mse create_pertubed_states_svd create_pertubed_states_rand PeriodicPadding read_e5_data PermaDropout make_dropout_model compute_mse PeriodicPadding read_e5_data compute_mse PeriodicPadding read_e5_data net_jacobian compute_svs PeriodicPadding read_e5_data compute_mse PeriodicPadding build_model_weynetal2019 read_e5_data PeriodicPadding list squeeze concat transpose to_array append expand_dims range open_mfdataset load normal reshape append sum array range mean normal std Sequential PermaDropout layers add convert_to_tensor squeeze jacobian reshape net_jacobian svds Sequential Adam compile | # ensemble-neural-network-weather-forecasts This repository contains the code for our publication "Ensemble methods for neural network-based weather forecasts". download_era5_z500.py downloads the training data. era5_compute_normalization_weigths.py computes and saves normalization weights necessary for the training. train_era5_2.5deg_weynetal_batch.py trains the neural networks the scripts era5_ensemble_make_and_eval_forecasts_dropout.py era5_ensemble_make_and_eval_forecasts_netens.py era5_ensemble_make_and_eval_forecasts.py implement the different ensemble methods. they use the trained networks make ensemble forecasts with them, and evaluate the forecasts. note that what in the paper is called "multitrain" is called "netens" in the code. | 3,657 |
siqueira-hc/Efficient-Facial-Feature-Learning-with-Wide-Ensemble-based-Convolutional-Neural-Networks | ['facial expression recognition'] | ['Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks'] | model/utils/uimage.py main_esr9.py model/ml/fer.py main_affectnet_continuous.py main_ck_plus.py model/utils/udata.py model/utils/umath.py model/utils/ufile.py model/screen/fer_demo.py controller/cvision.py main_fer_plus.py controller/cvalidation.py model/ml/esr_9.py main_affectnet_discrete.py model/ml/grad_cam.py plot evaluate Branch main Base Ensemble plot evaluate Branch main Base Ensemble plot evaluate Branch main Base Ensemble main webcam image video plot evaluate Branch main Base Ensemble validate_webcam_mode_arguments validate_image_video_mode_arguments is_none _pre_process_input_image _dlib_face_detection _predict recognize_facial_expression detect_face _haar_cascade_face_detection _generate_saliency_maps ESR ConvolutionalBranch Base FER GradCAM FERDemo AffectNetCategorical _generate_single_file_name pre_process_affect_net FERplus AffectNetDimensional sort_numeric_directories CohnKanade create_file write_to_file close_file blur draw_horizontal_bar draw_image convert_rgb_to_bgr resize is_video_capture_open crop_rectangle draw_graph convert_grey_to_bgr get_frame convert_rgb_to_grey convert_bgr_to_grey read convert_bgr_to_rgb initialize_video_capture release_video_capture write draw_rectangle draw_text superimpose set_fps plot view extend get_ensemble_size val_criterion_eval sqrt val_model_eval stack device append tensor to range len join format save append array range len zero_grad get_ensemble_size SGD DataLoader to_device device save str view MSELoss to_state_dict append AffectNetDimensional range format plot eval add_param_group float net load join evaluate backward print makedirs reload train step len max enumerate add_branch StepLR CrossEntropyLoss AffectNetCategorical Ensemble array CohnKanade create_file str time update show print FERDemo get_frame write_to_file set_fps create_file update read show is_running FERDemo recognize_facial_expression close_file write_to_file quit update create_file show FERDemo get_frame write_to_file set_fps branch frames validate_webcam_mode_arguments image ArgumentParser cuda webcam_id video face_detection display input parse_args webcam size no_plot add_argument output gradcam validate_image_video_mode_arguments FERplus input is_none _dlib_face_detection astype _haar_cascade_face_detection resize convert_bgr_to_grey _generate_saliency_maps _pre_process_input_image _predict detect_face device to FER append _FACE_DETECTOR_DLIB cnn_face_detection_model_v1 enumerate detectMultiScale CascadeClassifier INPUT_IMAGE_SIZE fromarray unsqueeze resize _ESR_9 get_class concatenate mean ESR append zeros expand_dims argmax array clip GradCAM int join read int str _generate_single_file_name print write resize range read_csv len join format print makedirs write open format write range close VideoCapture release release int isOpened print retrieve grab range format print IMREAD_COLOR IMREAD_GRAYSCALE imread print join imwrite makedirs rectangle rectangle FILLED arange grid fromstring set_linestyle set_visible xticks yticks ylim set_color legend gca update get_position plot set_position close xlim minimum reshape tostring_rgb draw figure zeros array len putText FONT_HERSHEY_COMPLEX uint8 applyColorMap astype resize COLORMAP_JET numpy arange grid xticks yticks show ylabel title ylim savefig legend close xlim xlabel print fill_between makedirs | # Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks [](https://paperswithcode.com/sota/facial-expression-recognition-on-affectnet?p=efficient-facial-feature-learning-with-wide) [](https://paperswithcode.com/sota/facial-expression-recognition-on-fer?p=efficient-facial-feature-learning-with-wide) **The trained model cannot be used for commercial purposes!** This repository contains: - Facial expression recognition framework. - Introduction to Ensembles with Shared Representations. - Implementation of an Ensemble with Shared Representations in PyTorch. - Scripts of experiments conducted for the AAAI-2020 conference. - [Our AAAI-2020 paper](https://github.com/siqueira-hc/Efficient-Facial-Feature-Learning-with-Wide-Ensemble-based-Convolutional-Neural-Networks/blob/master/media/Siqueira-AAAI_2020.pdf). | 3,658 |
sisl/mechamodlearn | ['model based reinforcement learning'] | ['A General Framework for Structured Learning of Mechanical Systems'] | mechamodlearn/models.py mechamodlearn/nested.py mechamodlearn/rigidbody.py mechamodlearn/dataset.py mechamodlearn/nn.py mechamodlearn/transform.py mechamodlearn/viz_utils.py mechamodlearn/systems/mlacrobot.py mechamodlearn/metric_tracker.py setup.py mechamodlearn/utils.py mechamodlearn/odesolver.py mechamodlearn/systems/pendulum.py mechamodlearn/systems/__init__.py experiments/simple.py mechamodlearn/trainer.py mechamodlearn/logger.py get_long_description run train get_dataset ODEPredDataset ActuatedTrajectoryDataset dumpkvs add_figure HumanOutputFormat SeqWriter get_dir set_step Logger CSVOutputFormat logkvs make_output_format setup add_text scoped_setup JSONOutputFormat _get_time_str add_hist _MyFormatter logkv KVWriter add_image TensorboardOutputFormat MetricTracker PotentialNet SharedMMVEmbed GeneralizedForces ControlAffineLinearForce ViscousJointDampingForce ControlAffineForceNet GeneralizedForceNet CholeskyMMNet flatten_ map_ zip_ filter_ weights_init_mlp LNMLP init_normc_ Identity ActuatedODEWrapper _check_inputs _assert_increasing FixedGridODESolver RK4 Euler Midpoint odeint _decreasing rk4_alt_step_func AbstractRigidBody LearnedRigidBody move_optimizer_to_gpu OfflineTrainer compute_qvloss TrainerBase odepred_transform fill_windowed temp_require_grad peak_memory_mb diffangles bfill_diagonal time_series_norm set_rng_seed require_and_zero_grads wrap_to_pi Timer bfill_lowertriangle vizqvmodel plot_traj MultiLinkAcrobotMM MultiLinkAcrobot DampedMultiLinkAcrobot MultiLinkAcrobotV SimplePendulumMM ActuatedSimplePendulum SimplePendulumV ActuatedDampedPendulum SimplePendulum randn FromSystem requires_grad_ _udim len pop join locals format set_rng_seed _qdim Adam get_dataset now parameters OfflineTrainer Path save _udim LearnedRigidBody thetamask print train makedirs decode move strip warn Logger argv addHandler strftime getenv setFormatter format _get_time_str _MyFormatter gettempdir removeHandler info _handler FileHandler join rmtree makedirs items list logkv output_formats isinstance pop pop flatten_ impl isinstance pop flatten_ impl data init_normc_ __name__ fill_ normal_ tuple func _check_inputs integrate ActuatedODEWrapper is_tensor view size _qdim mean requires_grad_ odeint dict list isinstance param_groups keys Tensor cuda is_cuda append windowed enumerate unbind fill_windowed stack range len size tril_indices size min diag_indices ru_maxrss seed is_available manual_seed_all manual_seed dtype size pi fmod unsqueeze new_ones to requires_grad_ zero_ requires_grad_ require_and_zero_grads zip abs range format subplots plot set_xlabel set_ylabel legend get_cmap range size transpose | sisl/mechamodlearn | 3,659 |
sitzikbs/DeepFit | ['surface normals estimation'] | ['DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares'] | utils/cgal_normal_estimation.py models/DeepFit.py test_n_est.py dataset.py utils/plyfile.py train_n_est.py utils/pcpnet_dataset.py evaluate.py models/ThreeDmFVNet.py utils/nyu_test_all.py run_DeepFit_single_experiment.py utils/eulerangles.py utils/tf_util.py evaluate_curvatures.py tutorial/tutorial_utils.py utils/pc_util.py get_data.py tutorial/compute_normals.py test_c_est.py utils/normal_estimation_utils.py utils/provider.py utils/visualization.py SequentialPointcloudPatchSampler load_shape RandomPointcloudPatchSampler Shape PointcloudPatchDataset SequentialShapeRandomPointcloudPatchSampler Cache l2_norm log_string l2_norm map_curvatures1 log_string download_and_unzip get_pcpnet_point_clouds get_point_clouds_and_models_from_repo get_data_loaders parse_arguments get_target_features test_n_est get_data_loaders parse_arguments get_target_features test_n_est train_pcpnet get_data_loaders log_string parse_arguments get_target_features compute_loss get_model PointNet3DmFVEncoder QSTN PointNetEncoder STN solve_linear_system compute_principal_curvatures PointNetFeatures DeepFit fit_Wjet get_3DmFV_pytorch Inception3D ThreeDmFVNet get_3d_grid_gmm Global3DmFVFeature normal2rgb SyntheticPointCloudDataset compute_principal_curvatures SinglePointCloudDataset curvatures2rgb quat2euler euler2quat mat2euler angle_axis2euler euler2angle_axis euler2mat get_2d_grid_gmm fisher_vector_per_point cos_angle l2_normalize get_gmm compute_principal_curvatures fisher_vector batch_quat_to_rotmat get_fisher_vectors get_3DmFV get_3d_grid_gmm get_learned_gmm euclidean_to_spherical SequentialPointcloudPatchSampler load_shape RandomPointcloudPatchSampler Shape PointcloudPatchDataset SequentialShapeRandomPointcloudPatchSampler Cache write_ply pyplot_draw__comperative_point_clouds pyplot_draw_point_cloud draw_point_cloud read_ply point_cloud_to_volume point_cloud_isoview pyplot_draw_volume point_cloud_to_volume_batch point_cloud_three_views volume_to_point_cloud _open_stream _lookup_type PlyData _split_line PlyProperty PlyParseError make2d PlyListProperty PlyElement get_data_loader translate_point_cloud rotate_point_cloud scale_point_cloud occlude_point_cloud replace_labels insert_outliers_to_point_cloud getDataFiles starve_gaussians rotate_point_cloud_by_angle shuffle_data jitter_point_cloud rotate_x_point_cloud_by_angle get_3dmfv_n_est conv2d_transpose fully_connected conv3d max_pool3d batch_norm_template get_fv_tf_no_mvn conv2d conv1d _variable_with_weight_decay batch_norm_for_conv1d dropout batch_norm_for_conv2d get_3dmfv_sym get_fv_tf get_3dmfv_seg avg_pool3d max_pool2d _variable_on_cpu get_session get_3dmfv avg_pool2d batch_norm_for_fc batch_norm_for_conv3d plot_normals plot_parametric_plane visualize_3d_points export_four_views plot_plane_normal plot_parametric_jet load int setrecursionlimit cKDTree round max sqrt sum square print write flush min max zeros_like remove urlretrieve print extractall close ZipFile makedirs print join download_and_unzip system add_argument ArgumentParser models modelpostfix tuple device cuda seed str use_pca gpu_idx view squeeze len transpose savetxt load_state_dict to logdir parmpostfix get_data_loaders eval use_point_stn get_target_features manual_seed zeros flush enumerate load join print min patches_per_shape compute_principal_curvatures repeat split randint numpy makedirs SequentialPointcloudPatchSampler batchSize DataLoader PointcloudPatchDataset SequentialShapeRandomPointcloudPatchSampler append outputs index batchSize format exec_module module_from_spec spec_from_file_location batchSize refine model tuple zero_grad nepoch shape_names SGD MultiStepLR overwrite ReduceLROnPlateau numpy save device compute_loss exists open seed str gpu_idx name transpose len exit Adam RMSprop refine_epoch load_state_dict input to logdir next range state_dict SummaryWriter format add_histogram get_data_loaders log_string eval get_target_features manual_seed item train enumerate load join backward print add_scalar system rmtree parameters randint get_model step makedirs norm cos_angle view abs clamp squeeze min mean shape pow zeros sum acos log enumerate RandomPointcloudPatchSampler system cuda sum diag_embed matmul solve_linear_system mean shape unsqueeze repeat permute normalize abs cat int zeros_like size cholesky cholesky_solve range exp normalize abs pi sign from_numpy pow sqrt repeat unsqueeze permute device power to sum cat T ones_like ones float32 _compute_precision_cholesky array GaussianMixture prod expand_dims clip RectBivariateSpline around array ev append array cos sin eps asarray atan2 sqrt flat cos sin angle_axis2mat load str dump get_2d_grid_gmm isinstance ValueError print mkdir isfile open get_3d_grid_gmm get_learned_gmm len float64 GaussianMixture astype fit T ones_like ones _compute_precision_cholesky array GaussianMixture prod append fisher_vector range array T atleast_2d normalize squeeze absolute sign dot sqrt predict_proba means_ covariances_ tile weights_ power expand_dims ravel T atleast_2d sqrt predict_proba tile weights_ swapaxes power expand_dims norm exp concatenate abs transpose square pi sign sqrt tile power expand_dims sum array sqrt rad2deg arctan2 bmm mul size pow unsqueeze new_empty sum squeeze point_cloud_to_volume flatten append expand_dims range zeros float astype append vstack array range data read array write array describe int exp abs transpose min mean sqrt argsort round argwhere zeros sum max range euler2mat concatenate draw_point_cloud draw_point_cloud show set_xlabel set_xlim add_subplot scatter set_ylabel figure set_zlabel set_zlim set_ylim set_xlabel set_xlim add_subplot axis scatter set_zlim figure set_zlabel set_ylabel savefig set_ylim pyplot_draw_point_cloud volume_to_point_cloud append split dtype len property hasattr property property property arange shuffle len reshape cos pi dot shape uniform sin zeros array range expand_dims uniform tile reshape cos dot shape sin zeros array range reshape cos dot shape sin zeros array range reshape pi dot shape uniform zeros array range shape clip randn int list concatenate choice shape uniform ceil range int reshape KDTree delete choice query shape append round range argmin rand choice shape means_ tile weights_ append power expand_dims sum range len asarray arange SequentialPointcloudPatchSampler print RandomPointcloudPatchSampler index DataLoader PointcloudPatchDataset SequentialShapeRandomPointcloudPatchSampler append multiply add_to_collection xavier_initializer _variable_on_cpu l2_loss truncated_normal_initializer value l2_normalize prob multiply concat transpose reduce_sum sign flatten sqrt pow tile MultivariateNormalDiag expand_dims abs zeros_like l2_normalize concat pi where sign flatten abs exp multiply transpose reduce_sum cast expand_dims range value square sqrt tile float32 divide pow int32 value l2_normalize prob multiply abs reduce_max concat square reduce_sum sign transpose sqrt pow flatten tile MultivariateNormalDiag expand_dims reduce_min value l2_normalize prob multiply concat transpose reduce_sum sign flatten sqrt pow tile MultivariateNormalDiag expand_dims abs value exp l2_normalize multiply concat transpose square pi reduce_sum sign pow sqrt flatten tile expand_dims abs value l2_normalize prob multiply concat transpose reshape float32 reduce_sum sign flatten sqrt pow cast tile MultivariateNormalDiag expand_dims abs str ConfigProto Session clim add_subplot show set_xlabel colorbar scatter savefig gca ones_like set_label view_init set_xlim set_zlim auto_scale_xyz set_zlabel get_cmap set_axis_off set_ylabel figure set_ylim show T ones _edgecolors3d outer savefig linspace legend plot_surface _facecolors3d show int T format _facecolors3d ones roots transpose _edgecolors3d outer savefig linspace legend plot_surface max show T tolist square sqrt savefig quiver append sum show close savefig quiver set_axis_off draw view_init close savefig | ***DeepFit***: 3D Surface Fitting via Neural Network Weighted Least Squares (ECCV 2020 Oral) --- Created by [Yizhak Ben-Shabat (Itzik)](http://www.itzikbs.com) and [Stephen Gould](http://users.cecs.anu.edu.au/~sgould/) from [ANU](https://www.anu.edu.au/) and [ACRV](https://www.roboticvision.org/) . <div align="center"> <a href="https://www.itzikbs.com/" target="blank"> <img src="doc/ybenshabat.jpg" alt="Yizhak Ben-Shabat (Itzik)"> </a> <a href="https://cecs.anu.edu.au/people/stephen-gould/" target="blank"> <img src="doc/sgould.jpg" alt="Stephen Gould"> </a> | 3,660 |
sitzikbs/Nesti-Net | ['surface normals estimation'] | ['Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networks'] | utils/utils.py test_n_est_w_switching.py test_n_est.py models/experts_n_est.py models/ms_sw_n_est.py utils/plyfile.py train_n_est.py utils/pcpnet_dataset.py train_n_est_w_experts.py utils/evaluate.py test_n_est_w_experts.py train_n_est_w_switching.py utils/nyu_test_all.py utils/eulerangles.py utils/tf_util.py utils/pc_util.py models/ms_norm_est.py models/ss_norm_est.py utils/provider.py utils/visualization.py get_models_and_data.py download_and_unzip get_point_clouds_and_models_from_repo get_point_clouds get_trained_model printout predict printout predict printout predict get_learning_rate eval_one_epoch log_string train_one_epoch train get_bn_decay get_learning_rate eval_one_epoch log_string train_one_epoch train get_bn_decay get_learning_rate eval_one_epoch log_string train_one_epoch train get_bn_decay inception_module get_model get_loss placeholder_inputs noise_est_net get_loss inception_module normal_est_net placeholder_inputs get_model inception_module get_model get_loss placeholder_inputs quat2euler euler2quat mat2euler angle_axis2euler euler2angle_axis euler2mat l2_norm log_string SequentialPointcloudPatchSampler load_shape RandomPointcloudPatchSampler Shape PointcloudPatchDataset SequentialShapeRandomPointcloudPatchSampler Cache write_ply pyplot_draw__comperative_point_clouds pyplot_draw_point_cloud draw_point_cloud read_ply point_cloud_to_volume point_cloud_isoview pyplot_draw_volume point_cloud_to_volume_batch point_cloud_three_views volume_to_point_cloud _open_stream _lookup_type PlyData _split_line PlyProperty PlyParseError make2d PlyListProperty PlyElement get_data_loader translate_point_cloud getDataFiles rotate_point_cloud_by_angle jitter_point_cloud load_dataset scale_point_cloud load_h5_data_label_seg replace_labels starve_gaussians loadDataFile_with_seg rotate_x_point_cloud_by_angle rotate_point_cloud occlude_point_cloud loadDataFile insert_outliers_to_point_cloud load_h5 shuffle_data load_single_model load_single_model_class get_3dmfv_n_est conv2d_transpose fully_connected conv3d max_pool3d batch_norm_template get_fv_tf_no_mvn conv2d conv1d _variable_with_weight_decay batch_norm_for_conv1d dropout batch_norm_for_conv2d get_3dmfv_sym get_fv_tf get_3dmfv_seg avg_pool3d max_pool2d _variable_on_cpu get_session get_3dmfv avg_pool2d batch_norm_for_fc batch_norm_for_conv3d get_2d_grid_gmm fisher_vector_per_point l2_normalize get_gmm fisher_vector get_fisher_vectors get_3DmFV get_3d_grid_gmm get_learned_gmm euclidean_to_spherical make_segmentation_triplets_for_paper visualize_fv_with_pc draw_phi_teta_domain draw_gaussian_points normal2rgb visualize_derivatives draw_point_cloud visualize_pc_seg_diff visualize_fv visualize_pc_overlay visualize_pc_normals sphere discrete_cmap draw_line_segments axisEqual3D visualize_confusion_matrix main set_ax_props draw_normal_vector orthogonal_proj visualize_single_fv_with_pc draw_gaussians visualize_pc_seg visualize_pc visualize_fv_pc_clas visualize_pc_with_svd remove urlretrieve print extractall close ZipFile makedirs print join download_and_unzip print join download_and_unzip system print write flush join restore get_data_loader get_session concatenate print tuple min printout savetxt Saver run zeros enumerate flush open argmax transpose astype len print write flush exponential_decay maximum minimum exponential_decay print tuple len log_string add_summary float enumerate run arccos tuple reshape len log_string square shape_names rad2deg mean sqrt add_summary append float abs scalar enumerate run randn squeeze transpose reshape pi euler2mat shape dot zeros range argmax squeeze uint16 float32 placeholder len get_3dmfv_n_est int value inception_module dropout reshape concat transpose fully_connected squeeze max_pool3d power expand_dims round enumerate len int conv3d avg_pool3d concat minimum norm ValueError multiply divide reduce_sum square cross sqrt reduce_mean greater where pow tile expand_dims abs scalar noise_est_net normal_est_net where l2_normalize value inception_module reshape fully_connected squeeze max_pool3d value inception_module reshape fully_connected squeeze max_pool3d append array cos sin eps asarray atan2 sqrt flat cos sin angle_axis2mat sqrt sum square load int setrecursionlimit cKDTree round max squeeze point_cloud_to_volume flatten append expand_dims range zeros float astype append vstack array range data read array write array describe int exp abs transpose min mean sqrt argsort round argwhere zeros sum max range euler2mat concatenate draw_point_cloud draw_point_cloud show set_xlabel set_xlim add_subplot scatter set_ylabel figure set_zlabel set_zlim set_ylim set_xlabel set_xlim add_subplot axis scatter set_zlim figure set_zlabel set_ylabel savefig set_ylim pyplot_draw_point_cloud volume_to_point_cloud append split dtype len property hasattr property property property arange shuffle len reshape cos pi dot shape uniform sin zeros array range expand_dims uniform tile reshape cos dot shape sin zeros array range reshape cos dot shape sin zeros array range reshape pi dot shape uniform zeros array range shape clip randn int list concatenate choice shape uniform ceil range int reshape KDTree delete choice query shape append round range argmin rand choice shape means_ tile weights_ append power expand_dims sum range len int concatenate squeeze File maximum where choice replace_labels expand_dims round len asarray arange getDataFiles join loadDataFile join str isinstance squeeze loadDataFile where getDataFiles join loadDataFile getDataFiles range len File SequentialPointcloudPatchSampler print RandomPointcloudPatchSampler index DataLoader PointcloudPatchDataset SequentialShapeRandomPointcloudPatchSampler append multiply add_to_collection xavier_initializer _variable_on_cpu l2_loss truncated_normal_initializer value l2_normalize prob multiply concat transpose reduce_sum sign flatten sqrt pow tile MultivariateNormalDiag expand_dims abs zeros_like l2_normalize concat pi where sign flatten abs exp multiply transpose reduce_sum cast expand_dims range value square sqrt tile float32 divide pow int32 value l2_normalize prob multiply abs reduce_max concat square reduce_sum sign transpose sqrt pow flatten tile MultivariateNormalDiag expand_dims reduce_min value l2_normalize prob multiply concat transpose reduce_sum sign flatten sqrt pow tile MultivariateNormalDiag expand_dims abs value exp l2_normalize multiply concat transpose square pi reduce_sum sign pow sqrt flatten tile expand_dims abs value l2_normalize prob multiply concat transpose reshape float32 reduce_sum sign flatten sqrt pow cast tile MultivariateNormalDiag expand_dims abs str ConfigProto Session load str dump get_2d_grid_gmm isinstance ValueError print mkdir isfile open get_3d_grid_gmm get_learned_gmm len float64 GaussianMixture astype fit T ones_like ones _compute_precision_cholesky array GaussianMixture prod T ones_like ones _compute_precision_cholesky array GaussianMixture prod append fisher_vector range array T atleast_2d normalize squeeze absolute sign dot sqrt predict_proba means_ covariances_ tile weights_ power expand_dims ravel T atleast_2d sqrt predict_proba tile weights_ swapaxes power expand_dims norm exp concatenate abs transpose square pi sign sqrt tile power expand_dims sum array sqrt rad2deg arctan2 mean array zip abs max show set_xlabel set_xlim add_subplot scatter set_ylabel figure set_zlabel set_zlim set_ylim show set_xlabel set_xlim add_subplot set_ylabel set_zlim figure quiver set_zlabel set_ylim show set_cmap set_clim sphere view_init min add_subplot ScalarMappable sqrt to_rgba figure weights_ plot_surface max range set_ax_props len show set_cmap set_title set_clim sphere print draw_point_cloud add_subplot ScalarMappable sqrt to_rgba figure weights_ plot_surface set_ax_props len show subplots arange set_title set_yticklabels reshape set_yticks subplots_adjust set_window_title imshow set_xticks means_ figure savefig gca tick_params range len from_list view_init add_subplot axis pi close scatter savefig figure axisEqual3D rotate_x_point_cloud_by_angle len from_list view_init add_subplot axis pi close scatter savefig figure int32 axisEqual3D rotate_x_point_cloud_by_angle show set_cmap set_clim view_init add_subplot axis ScalarMappable pi close to_rgba scatter figure savefig axisEqual3D rotate_x_point_cloud_by_angle show join AxesGrid int sort axis imshow title savefig figure mkdir open imread round enumerate len show set_bad set_title axes rainbow set_window_title imshow set_visible point_cloud_isoview figure savefig masked_where subplots arange set_bad set_yticklabels rainbow set_visible point_cloud_isoview tick_params str set_title set_window_title imshow savefig wm_geometry range masked_where set_yticks set_xticks len arange set_bad set_yticklabels rainbow axis set_visible point_cloud_isoview tick_params show set_title set_window_title imshow savefig masked_where axes set_yticks set_xticks figure len arange xticks max yticks show list set_title ylabel imshow savefig gca range product astype tight_layout xlabel text confusion_matrix figure len cos pi sin set_xlabel set_xlim set_ylabel set_zlim set_zlabel set_ylim show view_init add_subplot figure draw_gaussian_points set_ax_props join str constant get_fv_minmax get_session get_grid_gmm close getDataFiles visualize_pc means_ covariances_ visualize_fv weights_ expand_dims load_single_model_class show pyplot_draw_point_cloud get_session pc_svd float32 pi placeholder rotate_point_cloud_by_angle load_single_model expand_dims run divide square sqrt tile expand_dims sum show normal2rgb view_init add_subplot axis pi close scatter savefig figure axisEqual3D rotate_x_point_cloud_by_angle enumerate show set_title axes set_xlabel set_xlim close add_artist scatter set_ylabel figure legend savefig append Patch range set_ylim show axes figtext add_collection stack savefig figure append abs array LineCollection visualize_derivatives fisher_vector_per_point helper_struct get_grid_gmm get_gaussian_points dirname abspath load_single_model append str name linspace base get_cmap | sitzikbs/Nesti-Net | 3,661 |
sjauhri/Interactive-Learning-in-State-space | ['imitation learning'] | ['Interactive Imitation Learning in State-Space'] | gym_modifications/classic_control/__init__.py DCOACH/models/feedback.py DCOACH/models/dcoach_lunarlander.py TIPS/models/feedback.py DCOACH/models/dcoach.py gym_modifications/classic_control/continuous_cartpole.py TIPS/models/fdm_lunarl.py gym_modifications/lunar_lander.py TIPS/models/fdm_cartpole.py gym_modifications/__init__.py DCOACH/models/feedback_ext.py TIPS/models/tips.py TIPS/models/tips_cartpole.py TIPS/models/tips_lunarlander.py DCOACH/models/utils.py TIPS/models/feedback_ext.py gym_modifications/classic_control/cartpole_zero.py gym_modifications/classic_control/cartpole.py DCOACH/models/dcoach_reacher.py DCOACH/models/feedback_lunar.py DCOACH/models/dcoach_lunarlandercont.py TIPS/models/fdm_reacher.py TIPS/models/tips_lunarlandercont.py TIPS/models/feedback_lunar.py DCOACH/models/dcoach_cartpole.py gym_modifications/reacher.py TIPS/models/utils.py TIPS/models/tips_reacher.py DCOACH DCOACH_cartpole DCOACH_lunarlander DCOACH_lunarlandercont DCOACH_reacher Feedback Feedback_ext Feedback_lunar bias_initializer weight_initializer heuristic LunarLander LunarLanderContinuous demo_heuristic_lander ContactDetector ReacherEnv _merge CartPoleEnv CartPoleZeroEnv ContinuousCartPoleEnv fdm_cont fdm fdm_cont fdm FDM_ReacherEnv fdm_cont Feedback Feedback_ext Feedback_lunar TIPS TIPS_cartpole TIPS_lunarlander TIPS_lunarlandercont TIPS_reacher bias_initializer weight_initializer abs array clip continuous seed format heuristic print render reset step update squeeze cos sin b2World linearVelocity CreateDynamicBody Step position ApplyLinearImpulse b2World linearVelocity CreateDynamicBody astype float32 sign Step position abs clip ApplyLinearImpulse step reset_model | # Interactive Imitation Learning in State-Space This repository contains the implementation of a novel interactive learning method: TIPS (Teaching Imitative Policies in State-space). The code runs simulations in OpenAI Gym environments where an agent performs control tasks. A demonstrator can provide feedback (using arrow keys on a keyboard) to teach the task to the agent. The training is online and thus the agents' learnt behavior can be directly observed during the training process. Besides the method TIPS, we also provide an implementation of D-COACH, a comparative method that is in the same family but uses feedback in the action-space of the agent. ## Installation The code is implemented in **python** and uses the Tensorflow library for training neural networks. To use the code, it is necessary to first install the OpenAI gym toolkit (release v0.10.5): https://github.com/openai/gym Then, the files in the `gym_modifications` directory of this repository should be replaced/added in the installed gym directory on your PC. There are three gym environments used: **CartPole**, **LunarLanderContinuous** and **Reacher**. The Reacher environment uses the MuJoCo physics engine and thus also requires a MuJoCo license and the installation of mujoco-py (https://github.com/openai/mujoco-py). ### Requirements - pygame | 3,662 |
sjia1/ODT-with-noisy-outcomes | ['active learning'] | ['Optimal Decision Tree with Noisy Outcomes'] | func_su.py compute_cover_time_ec_nbhd gen_b estimate_G_new print_alive_sce compute_cover_time compute_cover_time_ec_clique estimate_G str range randint print print gen_b zeros sum range values list remove gen_b zeros sum range values len print sum range values len append sum range values len print sum range values len | sjia1/ODT-with-noisy-outcomes | 3,663 |
sjmluo/IGLLM | ['time series'] | ['Hierarchical Probabilistic Model for Blind Source Separation via Legendre Transformation'] | latent_linear_model.py latent_linear_model | sjmluo/IGLLM | 3,664 |
sjoerdvansteenkiste/Relational-NEM | ['common sense reasoning'] | ['Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions'] | network.py nem.py nem_model.py datasets.py utils.py cfg InputPipeLine build_graph get_logs run populate_debug_out print_log_dict run_epoch build_rollout_graph run_from_file create_debug_plots set_up_optimizer cfg create_curve_plots build_graphs log_log_dict add_noise build_debug_graph build_dynamic_graph rollout_from_file add_log compute_outer_loss get_loss_step_weights NEMCell binomial_cross_entropy_loss compute_outer_ub_loss dynamic_nem_iteration cfg static_nem_iterations compute_prior kl_loss_bernoulli ActivationFunctionWrapper ReshapeWrapper LayerNormWrapper InputWrapper cfg R_NEM build_network OutputWrapper create_directory delete_files tf_adjusted_rand_index color_half_spines evaluate_groups_seq color_spines print_vars parse_activation_function get_gamma_colors curve_plot evaluate_groups save_image overview_plot compute_gradients add_noise value reshape dynamic_nem_iteration stack get_default_graph get_name_scope add_noise get_loss_step_weights tf_adjusted_rand_index value format reshape static_nem_iterations stack get_default_graph append range get_tensor_by_name get_name_scope build_debug_graph reuse_variables get_variable_scope join suptitle close curve_plot savefig join format get_loss_step_weights suptitle evaluate_groups_seq close savefig overview_plot enumerate get_debug_samples create_debug_plots run populate_debug_out get_n_batches append range run append split get items list format add_log print join format sum get_loss_step_weights set_random_seed sum get_loss_step_weights set_random_seed create_directory trainable_variables delete_files get_loss_step_weights InputPipeLine output print_vars reset_default_graph build_graphs global_variables_initializer float sum isinstance get_loss_step_weights NEMCell shape compute_prior build_network shape compute_prior build_network NEMCell save join unlink isfile listdir makedirs as_list name print sum evaluate_groups range reshape mean adjusted_mutual_info_score append argmax max range set_linewidth set_visible set_color set_linewidth set_visible set_color ones hsv_to_rgb linspace sum plot_attention_summary_img subplots plot_img plot_gamma subplots_adjust get_gamma_colors shape set_visible range clip items list subplots set_title plot set_xlabel axis legend | # Relational Neural Expectation-Maximization          | 3,665 |
sjsu-smart-lab/Self-supervised-Monocular-Trained-Depth-Estimation-using-Self-attention-and-Discrete-Disparity-Volum | ['depth estimation', 'monocular depth estimation'] | ['Deep Ordinal Regression Network for Monocular Depth Estimation', 'Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volume'] | networks/monodepth2_decoder.py networks/asp_oc_block.py networks/util.py export_gt_depth.py kitti_utils.py evaluate_pose.py datasets/cityscapes_dataset.py networks/pose_cnn.py train.py evaluate_city_depth.py test_simple.py inplace_abn/__init__.py networks/__init__.py evaluate_depth.py trainer.py layers.py options.py utils.py networks/encoder_selfattn.py datasets/kitti_dataset.py inplace_abn/bn.py networks/pose_decoder.py datasets/__init__.py datasets/mono_dataset.py networks/decoder.py networks/resnet_encoder.py inplace_abn/functions.py networks/base_oc_block.py batch_post_process_disparity compute_errors evaluate batch_post_process_disparity compute_errors evaluate compute_ate dump_xyz evaluate main export_gt_depths_cityscapes export_gt_depths_kitti sub2ind load_velodyne_points generate_depth_map read_calib_file rot_from_axisangle BackprojectDepth get_smooth_loss compute_depth_errors upsample Project3D Conv3x3 get_translation_matrix SSIM transformation_from_parameters disp_to_depth ConvBlock MonodepthOptions parse_args print_size_of_model test_simple Trainer sec_to_hm sec_to_hm_str normalize_image readlines download_model_if_doesnt_exist CityscapesData KITTIOdomDataset KITTIDepthDataset KITTIDataset KITTIRAWDataset MonoDataset pil_loader InPlaceABN InPlaceABNSync ABN _act_forward _count_samples _broadcast_shape InPlaceABNSync InPlaceABN _reduce _check _act_backward ASP_OC_Module _SelfAttentionBlock SelfAttentionBlock2D BaseOC_Module BaseOC_Context_Module MSDepthDecoder upsample SoftAttnDepth Conv3x3 ConvBlock UpBlock ResNet ResNet_context get_resnet101_asp_oc_dsn DepthDecoder PoseCNN PoseDecoder ResnetEncoder ResNetMultiImageInput resnet_multiimage_input conv3x3 Bottleneck outS maximum mean sqrt abs log meshgrid shape linspace median DataLoader resize cuda get_resnet101_asp_oc_dsn std MSDepthDecoder logical_and load_weights_folder shape load_state_dict append expanduser range CityscapesData state_dict format concatenate no_self_attention astype mean eval load join num_ch_enc print compute_errors data_path int32 zeros array ext_disp_to_eval imwrite uint16 save_pred_disps save DepthDecoder clip eval_split len eval_stereo readlines KITTIRAWDataset makedirs no_ddv no_eval quit eval_eigen_to_benchmark append dot eye sqrt sum batch_size num_layers compute_ate dirname ResnetEncoder width KITTIOdomDataset height int reshape inv dot dump_xyz PoseDecoder join int format generate_depth_map print readlines astype float32 data_path dirname append savez_compressed split join sorted format list str print glob INTER_AREA astype float32 data_path dirname resize append IMREAD_UNCHANGED dataset imread savez_compressed add_argument export_gt_depths_cityscapes ArgumentParser parse_args export_gt_depths_kitti reshape set join T sub2ind int read_calib_file reshape hstack astype min dot shape vstack int32 eye round zeros load_velodyne_points rot_from_axisangle transpose clone matmul get_translation_matrix to view norm squeeze cos unsqueeze sin to mean abs mean sqrt abs max log add_argument ArgumentParser remove print save getsize state_dict print_size_of_model device DepthDecoder get_resnet101_asp_oc_dsn list MSDepthDecoder image_path load_state_dict dirname to format glob no_self_attention mean eval ext num_ch_enc join load isdir print no_ddv model_name isfile std len data float int sec_to_hm join format urlretrieve print quit makedirs fn append size enumerate size enumerate elu_forward slope leaky_relu_forward elu_backward slope leaky_relu_backward ResNet_context load_url cat load_state_dict ResNetMultiImageInput ceil int | # Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volume - ML Reproducibility Challenge 2020 This project is a reproduction of the CVPR 2020 paper > **Self-supervised Monocular Trained Depth Estimation > using Self-attention and Discrete Disparity Volume** > > Adrian Johnston, Gustavo Carneiro > > [CVPR 2020 (arXiv pdf)](https://arxiv.org/pdf/2003.13951.pdf) It proposes to close the performance gap with the fully-supervised methods using only the monocular sequence for training with the help | 3,666 |
skamdar/gesture_recognition | ['action recognition'] | ['Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?'] | utils/eval_ucf101.py thread.py utils/video_jpg.py datasets/grit.py utils/grit_json.py opts.py models/resnext.py utils/eval_grit.py train.py captureVideo.py main2.py datasets/hmdb51.py dataset.py models/wide_resnet.py models/densenet.py models/my_model.py utils/ucf101_json.py utils.py models/mccnn.py utils/eval_kinetics.py utils/grit_processing.py datasets/activitynet.py utils/n_frames_grit.py gradient_transforms.py models/pre_act_resnet.py temporal_transforms.py test.py utils/kinetics_json.py real_time.py datasets/ucf101.py utils/eval_hmdb51.py mean.py utils/hmdb51_json.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 models/lstm.py webcam.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 calcGradient get_std get_mean generate_model parse_opts readwebcam MultiScaleCornerCrop CenterCrop MultiScaleRandomCrop ToTensor Compose Scale Normalize RandomHorizontalFlip CornerCrop ClassLabel VideoID Compose TemporalBeginCrop LoopPadding TemporalCenterCrop TemporalRandomCrop calculate_video_results test readwebcam 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 cnnlstm get_class_labels load_annotation_data video_loader make_dataset GRIT accimage_loader get_default_image_loader get_default_video_loader cal_gradient 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 LSTM myModel myModel 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 compute_video_hit_at_k GRITclassification HMDBclassification compute_video_hit_at_k get_blocked_videos KINETICSclassification compute_video_hit_at_k UCFclassification compute_video_hit_at_k load_labels convert_grit_csv_to_activitynet_json convert_csv_to_dict 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 class_process load_labels convert_ucf101_csv_to_activitynet_json convert_csv_to_dict class_process class_process video_path UCF101 ActivityNet sample_duration GRIT HMDB51 annotation_path Kinetics video_path UCF101 n_val_samples ActivityNet sample_duration GRIT annotation_path HMDB51 Kinetics UCF101 video_path ActivityNet GRIT Kinetics HMDB51 annotation_path 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 myModel print pretrain_path densenet121 parse_args set_defaults add_argument ArgumentParser VideoCapture read print reshape transpose delete imshow shape acquire append release topk size mean stack append range update time format model print Variable cpu AverageMeter size eval softmax calculate_video_results append range enumerate len size topk format view print 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 fromarray bitwise_and shape append zeros array range fromarray shape zeros sobel array range split 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 print size ResNet ResNet ResNet ResNet ResNet ResNet ResNet ResNeXt ResNeXt ResNeXt WideResNet reset_index size tolist mean unique zeros values enumerate Request urlopen format ceil read_csv split append range len append range read_csv update load_labels convert_csv_to_dict join listdir append join listdir update get_labels convert_csv_to_dict update load_labels convert_csv_to_dict join int print sort append listdir update load_labels convert_csv_to_dict format call mkdir splitext exists | # 3D ResNets for Gesture Recogntion The reference codebase is taken from https://github.com/kenshohara/video-classification-3d-cnn-pytorch. Use cases are as described below by original authers. We used GRIT database: [ Tsironi, Eleni and Barros, Pablo and Weber, Cornelius and Wermter, Stefan, "An Analysis of Convolutional Long Short-Term Memory Recurrent Neural Networks for Gesture Recognition", Neurocomput., https://doi.org/10.1016/j.neucom.2016.12.088, Elsevier Science Publishers B. V. ](https://doi.org/10.1016/j.neucom.2016.12.088) ## Results obtained on GRIT dataset |Model | Use Case | Training Acc | Val Acc| | 3,667 |
skanda99/Simple-Neural-Networks | ['stochastic optimization'] | ['Adam: A Method for Stochastic Optimization'] | sample_classification.py SiNN.py Classification_Metrics Weights_Bias_Initializer Loss Optimizers Operations Activation | ### SIMPLE NEURAL NETWORKS Simple Neural Networks (SiNN) is a library for building sequential dense neural networks. Support for standard ingredients for a neural network are provided like activation functions, loss functions, weights/bias initializers, optimizers (Adam, SGD, BGD) and some necessary operations like feed-forward and back-propagation. Any of the above can be customized according to requirement and used as just a reference to the functions are passed as arguments in most of the cases. Each of the optimizer can calculate Receiver Operating characteristics (ROC) for training set. The library uses numpy and random frameworks in the background. A sample MNIST digit classification file has been put up for reference. ## Activation functions - Linear - Sigmoid - Softmax - ReLU - Tangent Hyperbolic ## Loss functions | 3,668 |
skiler07/mst-tf | ['style transfer'] | ['Multimodal Style Transfer via Graph Cuts'] | setup.py trainer/train.py trainer/utils.py trainer/MST.py MST parse_args train_mst load_weights_from_gcs create_tf_dataset get_Fcs plot_test_images restore_original_image DataLoader copy_file_to_gcs format train_step print save_decoder_weights create_tf_dataset Adam reset_states result plot_test_images take MST prefetch Mean create_file_writer range add_argument ArgumentParser get_content_feature_map float64 get_style_feature_map reshape astype apply_kmeans gen from_generator read write close FileIO open items join format subplots set_title suptitle collect print axis close restore_original_image imshow shape savefig copy_file_to_gcs startswith array enumerate | # Multimodal Style Transfer via Graph Cuts Implementation in TF 2.0.0a Paper implementation of [Multimodal Style Transfer via Graph Cuts](https://arxiv.org/abs/1904.04443) ## Results Open `Test.ipynb` to run your own tests. `alpha=1`   ## Training To start local training (after building the image datapath structure), simply run: ``` | 3,669 |
skmatz/prcc-pytorch | ['person re identification'] | ['Person Re-identification by Contour Sketch under Moderate Clothing Change'] | tests/test_prcc_pytorch.py prcc_pytorch/__init__.py prcc_pytorch/dataset.py Person PRCCDataset test_version | # PRCC-PyTorch :shirt: PyTorch implementation of PRCC dataset ## Overview [**PRCC** dataset](https://www.isee-ai.cn/~yangqize/clothing.html) is a dataset proposed by [Q. Yang et al.](https://ieeexplore.ieee.org/document/8936426). This repository is an unofficial implementation of the PRCC dataset for PyTorch. - Dataset is available [here](https://www.isee-ai.cn/~yangqize/clothing.html). - Original paper is available at [IEEE](https://ieeexplore.ieee.org/document/8936426) or [arXiv](https://arxiv.org/abs/2002.02295). | 3,670 |
skyler120/sparsity-halo | ['network pruning'] | ['HALO: Learning to Prune Neural Networks with Shrinkage'] | utils/logger.py models/cifar/vgg.py models/cifar/vgg_snip.py models/cifar/wrn.py cifar_prune_iterative.py utils/eval.py mnist_check_params.py utils/misc.py models/cifar/resnext.py models/cifar/preresnet.py cifar.py compute_energy.py check_params.py utils/__init__.py mnist.py cifar_ncvx.py mnist_prune_iterative.py utils/visualize.py models/cifar/densenet.py models/cifar/alexnet.py models/cifar/resnet.py models/cifar/__init__.py main save_checkpoint test init_model test make_dataset hook_fn get_all_layers LeNet LeNet_5 test LeNet_300_100 train init_model LeNet LeNet_5 test make_dataset LeNet_300_100 LeNet LeNet_5 test LeNet_300_100 main AlexNet alexnet densenet Transition DenseNet Bottleneck BasicBlock preresnet PreResNet Bottleneck conv3x3 BasicBlock ResNet Bottleneck conv3x3 resnet BasicBlock ResNeXtBottleneck resnext CifarResNeXt vgg19 VGG vgg16_bn vgg19_bn vgg11_bn make_layers vgg11 vgg13 vgg13_bn vgg16 VGG_SNIP wrn BasicBlock NetworkBlock WideResNet accuracy plot_overlap savefig Logger LoggerMonitor get_mean_and_std get_conv_zero_param AverageMeter mkdir_p init_params make_image show_mask_single show_mask gauss colorize show_batch endswith SGD DataLoader save_checkpoint Logger modules arch save dataset save_dir cuda dataloader numel load_state_dict percent sum CIFAR100 CrossEntropyLoss format Compose test start_epoch resume startswith CIFAR10 enumerate validation join random_split load int isinstance print sort clone Conv2d parameters mul_ zeros set_names makedirs update data time format criterion model size AverageMeter accuracy eval Bar item finish next enumerate join save print Compose DataLoader CIFAR10 dataset CIFAR100 dataloader format print endswith startswith arch cuda isinstance MaxPool2d Conv2d append numpy register_forward_hook modules isinstance Conv2d xi model zero_grad psi modules slope dataset abs argmax view FloatTensor len numel sum format l1 item batch enumerate norm criterion backward Variable print ws tqdm parameters step layer add_scalar print dataset len MNIST device is_available to save_model device to state_dict is_available MNIST AlexNet CifarResNeXt 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 WideResNet topk size t eq mul_ expand_as append sum max asarray arange plot numbers enumerate len print DataLoader div_ zeros range len modules isinstance Conv2d normal constant isinstance kaiming_normal Conv2d bias modules BatchNorm2d weight Linear makedirs numpy range zeros unsqueeze gauss show make_image imshow make_grid make_image subplot make_grid size clone axis upsampling imshow expand_as range make_image subplot make_grid size clone axis upsampling imshow expand_as cpu range len | # Hierarchical Adaptive Lasso: Learning Sparse Neural Networks via Single Stage Training This repository accompanies the paper, Hierarchical Adaptive Lasso: Learning Sparse Neural Networks via Single Stage Training (https://arxiv.org/abs/2008.10183). ## About the Repository In our paper we introduce HALO, a penalty with learnable parameters for sparsifying models (particularly deep neural networks). This repository contains the code necessary to replicate the experiments in Sections 5 and in the supplementary material and use HALO. ## Setup and Requirements This code was developed and tested on Nvidia V100 in the following environment: - CentOS 7 - Python 3.6.8 A ```requirements.txt``` file with all relevant python packages is included. Apart from python packages, you will want to make sure your workspace has enough room for downloading CIFAR-10/0 and storing several copies of models (VGG models are typically around 100MB unpruned). ### Evaluating pre-trained models | 3,671 |
skyoung/RFL | ['visual tracking'] | ['Recurrent Filter Learning for Visual Tracking'] | train.py rfl_net/xz_net.py data_preprocessing/process_xml.py rfl_net/rnn.py tracking/tracking_demo.py eval.py data_preprocessing/build_data_vid.py data_preprocessing/generate_vidb.py data_input/prepare_targets.py rfl_net/conv_lstm.py data_preprocessing/collect_vid_info.py data_input/data_input.py config.py tracking/rfl_tracker.py rfl_net/utils.py rfl_net/rfl_net.py rfl_net/network.py EvalNet train DataInput bbox_overlaps create_labels_overlap_py create_labels_overlap _float_feature_list _int64_feature partition_vid _bytes_feature_list save_to_tfrecords Vid convert_to_example build_tfrecords EncodeJpeg _bytes_feature _float_feature process_videos process collect_video_info generate_vidb Vid BoundingBox process_xml get_int get_item find_num_bb _conv_linear BasicConvLSTMCell InitLSTMSate layer Network RFLNet lazy_property DropoutWrapper rnn activation_summary print_and_log ConvXZNet XZNet FilterNet calc_x_size RFLearner RFLTracker damp_state calc_z_size run_tracker load_seq_config display_result ConfigProto py_func count_nonzero arange concatenate print reshape transpose hstack stride meshgrid zeros expand_dims bbox_overlaps append minimum prod maximum int sorted print unique append sum array range len load join Thread basename append partition_vid TFRecordWriter print Coordinator tfrecords_path start open enumerate flush len print save_to_tfrecords tolist objs dir acquire append process release get_img_buffer enumerate tuple z_exemplar_size paste floor resize open new prod hstack astype sqrt tile crop join fix_aspect x_instance_size maximum context_amount z_scale img_path array mode len write convert_to_example SerializeToString zip enumerate FeatureLists SequenceExample Features join sorted write close open listdir len pop int join sorted dump max_trackid print trackid Vid process_xml open img_path listdir append split iter BoundingBox join parse get_int get_item getroot append find_num_bb range __name__ name zero_fraction histogram scalar print fix_aspect context_amount sqrt z_scale repeat prod x_instance_size z_exemplar_size tuple join sorted otb_data_dir readlines append listdir open FONT_HERSHEY_DUPLEX join imwrite save_path tuple putText astype is_save imshow rectangle split merge ConfigProto | ## Recurrent Filter Learning for Visual Tracking This is the implementation of our RFL tracker published in ICCV2017 workshop on VOT. Our code is written in **python3(3.5)** using **Tensorflow(>=1.2)** toolbox **For easy comparison, we upload our OTB100 results files to the main directory `./otb100_results.zip`** ### Tracking You use our pretrained model to test our tracker first. 1. Download the model from the link: [model](https://drive.google.com/drive/folders/0BzxOz7xyra_-dzJaY2d0Y1RiZFk?resourcekey=0-aOh9gKNpJ-UHkR0LVJcZNA&usp=sharing) 2. Put the model into directory `./output/models` 3. Run `python3 tracking_demo.py` in directory `./tracking` ### Training | 3,672 |
sleebapaul/AuriaKathi | ['image stylization'] | ['A Closed-form Solution to Photorealistic Image Stylization'] | AttnGAN/gen_art.py AttnGAN/model.py GPT_2_Finetuning/finetune_gpt2.py NVIDIAFastPhotoStyle/demo.py NVIDIAFastPhotoStyle/process_stylization.py GPT_2_Finetuning/gpt_2_simple/encoder.py AttnGAN/miscc/losses.py NVIDIAFastPhotoStyle/smooth_filter.py AttnGAN/GlobalAttention.py GPT_2_Finetuning/gpt_2_simple/load_dataset.py GPT_2_Finetuning/gpt_2_simple/sample.py GPT_2_Finetuning/gpt_2_simple/memory_saving_gradients.py GPT_2_Finetuning/gpt_2_simple/accumulate.py AttnGAN/miscc/config.py NVIDIAFastPhotoStyle/photo_gif.py NVIDIAFastPhotoStyle/photo_wct.py GPT_2_Finetuning/generateFromGPT.py AttnGAN/trainer.py GPT_2_Finetuning/gpt_2.py NVIDIAFastPhotoStyle/models.py NVIDIAFastPhotoStyle/photo_smooth.py AttnGAN/datasets.py GPT_2_Finetuning/gpt_2_simple/model.py AttnGAN/miscc/utils.py TextDataset get_imgs prepare_data parse_args gen_example_from_text func_attention GlobalAttentionGeneral conv1x1 CA_NET RNN_ENCODER NEXT_STAGE_G conv1x1 D_NET256 INIT_STAGE_G downBlock G_DCGAN GET_IMAGE_G upBlock Block3x3_leakRelu G_NET ResBlock CNN_ENCODER D_NET64 encode_image_by_16times D_NET128 Block3x3_relu conv3x3 D_GET_LOGITS GLU condGANTrainer cfg_from_file _merge_a_into_b discriminator_loss words_loss generator_loss sent_loss KL_loss cosine_similarity drawCaption build_super_images2 weights_init build_super_images copy_G_params load_params mkdir_p generate_to_file finetune cmd_generate generateAML get_tarfile_name start_tf_sess encode_csv cmd encode_dataset generate load_gpt2 is_gpt2_downloaded cmd_finetune AccumulatingOptimizer bytes_to_unicode get_pairs get_encoder Encoder binary_search Sampler load_dataset gradients_memory capture_ops format_ops gradients _to_op _to_ops _is_iterable debug_print my_add_control_inputs tf_toposort gradients_speed gradients_collection fast_backward_ops mlp default_hparams norm past_shape model block merge_states gelu attention_mask positions_for conv1d softmax expand_tile attn shape_list split_states top_p_logits sample_sequence top_k_logits VGGDecoder VGGEncoder GIFSmoothing Propagator PhotoWCT ReMapping stylization memory_limit_image_resize Timer smooth_local_affine smooth_filter Variable sort CUDA squeeze append numpy cuda range len minimum int size convert maximum B_DCGAN transform normalize BRANCH_NUM crop range append add_argument ArgumentParser RegexpTokenizer decode max asarray replace print gen_example lower append zeros tokenize range len bmm contiguous view Sequential conv3x3 Upsample BatchNorm2d GLU conv3x3 Sequential BatchNorm2d GLU conv3x3 LeakyReLU Sequential BatchNorm2d BatchNorm2d LeakyReLU Sequential Conv2d BatchNorm2d LeakyReLU Sequential Conv2d items list ndarray isinstance type array _merge_a_into_b norm sum bmm uint8 norm concatenate reshape CUDA transpose astype clamp squeeze ByteTensor masked_fill_ unsqueeze append GAMMA3 cuda range exp_ CUDA GAMMA3 cuda log view transpose tolist append sum range cat concatenate func_attention astype ByteTensor masked_fill_ GAMMA1 uint8 reshape contiguous repeat cosine_similarity detach size UNCOND_DNET netD COND_DNET words_loss sent_loss size image_encoder len UNCOND_DNET LAMBDA range COND_DNET add_ mul_ fromarray load_default decode Draw text size append numpy range len paste max fromarray drawCaption view ones transpose new shape append range cat pyramid_expand concatenate size astype uint8 print min mul_ zeros numpy paste max fromarray drawCaption view ones transpose new shape append sum range pyramid_expand concatenate size astype float uint8 print min mul_ zeros numpy len data fill_ orthogonal normal_ __name__ copy_ parameters zip deepcopy list makedirs ConfigProto OFF gradients model AccumulatingOptimizer Saver save Sampler run list restore maketree exit copyfile placeholder total_size apply_gradients load_dataset range get_encoder format latest_checkpoint FileWriter sparse_softmax_cross_entropy_with_logits zip compute_gradients generate_samples join default_hparams remove time int sample_sequence print AdamOptimizer reduce_mean reset int32 add_summary global_variables_initializer scalar join default_hparams restore model latest_checkpoint print placeholder Saver int32 global_variables_initializer run decode search set_random_seed lstrip run seed open escape placeholder encode append range get_encoder format group close S join default_hparams print write int32 decode search set_random_seed lstrip run seed open escape placeholder append encode range get_encoder format group close S default_hparams print write int32 generate sep replace join print get_encoder load_dataset savez_compressed cmd_generate add_argument ArgumentParser parse_args cmd_finetune finetune start_tf_sess download_gpt2 generate_to_file join format start_tf_sess utcnow mkdir trange load_gpt2 append list range ord add set join encode isdir glob endswith tqdm stack isfile append walk f my_add_control_inputs device stop_gradient fast_backward_ops values sgv list get_collection _unsparsify tf_gradients tf_toposort intersection ceil append range filter_ts_from_regex _set_device format_ops get_backward_walk_ops _to_ops debug_print op set sqrt zip get_forward_walk_ops copy_with_input_replacements reroute_ts filter_ts keys int items len toposort list inputs set outputs intersection get_forward_walk_ops append difference get_backward_walk_ops set int time str select_ops extend get_default_graph hasattr iter print tuple add_control_inputs as_list shape exp reduce_max shape_list shape_list range convert_to_tensor ndims fill height print thumbnail BICUBIC width load _best_local_affine_kernel bytes Module namedtuple Stream numpy Program _reconstruction_best_kernel encode _bilateral_smooth_kernel cuda compile get_function fromarray uint8 transpose convert ascontiguousarray shape resize smooth_local_affine array clip | # Source code of Auria Kathi - The first AI Poet Artist ## Data Sources and trained models For setting up Azure, I've uploaded all the required files to Azure Machine Learning Datastores. Whenever the data is required, it is referred from the respective Datastore. If you would like to set things up locally, the following links are useful **Haikus for Language Model** - Cleaned haiku dataset: https://bit.ly/2MHlFMu **AttnGAN** - Image Encoder - https://bit.ly/2MGBnaH - Text Encoder - https://bit.ly/2ZA72zX - AttnGAN Model - https://bit.ly/2F34vEZ | 3,673 |
smallcowbaby/OmniAnomaly | ['time series', 'anomaly detection'] | ['Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network'] | data_preprocess.py omni_anomaly/training.py omni_anomaly/recurrent_distribution.py omni_anomaly/vae.py omni_anomaly/prediction.py omni_anomaly/model.py omni_anomaly/spot.py main.py omni_anomaly/utils.py omni_anomaly/eval_methods.py omni_anomaly/wrapper.py load_and_save load_data main ExpConfig adjust_predicts calc_seq bf_search calc_point2point pot_eval OmniAnomaly Predictor RecurrentDistribution backMean SPOT biSPOT dSPOT bidSPOT Trainer minibatch_slices_iterator BatchSlidingWindow get_data preprocess save_z get_data_dim Lambda VAE TfpDistribution wrap_params_net softplus_std rnn wrap_params_net_srnn genfromtxt join print join sorted int asarray print endswith load_and_save strip literal_eval extend concatenate_and_save zeros listdir makedirs get_data_dim basicConfig max_test_size get_data dataset max_train_size sum asarray range len adjust_predicts calc_point2point list append print range calc_seq adjust_predicts initialize print SPOT fit len calc_point2point run append sum range len range startswith join print close preprocess get_data_dim open asarray print nan_to_num any fit_transform | # OmniAnomaly ### Anomaly Detection for Multivariate Time Series through Modeling Temporal Dependence of Stochastic Variables OmniAnomaly is a stochastic recurrent neural network model which glues Gated Recurrent Unit (GRU) and Variational auto-encoder (VAE), its core idea is to learn the normal patterns of multivariate time series and uses the reconstruction probability to do anomaly judgment. ## Getting Started #### Clone the repo ``` git clone https://github.com/smallcowbaby/OmniAnomaly && cd OmniAnomaly ``` #### Get data SMD (Server Machine Dataset) is in folder `ServerMachineDataset`. | 3,674 |
smallflyingpig/universal_adversarial_perturbation_generative_network_for_speaker_recognition | ['speaker recognition'] | ['Universal Adversarial Perturbations Generative Network for Speaker Recognition'] | common/model.py common/utils.py common/script.py train_generator.py common/dataset.py generator.py common/trainer.py plot.py common/prepare_dataset.py InterpolationBlock Generator1D Identity LinearBlock UpBlock main plot_UAP_length get_parser UniversalLoss batch_process_generator SpeakerLossTarget test_wav save_grad evaluate test_interpolation _init_fn get_pretrained_models MSEWithThreshold get_parser get_dict_str main sentence_test SpeakerLoss EvalHook load_pickle TIMIT TIMIT_speaker_norm LibriSpeech_speaker TIMIT_base read_list TIMIT_speaker TIMIT_speech act_fun SincConv_fast MLP sinc SincNet sinc_conv LayerNorm Indentity SincClassifier flip BandPassFilter prepare_data_for_speech prepare_data_for_speaker convertPredictions chunk_file_with_phone main get_parser transformPhn plot_wave plot_perturtation plot_freq_analysis get_parser main plot_band_pass_filter plot_spectrogram IncrementalAverage BaseTrainer load_checkpoint RunningAverage save_checkpoint ClassifierTrainer weights_to_cpu get_noise_scale read_conf get_dict_from_args MOS_LQO SNR str_to_bool PESQ show subplot format plot xlabel ylabel tight_layout subplots_adjust data_path title figure legend zip sort_values read_csv enumerate len parse_args add_argument ArgumentParser update format randn print multiply_dict len close accumulate_dict tqdm get_dict_str eval DataLoader set_description item noise_dim cuda enumerate detach speaker_model squeeze stack append sum max cat randn float16 set_description unsqueeze save sentence_test max clip dirname get_dict_str append noise_dim PESQ update format RunningAverage astype close eval preprocess enumerate join read print write to_csv SNR tqdm average numpy makedirs randn float16 set_description unsqueeze save sentence_test max clip dirname get_dict_str append noise_dim PESQ update format RunningAverage astype close eval preprocess enumerate join read print write to_csv SNR tqdm average numpy makedirs join randn zero_grad loss_func clamp_ forward cuda list get_dict_str noise_dim detach get update save_grad mean eval item items backward register_hook train step fs load list items dnn load_raw_state_dict print load_checkpoint get_dict_from_args parameters eval cnn classifier SincClassifier model_path seed test_output batch_size read_conf LibriSpeech_speaker target noise_scale DataLoader output_dir cuda run seed pt_file StepLR Generator1D set_device DistributedSampler TIMIT_speaker_norm Adam speaker_cfg frame_dim noise_dim SpeakerLoss EvalHook UniversalLoss test_wav speaker_model init_process_group readlines no_dist test eval manual_seed item info load deepcopy SpeakerLossTarget join evaluate load_raw_state_dict print load_checkpoint test_interpolation get_pretrained_models parameters data_root beta ClassifierTrainer local_rank size view contiguous cat flip pi sin append range len append int min len transformPhn list format read join print len astype close to_csv tqdm chunk_file_with_phone append rglob DataFrame makedirs join list sorted read format print len astype close to_csv tqdm set chunk_file_with_phone append DataFrame transformPhn fs wshift prepare_data_for_speech wlen prepare_data_for_speaker load show axis waveplot figure load show stft amplitude_to_db axis colorbar figure specshow abs load join show get_normed_spec subplots set_xlabel tight_layout bar dirname legend set_label_coords load subplot show format stft amplitude_to_db tight_layout colorbar title unsqueeze numpy waveplot figure zip specshow abs BandPassFilter show subplot list format plot xlabel tolist ylabel tight_layout subplots_adjust title figure zip annotate read_csv enumerate len plot_perturtation plot_freq_analysis plot_band_pass_filter plot_data_path load hasattr isinstance load_state_dict startswith OrderedDict items list cpu hasattr makedirs dirname save module state_dict get int read list str Namespace print ConfigParser map str_to_bool float split getattr mean max log10 exp | smallflyingpig/universal_adversarial_perturbation_generative_network_for_speaker_recognition | 3,675 |
smartworkdilip/Art-generation-with-neural-style-transfer | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | art_generation_neural_style_transfer.py compute_style_cost model_nn compute_content_cost gram_matrix compute_layer_style_cost total_cost as_list reshape square reduce_sum transpose matmul as_list reshape transpose subtract square reduce_sum gram_matrix compute_layer_style_cost run str print assign global_variables_initializer save_image range run | smartworkdilip/Art-generation-with-neural-style-transfer | 3,676 |
smdYe/FC-DenseNet-Keras | ['semantic segmentation'] | ['The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation'] | layers.py tiramisu_net.py TransitionDown SoftmaxLayer BN_ReLU_Conv TransitionUp Tiramisu BN_ReLU_Conv concatenate TransitionDown SoftmaxLayer concatenate print Model BN_ReLU_Conv TransitionUp summary append Input range len | smdYe/FC-DenseNet-Keras | 3,677 |
smstrzd/IntegratedGradCAM | ['object localization'] | ['Integrated Grad-CAM: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks via Integrated Gradient-Based Scoring'] | dependency/TorchRay-master/examples/linear_approx_manual.py dependency/TorchRay-master/build/lib/torchray/attribution/linear_approx.py dependency/TorchRay-master/torchray/benchmark/datasets.py dependency/TorchRay-master/torchray/attribution/linear_approx.py dependency/TorchRay-master/docs/conf.py dependency/TorchRay-master/torchray/benchmark/server.py dependency/TorchRay-master/examples/contrastive_excitation_backprop.py dependency/TorchRay-master/torchray/utils.py dependency/TorchRay-master/examples/gradient_manual.py dependency/TorchRay-master/build/lib/torchray/benchmark/datasets.py dependency/TorchRay-master/build/lib/torchray/attribution/rise.py dependency/TorchRay-master/setup.py dependency/TorchRay-master/build/lib/torchray/__init__.py dependency/TorchRay-master/examples/grad_cam_manual.py dependency/TorchRay-master/torchray/benchmark/pointing_game.py dependency/TorchRay-master/build/lib/torchray/attribution/common.py dependency/TorchRay-master/build/lib/torchray/attribution/excitation_backprop.py dependency/TorchRay-master/torchray/attribution/gradient.py dependency/TorchRay-master/examples/attribution_benchmark.py dependency/TorchRay-master/examples/guided_backprop.py dependency/TorchRay-master/examples/linear_approx.py utils.py dependency/TorchRay-master/build/lib/torchray/benchmark/pointing_game.py dependency/TorchRay-master/examples/__init__.py dependency/TorchRay-master/torchray/benchmark/__init__.py dependency/TorchRay-master/torchray/__init__.py dependency/TorchRay-master/examples/__main__.py dependency/TorchRay-master/torchray/attribution/rise.py dependency/TorchRay-master/examples/deconvnet_manual.py dependency/TorchRay-master/build/lib/torchray/utils.py dependency/TorchRay-master/torchray/attribution/excitation_backprop.py dependency/TorchRay-master/torchray/benchmark/logging.py dependency/TorchRay-master/torchray/benchmark/models.py dependency/TorchRay-master/examples/extremal_perturbation.py dependency/TorchRay-master/torchray/attribution/common.py dependency/TorchRay-master/examples/grad_cam.py dependency/TorchRay-master/torchray/attribution/deconvnet.py dependency/TorchRay-master/build/lib/torchray/attribution/gradient.py dependency/TorchRay-master/build/lib/torchray/benchmark/__init__.py dependency/TorchRay-master/torchray/attribution/grad_cam.py dependency/TorchRay-master/build/lib/torchray/attribution/guided_backprop.py dependency/TorchRay-master/torchray/attribution/extremal_perturbation.py dependency/TorchRay-master/examples/gradient.py dependency/TorchRay-master/examples/excitation_backprop_manual.py dependency/TorchRay-master/examples/rise.py dependency/TorchRay-master/examples/contrastive_excitation_backprop_manual.py dependency/TorchRay-master/build/lib/torchray/benchmark/logging.py dependency/TorchRay-master/examples/excitation_backprop.py dependency/TorchRay-master/examples/guided_backprop_manual.py dependency/TorchRay-master/build/lib/torchray/attribution/deconvnet.py dependency/TorchRay-master/build/lib/torchray/attribution/grad_cam.py dependency/TorchRay-master/torchray/attribution/guided_backprop.py dependency/TorchRay-master/build/lib/torchray/attribution/extremal_perturbation.py dependency/TorchRay-master/build/lib/torchray/benchmark/models.py dependency/TorchRay-master/examples/deconvnet.py dependency/TorchRay-master/build/lib/torchray/benchmark/server.py xmkdir im_to_numpy resample get_config pil_to_tensor is_url get_device imsmooth imarraysc imread imsc tensor_to_im NullContext gradient_to_saliency _CatchOutputs _wrap_in_list Probe _CatchInputs _InjectContrast resize_saliency get_backward_gradient _Catch ReLUContext get_module Patch attach_debug_probes get_pointing_gradient saliency DeConvNetReLU DeConvNetContext deconvnet EltwiseSumFunction _get_classifier_layer ExcitationBackpropContext gradient_to_excitation_backprop_saliency excitation_backprop gradient_to_contrastive_excitation_backprop_saliency contrastive_excitation_backprop EltwiseSum update_resnet Perturbation contrastive_reward simple_reward extremal_perturbation MaskGenerator gradient gradient_to_grad_cam_saliency grad_cam GuidedBackpropReLU guided_backprop GuidedBackpropContext linear_approx gradient_to_linear_approx_saliency rise rise_class _upsample_reflect coco_as_image_name voc_as_image_size coco_as_class_ids coco_as_image_size voc_as_mask get_dataset VOCDetection ImageFolder coco_as_mask voc_as_class_ids voc_as_image_name CocoDetection mongo_load data_to_mongo mongo_save mongo_connect data_from_mongo last_lines _load_caffe_resnet50 _caffe_vgg16_to_fc _load_caffe_vgg16 replace_module get_transform _caffe_resnet50_to_fc _replace_module get_model _fix_caffe_maxpool PointingGameBenchmark PointingGame run_server plot_example get_example_data setup ExperimentExecutor ProcessingError _saliency_to_point Experiment run_all_examples xmkdir im_to_numpy resample get_config pil_to_tensor is_url get_device imsmooth imarraysc imread imsc tensor_to_im NullContext gradient_to_saliency _CatchOutputs _wrap_in_list Probe _CatchInputs _InjectContrast resize_saliency get_backward_gradient _Catch ReLUContext get_module Patch attach_debug_probes get_pointing_gradient saliency DeConvNetReLU DeConvNetContext deconvnet EltwiseSumFunction _get_classifier_layer ExcitationBackpropContext gradient_to_excitation_backprop_saliency excitation_backprop gradient_to_contrastive_excitation_backprop_saliency contrastive_excitation_backprop EltwiseSum update_resnet Perturbation contrastive_reward simple_reward extremal_perturbation MaskGenerator gradient gradient_to_grad_cam_saliency grad_cam GuidedBackpropReLU guided_backprop GuidedBackpropContext linear_approx gradient_to_linear_approx_saliency rise rise_class _upsample_reflect coco_as_image_name voc_as_image_size coco_as_class_ids coco_as_image_size voc_as_mask get_dataset VOCDetection ImageFolder coco_as_mask voc_as_class_ids voc_as_image_name CocoDetection mongo_load data_to_mongo mongo_save mongo_connect data_from_mongo last_lines _load_caffe_resnet50 _caffe_vgg16_to_fc _load_caffe_vgg16 replace_module get_transform _caffe_resnet50_to_fc _replace_module get_model _fix_caffe_maxpool PointingGameBenchmark PointingGame run_server plot_example get_example_data join exists device makedirs urlparse array permute ANTIALIAS convert min Request urlopen is_url resize float open pil_to_tensor isinstance Image dtype arange expand unsqueeze device meshgrid cat len exp arange expand conv2d pad ceil ceil sqrt range zeros isinstance exp zeros_like zeros_like scatter_ shape expand_as tensor len named_modules Module isinstance interpolate OrderedDict Probe named_modules gradient_to_saliency requires_grad remove training named_parameters resize_saliency requires_grad_ eval get_module zero_ imsmooth train Probe attach_debug_probes named_modules format isinstance print __get__ EltwiseSum get_module split remove _get_classifier_layer training resize_saliency requires_grad_ eval parameters get_module attach_debug_probes gradient_to_contrastive_excitation_backprop_saliency int get int model zero_grad SGD clf numpy device shape_out max subplot apply reward_func title legend generate imsmooth to sum prod range cat detach format plot shape_in mean requires_grad_ flip imsc enumerate join isinstance backward print clamp pause resize_saliency parameters figure zeros step split mean clamp sum grad sum grad pad interpolate enumerate list rise shape tensor cat isinstance int bool voc_as_image_size zeros to int bool tensor add_ zeros to annToMask get join format VOCDetection ImageFolder CocoDetection MongoClient get_config server_info replace_one with_options Binary read BytesIO seek ndarray isinstance save Tensor Binary BytesIO isinstance len min split modules MaxPool2d isinstance _fix_caffe_maxpool load_state_dict children isinstance view copy Conv2d copy_ shape MethodType enumerate Linear isinstance MaxPool2d Conv2d sign load_state_dict modules BatchNorm2d _fix_caffe_maxpool range view copy Conv2d copy_ shape bias MethodType named_children isinstance Sequential OrderedDict getattr setattr _load_caffe_resnet50 _caffe_vgg16_to_fc _load_caffe_vgg16 replace_module in_features _caffe_resnet50_to_fc eval load_state_dict_from_url Linear Compose print get_config call get BytesIO Compose requires_grad_ eval parameters unsqueeze get_device content to open show subplot format isinstance makedirs strip title clf savefig dirname abspath range imsc len add_stylesheet argmax view len pause draw figure | smstrzd/IntegratedGradCAM | 3,678 |
smurthy2020/fashion | ['data augmentation'] | ['DENSER: Deep Evolutionary Network Structured Representation'] | utils/helper.py configs.py benchmark/convnet.py app.py benchmark/runner.py utils/argparser.py utils/mnist_reader.py visualization/project_zalando.py start_s3_sync get_json_logger touch touch_dir _get_logger main cnn_model_fn PredictJob JobWorker JobManager get_args_request parse_arg get_args_cli now_int upload_result_s3 get_sprite_image invert_grayscale create_sprite_image vector_to_matrix_mnist UploadS3Thread load_mnist UploadS3Thread start Event dirname makedirs makedirs setFormatter touch_dir DEBUG getLogger addHandler StreamHandler Formatter touch setLevel INFO FileHandler setFormatter getLogger addHandler Formatter touch setLevel INFO FileHandler dense max_pooling2d dropout one_hot minimize reshape GradientDescentOptimizer conv2d softmax_cross_entropy asarray evaluate print Estimator shuffle labels images numpy_input_fn train range read_data_sets int append items list defaultdict utcfromtimestamp info int isinstance ones sqrt ceil array range vector_to_matrix_mnist invert_grayscale join | smurthy2020/fashion | 3,679 |
snap-stanford/GraphRNN | ['graph generation'] | ['GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models'] | baselines/mmsb.py baselines/graphvae/model.py dataset/ENZYMES/load_data.py baselines/graphvae/train.py eval/mmd.py train.py data.py eval/stats.py eval/setup.py args.py plot.py baselines/graphvae/data.py baselines/baseline_simple.py main_DeepGMG.py evaluate.py utils.py create_graphs.py main.py model.py analysis.py test_MMD.py find_nearest_idx Args 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 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 CNN_decoder GRU_plain gumbel_softmax CNN_decoder_share GCN_decoder sample_sigmoid GCN_encoder LSTM_plain CNN_decoder_attention calc_graph_embedding MLP_token_plain sample_sigmoid_supervised MLP_VAE_plain preprocess Graphsage_Encoder GCN_generator 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 Graph_generator_LSTM compute_mmd compute_kernel train_rnn_forward_epoch test_mlp_epoch train_nll train_mlp_forward_epoch test_vae_partial_epoch train_vae_epoch train_graph_completion train_mlp_epoch train_rnn_epoch test_rnn_epoch test_vae_epoch test_mlp_partial_simple_epoch train test_mlp_partial_epoch draw_graph_list save_graph_list pick_connected_component caveman_special pick_connected_component_new perturb export_graphs_to_txt decode_graph n_community get_graph load_graph_list test_perturbed perturb_new snap_txt_output_to_nx draw_graph citeseer_ego imsave save_prediction_histogram 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 emd kernel_parallel_worker l2 gaussian compute_mmd disc gaussian_emd test kernel_parallel_unpacked compute_emd clustering_worker degree_worker degree_stats edge_list_reindexed clustering_stats orca orbit_stats_all add_tensor motif_stats find_nearest_idx Args 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 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 CNN_decoder GRU_plain gumbel_softmax CNN_decoder_share GCN_decoder sample_sigmoid GCN_encoder LSTM_plain CNN_decoder_attention calc_graph_embedding MLP_token_plain sample_sigmoid_supervised MLP_VAE_plain preprocess Graphsage_Encoder GCN_generator 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 Graph_generator_LSTM compute_mmd compute_kernel train_rnn_forward_epoch test_mlp_epoch train_nll train_mlp_forward_epoch test_vae_partial_epoch train_vae_epoch train_graph_completion train_mlp_epoch train_rnn_epoch test_rnn_epoch test_vae_epoch test_mlp_partial_simple_epoch train test_mlp_partial_epoch draw_graph_list save_graph_list pick_connected_component caveman_special pick_connected_component_new perturb export_graphs_to_txt decode_graph n_community get_graph load_graph_list test_perturbed perturb_new snap_txt_output_to_nx draw_graph citeseer_ego imsave save_prediction_histogram 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 emd kernel_parallel_worker l2 gaussian compute_mmd disc gaussian_emd test kernel_parallel_unpacked compute_emd clustering_worker degree_worker degree_stats edge_list_reindexed clustering_stats orca orbit_stats_all add_tensor motif_stats find_nearest_idx Args 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 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 CNN_decoder GRU_plain gumbel_softmax CNN_decoder_share GCN_decoder sample_sigmoid GCN_encoder LSTM_plain CNN_decoder_attention calc_graph_embedding MLP_token_plain sample_sigmoid_supervised MLP_VAE_plain preprocess Graphsage_Encoder GCN_generator 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 Graph_generator_LSTM compute_mmd compute_kernel train_rnn_forward_epoch test_mlp_epoch train_nll train_mlp_forward_epoch test_vae_partial_epoch train_vae_epoch train_graph_completion train_mlp_epoch train_rnn_epoch test_rnn_epoch test_vae_epoch test_mlp_partial_simple_epoch train test_mlp_partial_epoch draw_graph_list save_graph_list pick_connected_component caveman_special pick_connected_component_new perturb export_graphs_to_txt decode_graph n_community get_graph load_graph_list test_perturbed perturb_new snap_txt_output_to_nx draw_graph citeseer_ego imsave save_prediction_histogram 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 emd kernel_parallel_worker l2 gaussian compute_mmd disc gaussian_emd test kernel_parallel_unpacked compute_emd clustering_worker degree_worker degree_stats edge_list_reindexed clustering_stats orca orbit_stats_all add_tensor motif_stats argmin number_of_nodes caveman_special ladder_graph barabasi_albert_graph perturb_new max connected_component_subgraphs grid_2d_graph append range balanced_tree Graph_load_batch shuffle choice startswith Graph_load int print convert_node_labels_to_integers n_community ego_graph 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 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 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 data all size sigmoid any float cuda range size sigmoid any float 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 size repeat view compute_kernel data zero_grad num_layers binary_cross_entropy_weight cuda max exp tolist epochs sum hidden_size_rnn format pack_padded_sequence mean log_value graph_type float rnn enumerate backward print sort batch_ratio min output sigmoid index_select pow fname train step init_hidden data int output max_num_node sigmoid decode_adj eval sample_sigmoid long numpy get_graph append range cuda init_hidden rnn data cuda max_num_node get_graph append range sample_sigmoid_supervised size decode_adj eval float long rnn enumerate int print output sigmoid numpy init_hidden zero_grad num_layers binary_cross_entropy_weight cuda max tolist step hidden_size_rnn format pack_padded_sequence log_value graph_type float rnn enumerate backward print sort batch_ratio output sigmoid index_select fname train epochs init_hidden data int output max_num_node sigmoid decode_adj eval sample_sigmoid long numpy get_graph append range cuda init_hidden rnn data cuda max_num_node get_graph append range sample_sigmoid_supervised size decode_adj eval float long rnn enumerate int print output sigmoid numpy init_hidden data cuda max_num_node get_graph append range size decode_adj eval float long rnn enumerate int print output sigmoid sample_sigmoid_supervised_simple numpy init_hidden zero_grad num_layers cuda max tolist hidden_size_rnn range format pack_padded_sequence size log_value graph_type float rnn enumerate print sort batch_ratio min output sigmoid index_select fname train epochs init_hidden data zero_grad num_layers binary_cross_entropy_weight max cuda view tolist epochs bincount sum hidden_size_rnn range cat format LongTensor pack_padded_sequence size log_value graph_type float rnn enumerate backward print sort batch_ratio extend output sigmoid index_select fname train step array init_hidden len int append min output max_num_node decode_adj eval sample_sigmoid cat long max_prev_node numpy get_graph range cuda init_hidden rnn data zero_grad num_layers binary_cross_entropy_weight max cuda view tolist bincount sum hidden_size_rnn range cat format LongTensor pack_padded_sequence size log_value graph_type float rnn enumerate print sort batch_ratio extend output sigmoid index_select fname train epochs array init_hidden len timing_save_path MultiStepLR model_save_path fname_pred train_rnn_epoch test_rnn_epoch test_vae_epoch save str list test_mlp_epoch graph_save_path Adam load_state_dict range state_dict format load_epoch lr load time print extend train_vae_epoch fname parameters train_mlp_epoch zeros epochs save_graph_list load str format graph_save_path print test_vae_partial_epoch load_epoch model_save_path fname fname_pred load_state_dict test_mlp_partial_simple_epoch range save_graph_list load str format print note load_epoch model_save_path fname graph_type load_state_dict nll_save_path 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 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 connected_component_subgraphs selfloop_edges pick_connected_component_new convert_node_labels_to_integers remove_edges_from max range len 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 model float backward zero_grad step cuda 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 hstack astype float max len norm emd hstack astype float max len norm print array hstack max len print compute_mmd now append range array degree_histogram len list histogram values list print compute_mmd now histogram append range values len dict nodes append edges str remove number_of_nodes check_output strip write close len edge_list_reindexed number_of_edges find array open number_of_nodes compute_mmd orca append sum number_of_nodes print compute_mmd orca append sum array len | # GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Model This repository is the official PyTorch implementation of GraphRNN, a graph generative model using auto-regressive model. [Jiaxuan You](https://cs.stanford.edu/~jiaxuan/)\*, [Rex Ying](https://cs.stanford.edu/people/rexy/)\*, [Xiang Ren](http://www-bcf.usc.edu/~xiangren/), [William L. Hamilton](https://stanford.edu/~wleif/), [Jure Leskovec](https://cs.stanford.edu/people/jure/index.html), [GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Model](https://arxiv.org/abs/1802.08773) (ICML 2018) ## Installation Install PyTorch following the instuctions on the [official website](https://pytorch.org/). The code has been tested over PyTorch 0.2.0 and 0.4.0 versions. ```bash conda install pytorch torchvision cuda90 -c pytorch ``` Then install the other dependencies. ```bash | 3,680 |
snap-stanford/masa | ['time series'] | ['MASA: Motif-Aware State Assignment in Noisy Time Series Data'] | src/admm_solver.py paper_code/generateDatasets/8Constants.py scripts/cycling/cycling.py paper_code/scripts/synthetic/baseline.py paper_code/scripts/synthetic/scalabilityTiming.py src/motif/find_motif.py paper_code/scripts/aggregation_and_plotting/aggregate.py paper_code/generateDatasets/snap.py paper_code/scripts/aggregation_and_plotting/analyze_synthetic.py paper_code/generateDatasets/generate_synthetic.py paper_code/scripts/aggregation_and_plotting/analyze_synthetic_motifs.py src/motif/test_utils.py paper_code/scripts/case_studies/runCaseStudy.py src/motif/rstr_suffix/rstr_max.zipf.py paper_code/scripts/aggregation_and_plotting/plotGammaRobustness.py scripts/cycling/create_cycling_dataset.py src/motif/rstr_suffix/tools_karkkainen_sanders.py src/motif/rstr_suffix/rstr_max.py paper_code/scripts/synthetic/syntheticPlot.py paper_code/scripts/aggregation_and_plotting/scalabilityPlot.py src/motif/hmm.py paper_code/generateDatasets/constants.py example.py scripts/cycling/cycling_evaluate.py paper_code/generateDatasets/generate_synthetic_util.py paper_code/scripts/trash/example.py paper_code/scripts/case_studies/pca_boeing.py src/CASC_helper.py CASC_solver.py paper_code/scripts/aggregation_and_plotting/results.py src/motif/rstr_suffix/rstr_max.test.py paper_code/scripts/synthetic/synthetic.py paper_code/scripts/trash/test.py src/motif/rstr_suffix/rstr_max.unittest.py CASCSolver runCASC pickleObject createDataset createSegments createCorrect genRandInv generate_data getMeanCov genInvCov generate_inverse TFlt_GetPrcStr iterhashset LoadConnList TBPGraph_New TMOut itervec TNEANetAIntI PlotShortPathDistr_PNEANet GetNodeTriads_PNGraph GenCircle_PUNGraph LoadEdgeListNet TSStr GetTriangleCnt_PDirNet DrawGViz DelSelfEdges_PUNGraph IsWeaklyConn_PUndirNet GetTreeSig_PUNGraph ToGraph_PUNGraph TInt_GetMn GetCmnNbrs GetDegCnt GetDegreeCentr GetSccSzCnt_PUndirNet TCh_IsHex GetKCoreEdges_PUNGraph GetMxScc_PDirNet DrawGViz_PUNGraph TNEGraph_New TBool_GetStr TInt_GetInRng GetMxBiCon_PUNGraph GenRndGnm_PUndirNet TExcept_ThrowFull GenCircle_PDirNet DelZeroDegNodes_PUndirNet TStr_GetNrNumFExt GetBfsEffDiam_PUNGraph IsConnected_PUndirNet TCliqueOverlap_CalculateOverlapMtx ConvertGraph_PNEANet_PUNGraph PlotInDegDistr GetOutDegCnt_PUndirNet GetTreeRootNId_PUndirNet PrintGraphStatTable_PUNGraph 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 Edges GetHits GetKCoreEdges GetClustCf_PNEANet TNEANetAFltI PlotKCoreEdges_PUNGraph GetKCoreEdges_PUndirNet SaveGViz_PUndirNet GetEdgesInOut_PNEANet TStrUtil_SplitWords PTable TChA_LoadTxt GetMxWccSz_PNGraph TNotify_OnNotify CntUniqUndirEdges_PDirNet CntUniqDirEdges GetPageRankMP_PNEANet TStr_Base64Decode ConvertESubGraph_PNGraph_PNEANet PlotKCoreNodes_PNEANet ReebSimplify PlotOutDegDistr_PNGraph GetMxInDegNId GenBaraHierar_PNEANet MxDegree_PNEANet TestAnf_PDirNet GetEigenVectorCentr TSFlt TRowIteratorWithRemove GetWccs_PDirNet GenRMatEpinions GetNodeClustCf_PDirNet PNEANet GetKCoreEdges_PNGraph TUNGraphEdgeI GetClustCf_PUndirNet TRStr_CmpI MxDegree_PUNGraph TDirNet_New 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 print_array ToGraphMP ToNetwork GetMxScc_PUNGraph GetMxSccSz_PDirNet EventImportance TTable_LoadSS GenTree_PUNGraph LoadConnList_PDirNet GetGroupClosenessCentr TStrUtil_SplitLines DelNodes_PNEANet GetMxWcc_PUNGraph TGUtil GetNodesAtHop_PNEANet LoadEdgeList_PUndirNet TFlt PlotOutDegDistr_PDirNet MaxCPGreedyBetter1 GetTreeRootNId_PDirNet LoadConnListStr GetRndSubGraph_PNEANet 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 TExcept_GetOnExceptF GetTriadEdges_PNEANet PlotKCoreEdges LoadMode SaveMatlabSparseMtx_PNGraph GetDegSeqV_PDirNet IsTree getitem_hashset TUndirFFire TAGMUtil_GetNodeMembership PlotInDegDistr_PNGraph GetMxSccSz_PNGraph ToGraphMP3_PNGraphMP PercentMxScc_PNEANet TFlt_GetMx GetTriadEdges GetBfsFullDiam TInt_SaveFrugalInt TAGMUtil_SaveGephi PercentDegree_PUNGraph TUNGraph_New CntUniqDirEdges_PDirNet GetKCoreNodes TStrUtil_GetTmFromStr GetInvParticipRatEig GetPageRank_PDirNet CmtyEvolutionJson PlotEigValDistr GetNodeEcc IterHash TStrHashF_Md5_GetSecHashCd 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 PyToTIntV GetRndESubGraph_PNGraph GetNodesAtHops_PNEANet PNEANet_New SaveEdgeList_PUndirNet GetModularity_PUndirNet TNGraphMP_Load TVoid TAscFlt PrintGraphStatTable_PDirNet TNEANetMP_Load GetHitsMP_PUndirNet ReebRefine CntNonZNodes_PUNGraph PercentMxWcc_PDirNet GetRndSubGraph_PUNGraph TStr_GetStr GetTriadParticip_PUNGraph GetWeightedBetweennessCentr TIntPrFltH TStr_AddToFMid PlotSccDistr_PNEANet CntDegNodes_PDirNet CntDegNodes_PNGraph TBPGraph_GetSmallGraph WarnNotify GetId TGUtil_MakeExpBins GetKCoreNodes_PUNGraph TIntStrH PlotHops_PDirNet GetNodeTriads_PUndirNet TInt_Abs SavePajek_PUndirNet LoadPajek_PUNGraph GetPageRank GetRndESubGraph_PNEANet TNotify ConvertESubGraph_PUndirNet_PNEANet PercentDegree_PUndirNet CntDegNodes_PUNGraph GetAnfEffDiam TCrossNet ConvertSubGraph_PDirNet_PDirNet TChAIn_New GetNodeClustCf_PNGraph AddSelfEdges_PNGraph GetNodesAtHop_PUNGraph GetModularity TSIn GetLen2Paths IsConnected GenStar_PNGraph TTable_GetFltNodePropertyTable MMNodes PlotInvParticipRat GetKCoreNodes_PNEANet TCliqueOverlap_Intersection TCliqueOverlap_GetIntersection TUNGraph IsTree_PDirNet TTableRow TTable_GetMapPageRank PlotKCoreEdges_PDirNet ToNetworkMP_PNEANetMP CntInDegNodes_PUNGraph TAGMUtil_GetNbhCom GetTriadParticip_PDirNet GetWccSzCnt_PUndirNet TPairHashImpl1_GetHashCd TIntFltHI GetRndESubGraph_PUNGraph GenStar_PNEANet MaxCPGreedyBetter2 TNEANetMP LoadEdgeListStr_PUNGraph Get1CnCom GetFarnessCentr_PNEANet TFltPrV_GetV ConvertSubGraph_PDirNet_PUNGraph Schema_GetV TMMNet_Load PlotClustCf_PUndirNet GetMxInDegNId_PNEANet ConvertESubGraph_PNEANet_PNEANet CntInDegNodes_PNEANet GetShortPath_PNGraph CalcAvgDiamPdf TStrHashF_DJB_GetPrimHashCd _swig_setattr_nondynamic_method TNotify_OnLn GetNodeInDegV_PDirNet IsConnected_PNEANet GetBfsTree PyTFltV IsWeaklyConn TNEANet_Load_V1 GetSccSzCnt_PNEANet TAGMUtil_SaveBipartiteGephi GetKCore_PDirNet GetCmnNbrs_PDirNet ConvertGraph_PNGraph_PUNGraph TSInOut ConvertGraph_PNGraph_PNGraph CntUniqUndirEdges_PNEANet GetMxOutDegNId_PUNGraph TRnd_GetNrmDevStep GenBaraHierar PlotClustCf_PDirNet GetEigVals GetTriadEdges_PUndirNet GetPageRank_v1_PUNGraph TFIn_New len_hash GroupStmt TFile_Del GetPageRankMP_PNGraph TTable_GetMP PrintInfo_PUNGraph GetWccSzCnt PercentMxScc_PDirNet GetEdgesInOut_PUndirNet GetTriangleCnt_PUNGraph TCrossNetAStrI TAGMUtil_GenCmtyVVFromPL TExcept GenGrid_PDirNet SaveGViz_PNGraph TBPGraph_Load TTable_GetMapHitsIterator CntNonZNodes_PNEANet MakeUnDir_PDirNet CntEdgesToSet PercentMxWcc_PUndirNet GetBfsTree_PNEANet ConvertSubGraph_PNEANet_PUNGraph GenFull_PNEANet TStr_GetNrAbsFPath PrintGraphStatTable_PNGraphMP TFltRect CntSelfEdges_PDirNet GetMxScc_PUndirNet NodesGTEDegree TUInt64 PlotShortPathDistr_PUNGraph MxSccSz TIntH GetSubGraph_PDirNet ExeStop GetHits_PNEANet GetMxOutDegNId_PNGraph TStdIn_New GetSccSzCnt TStrPool_New CntSelfEdges TStrUtil_GetAddWIdV TStr_PutFBaseIfEmpty TIntTrV_SwapI GenTree GetInDegCnt_PDirNet TDirNetNodeI LoadCrossNetToNet GetNodesAtHops_PUndirNet PercentDegree_PNEANet GetMxSccSz_PNEANet GetMxOutDegNId TModeNetNodeI GetTriads_PUNGraph GetBfsEffDiam_PNGraph TStr_Fmt PercentMxScc_PNGraph CntOutDegNodes_PUndirNet TStrUtil_IsLatinStr GetNodeTriads_PDirNet GenCopyModel GetNodeOutDegV_PNGraph GetNodeEcc_PUNGraph TIntTr TNEANet TMemIn GetBfsTree_PDirNet LoadEdgeList_PNEANet WrNotify PrintGraphStatTable_PNEANet GetMxDegNId_PNGraph GetWeightedClosenessCentr GetTriangleCnt_PNGraph GetDegSeqV TFIn LoadEdgeListStr GenForestFire TPredicate_EvalStrAtom DrawGViz_PNGraph TMem_LoadMem TIntTrV TFlt_GetKiloStr CntEdgesToSet_PUNGraph CntOutDegNodes_PNGraph TPairHashImpl2_GetHashCd TUndirNetNodeI GetMxDegNId_PUNGraph PercentMxScc_PUNGraph TAGMUtil GetMxDegNId_PDirNet GetSubGraph_PUNGraph TFlt_Round TStopwatch_GetInstance CntUniqBiDirEdges_PUNGraph GenConfModel len_vec TestAnf_PUNGraph PercentMxWcc_PUNGraph CntUniqBiDirEdges TNotify_OnTxt GetTreeSig_PUndirNet Schema GetInDegCnt TStr_GetNrFPath TStrHashF_Murmur3_GetPrimHashCd TAGMFit GetTriadEdges_PUNGraph GetUnDir TGUtil_GetPdf TUInt_GetStr LoadEdgeListStr_PNEANet ConvertSubGraph_PUNGraph_PUNGraph GenGrid_PUndirNet ToNetworkMP2_PNEANetMP TTable TIntIntH GetOutDegCnt_PUNGraph IsTree_PUNGraph TNGraphMP_GetSmallGraph TPairHashImpl1 GetTriadParticip_PNEANet CntUniqDirEdges_PUNGraph TCh_IsAlNum TStrPool TAGM_GenAGM TFlt_GetMn TExcept_IsOnExceptF PlotKCoreNodes_PUndirNet GetRndESubGraph_PUndirNet GetMxScc_PNGraph GetHitsMP_PDirNet TAGMFast CntUniqBiDirEdges_PDirNet GetNodeClustCf TFfGGen_GenFFGraphs TAGMUtil_DumpCmtyVV IsWeaklyConn_PUNGraph GetInDegCnt_PNGraph PlotClustCf_PUNGraph PNEANetV_GetV GetNodeOutDegV_PUNGraph CntUniqUndirEdges_PNGraph TUInt_IsIpv6Str SaveMatlabSparseMtx_PNEANet TNEANet_New LoadDyNet GetMxWccSz_PUNGraph SaveEdgeListNet PlotWccDistr_PNEANet TInt_GetRnd TCnComV GetDegCnt_PDirNet TIntPr GetClosenessCentr_PUNGraph TStrUtil_CountWords ConvertSubGraph_PUndirNet_PNEANet GetFarnessCentr_PUNGraph TFltPrV_SwapI TStrIn_New GetMxDegNId PlotSccDistr_PUndirNet TBool_GetRnd GetNodesAtHops_PUNGraph GetBfsEffDiam_PDirNet GetDegCnt_PUndirNet MakeUnDir TMMNet GetCmnNbrs_PNEANet GetWccSzCnt_PNGraph GetShortPath GetDegSeqV_PNEANet ConvertGraph_PDirNet_PUNGraph TUndirNet TNGraphMPNodeI TNGraphMP_New CntOutDegNodes_PUNGraph TUNGraphMtx TMIn ConvertSubGraph_PNEANet_PNEANet TCallbackNotify_New CmtyTest TStrUtil_GetXmlTagNmVal TNEANetNodeI GetMxBiCon GetSubGraph_PUndirNet 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 GetBfsFullDiam_PUNGraph TCh_GetNum GetWccs_PUndirNet InfomapOnline StatNotify TLnRet TStr_GetSpaceStr PlotShortPathDistr PlotOutDegDistr_PUNGraph TUInt_GetRnd TUndirNetEdgeI DelDegKNodes_PDirNet GenRewire GetTriadParticip_PUndirNet DelSelfEdges_PNEANet GetSccs_PNEANet LoadConnList_PUNGraph GetNodesAtHop_PUndirNet TNEGraph LoadEdgeList getitem_hash TIntHI LoadCrossNet Schema_SwapI TCh_IsUc TAGM TCh_IsNum GetBetweennessCentr GetSngVals TMemOut_New TSInt GetMxOutDegNId_PDirNet GetBfsTree_PUNGraph GetAnfEffDiam_PNGraph GetWccSzCnt_PNEANet GetEdgesInOut_PNGraph GetMxOutDegNId_PUndirNet node2vec TMemIn_New GenGrid_PNEANet TCnCom_SaveTxt TNullNotify GetTreeRootNId GetWccSzCnt_PUNGraph GetModularity_PDirNet GetInDegCnt_PNEANet TUNGraphNodeI TFlt_Abs PlotEigValRank PNEANetV_SwapI GetESubGraph_PNEANet PlotShortPathDistr_PDirNet GetBiConSzCnt PlotOutDegDistr_PUndirNet LoadConnListStr_PDirNet TIntPrV_SwapI Save GetMxInDegNId_PDirNet TNativeCallbackNotify_New ConvertGraph_PUndirNet_PNGraph CntDegNodes GetOutEdges GetNodeOutDegV_PNEANet TCrossNetAFltI Intersect1 LoadEdgeListStr_PDirNet TIntFltKd TNEANet_Load TStrUtil_GetNormalizedUrl CntNonZNodes_PNGraph GetModularity_PNEANet GetNodeEcc_PNEANet TNGraphEdgeI ToNetworkMP TStrHashF_DJB_GetSecHashCd TTable_TableFromHashMap DelZeroDegNodes_PNEANet NodesGTEDegree_PUNGraph ErrNotify LoadPajek_PNEANet GetAnf_PNGraph TDbStr ToNetwork_PNEANet LoadConnList_PNGraph GetPageRank_v1_PNEANet PrintInfo_PNGraph SaveGViz TDirNet_Load_V1 TDirNet_GetSmallGraph TNGraphMPEdgeI GetRndSubGraph_PUndirNet GetTriangleCnt_PNEANet TChRet CalcEffDiam TAGMUtil_GetConductance GetKCore_PUndirNet TStrPool64_New TModeNetEdgeI TIntVToPy GetNodeInDegV_PUNGraph GetHits_PUndirNet GetNodeOutDegV TMMNetModeNetI GenTree_PDirNet TInt_IsEven TFile_DelWc TNGraphMtx GenRndGnm_PUNGraph TIntFltH GetTreeSig_PDirNet TMOut_New TStrUtil_SplitSentences GetBfsTree_PUndirNet TNEANetMP_New TGUtil_GetCCdf TInt_GetMx TLFlt_GetStr TUNGraph_GetSmallGraph GetBfsFullDiam_PNEANet GetMxInDegNId_PUNGraph GetCmnNbrs_PNGraph CntDegNodes_PUndirNet TChAIn GetModularity_PNGraph GetShortPath_PUNGraph TNullNotify_New TStr_GetFNmStr TUInt64_GetMegaStr GetPageRank_v1_PNGraph CntInDegNodes_PDirNet MakeUnDir_PUndirNet GetTriangleCnt GetBfsFullDiam_PDirNet TInt_TestFrugalInt GenTree_PNGraph TStr_PutFExtIfEmpty GetArtPoints TCliqueOverlap_GetMaxCliques PlotWccDistr_PUndirNet TNGraph Nodes GetRndESubGraph_PDirNet LoadPajek_PUndirNet TCliqueOverlap_GetOverlapCliques GetUnDir_PDirNet TIntFltKdV TLogNotify_New TStdNotify_New TMem_New GetMxSccSz ConvertGraph_PUNGraph_PNEANet GenFull_PNGraph GetMxInDegNId_PUndirNet ConvertESubGraph_PDirNet_PNEANet NodesGTEDegree_PNEANet AddSelfEdges_PUndirNet CntUniqBiDirEdges_PUndirNet PNEANetV GetTriads_PDirNet ConvertSubGraph_PUNGraph_PNGraph TNEANetAStrI TUInt_GetMegaStr ConvertGraph_PNEANet_PNGraph TStrUtil_SplitOnCh TConv_Pt64Ints32 TLFlt TStr_GetNrFNm DelZeroDegNodes DelDegKNodes_PUNGraph PercentDegree_PNGraph TFltV_SwapI TUndirNet_GetSmallGraph GetBfsEffDiam_PUndirNet CntUniqUndirEdges_PUndirNet TDirNetEdgeI CntEdgesToSet_PNGraph TInt_Sign GetPageRank_PUndirNet TArtPointVisitor GenPrefAttach GetUnDir_PNEANet PlotHops_PNGraph CntEdgesToSet_PUndirNet MxWccSz CntOutDegNodes_PNEANet PNEANetMP TUNGraph_Load PNGraphMP TUnionFind PDirNet_New SaveEdgeList_PNGraph PlotWccDistr_PDirNet TFile DelDegKNodes_PNEANet GenCircle_PUndirNet GenRndPowerLaw AddSelfEdges_PUNGraph GetNodeOutDegV_PDirNet GetBetweennessCentr_PNEANet TStr_Base64Encode delitem_hash TTable_Load ConvertGraph_PNEANet_PNEANet delitem_hashset TStrIntSH GenBaraHierar_PDirNet TDirNet GetSccs_PUndirNet TAGMUtil_ConnectCmtyVV TUCh TCrossNetAIntI GetAnfEffDiam_PUNGraph TCh_GetHexCh TDirNet_Load GetMxWcc_PNEANet GetInDegCnt_PUNGraph GetNodeWcc_PNEANet PlotShortPathDistr_PUndirNet GetSubGraph_PNEANet ToGraphMP_PNGraphMP GetWccs TStr_IsAbsFPath TestAnf_PUndirNet TIntStrHI GetNodeWcc_PNGraph TBigStrPool IsTree_PNGraph TRnd_LoadTxt GetPageRankMP_PUNGraph TBool_GetValFromStr GetNodeEcc_PDirNet TAtomicPredicate TStdIn GetRndWalkRestart_PNEANet TFlt_GetStr GetRndWalkRestart_PNGraph TStrUtil_GetStdNameV 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 TIntIntVV_GetV GenFull_PUNGraph TStrTAttrPr GetSccSzCnt_PDirNet TBool_Get01Str TForestFire TestAnf_PNGraph GetHitsMP_PUNGraph PlotWccDistr GetMxSccSz_PUndirNet GetWeightedPageRank GetWccSzCnt_PDirNet TInt swig_import_helper TTable_New Infomap GenSmallWorld GetClosenessCentr_PNEANet GetUnDir_PUndirNet TFlt_Eq6 GenCircle GetClosenessCentr TMMNet_New GetTriangleCnt_PUndirNet DelDegKNodes_PUndirNet GetBetweennessCentr_PUNGraph GenStar TMMNetCrossNetI GetTriads CntUniqBiDirEdges_PNGraph SavePajek_PNGraph GenStar_PDirNet TCnCom GetSccSzCnt_PUNGraph TUndirNet_Load GetKCore GetNodeClustCf_PUndirNet GetKCore_PUNGraph AddSelfEdges PercentMxWcc_PNEANet GetSubGraph_PNGraph TCliqueOverlap_GetCPMCommunities GetWccs_PNGraph TTable_GetEdgeTablePN GenCircle_PNGraph GetTriadParticip_PNGraph TStrHashF_OldGLib_GetSecHashCd GenRndGnm_PNEANet TUndirNet_New GetESubGraph ConvertGraph GetEdgesInOut_PUNGraph GetOutDegCnt TIntV TStrIntH CommunityCNM TSOut NodesGTEDegree_PNGraph PrintInfo TTable_NormalizeColName GetKCoreEdges_PDirNet TIntFltKdV_GetV PMMNet_New TRnd_GetUniDevStep CmtyEvolutionFileBatch PlotOutDegDistr_PNEANet PlotHops_PUndirNet PlotWccDistr_PUNGraph GetSccs_PUNGraph TStr_GetNumFNm TMIn_New PlotClustCf_PNGraph GetTreeRootNId_PUNGraph GetSccs_PNGraph TFile_Rename PlotInDegDistr_PDirNet GetMxWcc_PUndirNet PlotInDegDistr_PUndirNet GetNodeEcc_PUndirNet TInt_GetMegaStr GenGeoPrefAttach ToGraph LoadEdgeList_PNGraph GetRndSubGraph_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 TBigStrPool_Load DrawGViz_PNEANet GenRndGnm_PNGraph GetAnf_PUndirNet GetSubTreeSz GetTriadParticip TCliqueOverlap GetTreeSig TStrHashF_Murmur3_GetSecHashCd ConvertSubGraph_PNGraph_PNGraph TFlt_Sign DelZeroDegNodes_PNGraph TInt_LoadFrugalIntV GetMxWccSz_PNEANet GetNodesAtHop_PDirNet GetHitsMP_PNEANet ConvertGraph_PUNGraph_PUNGraph LoadConnListStr_PNGraph TStrUtil_GetShorStr DelSelfEdges_PNGraph GetRndWalkRestart_PUNGraph GenDegSeq SaveMatlabSparseMtx PlotInDegDistr_PUNGraph GetWeightedPageRankMP TStrV_SwapI LoadPajek_PNGraph GetMxBiCon_PNEANet GetSccSzCnt_PNGraph CntDegNodes_PNEANet TExcept_New GetModularity_PUNGraph _swig_getattr GenRndGnm AddSelfEdges_PNEANet TNGraph_Load TTable_SetMP TSBase GenBaraHierar_PUNGraph TStrUtil GetWeightedShortestPath SaveMatlabSparseMtx_PUNGraph TIntSet Empty PercentMxScc TStrUtil_RemoveHtmlTags CntSelfEdges_PNGraph PNGraphMP_New CntNonZNodes_PUndirNet IsWeaklyConn_PDirNet TTable_GetEdgeTable TIntHSI GetKCoreNodes_PUndirNet PlotKCoreNodes CntNonZNodes DelNodes_PNGraph TStr_LoadTxt PercentMxScc_PUndirNet PUndirNet TFltV_GetV GetEgonet GetNodesAtHop_PNGraph GetSubTreeSz_PDirNet LoadPajek_PDirNet TStr_GetNrFExt TCh_IsHashCh GetNodesAtHop TStrV GetBfsFullDiam_PNGraph TStrUtil_GetXmlTagNmVal2 GetSccs_PDirNet CntOutDegNodes GetMxScc_PNEANet TCh_IsWs TStdNotify CntUniqDirEdges_PNGraph TIntIntHI GetPageRank_PNGraph TIntIntVV_SwapI TRowIterator GetClustCf_PUNGraph GetFlagStr ConvertGraph_PUndirNet_PNEANet MxDegree_PUndirNet IsTree_PNEANet TBool_IsValStr CntUniqUndirEdges GetOutDegCnt_PNGraph PlotSngValRank GetHits_PNGraph GetOutDegCnt_PNEANet GVizGetLayoutStr GenBaraHierar_PNGraph TNativeCallbackNotify TIntIntVV PlotSccDistr_PNGraph GetTriadEdges_PDirNet LoadConnList_PUndirNet GetMxBiCon_PNGraph GetDegCnt_PNGraph 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 TUInt64_GetKiloStr TAGMUtil_RewireCmtyVV TIntFltKdV_SwapI GetSubTreeSz_PNEANet TNotify_OnStatus GetPageRankMP_PUndirNet TCliqueOverlap_GetRelativeComplement TUInt GetNodesAtHops_PDirNet PlotKCoreEdges_PNEANet MxDegree_PDirNet PlotInDegDistr_PNEANet DrawGViz_PDirNet PlotKCoreNodes_PDirNet PMMNet getitem_vec GetAnf_PDirNet TAGM_RndConnectInsideCommunity GetSccs CntUniqBiDirEdges_PNEANet TNGraphMP setitem_vec GetClustCf_PNGraph TNEANetMPNodeI TBPGraph _swig_setattr PNGraph_New delitem_vec TStopwatch GenRMat ConvertESubGraph_PUNGraph_PNEANet TNGraph_New TStrIntPrV_GetV GetPageRank_v1_PUndirNet GetInDegCnt_PUndirNet TCh NodesGTEDegree_PDirNet GenGrid TBool_GetYesNoStr SaveMatlabSparseMtx_PUndirNet ConvertGraph_PNGraph_PNEANet CntEdgesToSet_PNEANet GetBetweennessCentr_PNGraph GetCommon PercentDegree_PDirNet TStrPool64 TStr_PutFExt PlotShortPathDistr_PNGraph TAGMUtil_Intersection SaveEdgeList SaveEdgeList_PNEANet GetRndSubGraph contains_hashset TIntSet_GetSet ConvertSubGraph_PNEANet_PNGraph TStrIntPrV DrawGViz_PUndirNet GetKCoreNodes_PDirNet TMemOut GetUnDir_PNGraph PlotKCoreEdges_PNGraph DelNodes TStr DelDegKNodes_PNGraph TStrV_GetV GetAnf CmtyGirvanNewmanStep PlotKCoreEdges_PUndirNet GenRndBipart TUInt_GetStrFromIpUInt IsWeaklyConn_PNGraph TBiConVisitor GetTreeSig_PNEANet TestAnf GetCmnNbrs_PUNGraph GetClustCf GetHitsMP TStr_GetNullStr LoadEdgeListStr_PNGraph AddSelfEdges_PDirNet GetTreeRootNId_PNEANet IsConnected_PNGraph SaveMatlabSparseMtx_PDirNet PlotHops LoadEdgeList_PUNGraph IterVec TPrGraph GetKCore_PNGraph TAGMUtil_LoadCmtyVV GetWccs_PUNGraph TBool GenTree_PNEANet IsConnected_PUNGraph GenFull_PUndirNet LoadEdgeList_PDirNet TRStr_GetNullRStr TCrossNetEdgeI PrintInfo_PUndirNet GetKCoreEdges_PNEANet PercentMxWcc_PNGraph setitem_hash TCnComV_SwapI GetPageRankMP GetNodeInDegV_PNGraph CntInDegNodes_PNGraph SaveEdgeList_PDirNet GetMxWcc_PDirNet PUNGraph SaveGViz_PNEANet PrintGraphStatTable_PNEANetMP PrintInfo_PNEANet TCh_GetUc GetInEdges PrintGraphStatTable_PUndirNet DelZeroDegNodes_PUNGraph GetMxWccSz_PDirNet GetEigVec IsWeaklyConn_PNEANet TTable_NormalizeColNameV ToGraphMP3 TStdOut_New TStrUtil_GetDomNm2 GetBfsEffDiam TStrPool_Load TStrUtil_GetStdName TInt_LoadFrugalInt TFile_GetUniqueFNm TNotify_GetTypeStr GetTreeSig_PNGraph GetTriadEdges_PNGraph GetTriads_PNGraph TFltV PNGraph IsConnected_PDirNet GetMxDegNId_PNEANet DelNodes_PUndirNet TRnd_GetExpDevStep TAttrPair GetFarnessCentr SaveToErrLog GenCircle_PNEANet TExcept_PutOnExceptF GetClustCf_PDirNet TPrimitive GetHits_PDirNet PDirNet GetNodeInDegV PrintInfo_PDirNet GetEdgesInOut_PDirNet TGUtil_Normalize GetEdgesInOut TStrIntHI LoadNodeList TInt_GetHexStr CmtyEvolutionFileBatchV LoadConnListStr_PUNGraph DelNodes_PUNGraph TIntV_SwapI TLogRegPredict TCh_GetUsFromYuAscii GetNodesAtHops TCh_GetStr GetBfsFullDiam_PUndirNet CntUniqUndirEdges_PUNGraph TAGMFastUtil TNGraph_GetSmallGraph GetRndESubGraph GetNodeOutDegV_PUndirNet MxDegree count PTable_New TTableIterator GetAnf_PNEANet GetShortPath_PUndirNet TRnd DelZeroDegNodes_PDirNet TUInt_GetKiloStr TCallbackNotify CntSelfEdges_PUNGraph PlotSccDistr_PUNGraph GetCmnNbrs_PUndirNet TGUtil_GetCdf GetMxWccSz GetMxBiCon_PUndirNet GetDegCnt_PNEANet GetSubTreeSz_PUndirNet TStdErrNotify_New TAttr GetClosenessCentr_PNGraph TestAnf_PNEANet 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 TStrHashF_Murmur3 TCs_GetCsFromBf TUInt64_GetHexStr TRStr iterhash GetKCore_PNEANet ConvertSubGraph_PUndirNet_PUndirNet CntNonZNodes_PDirNet Clr GetNodeClustCf_PUNGraph TPairHashImpl2 GetShortPath_PDirNet ConvertSubGraph_PUndirNet_PNGraph TStrUtil_GetCleanWrdStr TNGraphNodeI TIntStrPr GetBfsEffDiam_PNEANet TIntTrV_GetV GetSubGraph TInt_SaveFrugalIntV GetKCoreNodes_PNGraph TBigStrPool_New GetMxScc CntOutDegNodes_PDirNet TStdErrNotify 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 TNEGraph_Load GetPageRank_PUNGraph TSOutMnp GetNodeClustCf_PNEANet TUInt_GetUIntFromIpStr GetSngVec GetAnfEffDiam_PNEANet GetEdgeBridges GetMxWcc_PNGraph GenTree_PUndirNet MaxCPGreedyBetter3 GetRndSubGraph_PDirNet TFlt_GetGigaStr TFOut_New TLogRegPredict_Load GetAnf_PUNGraph PlotClustCf_PNEANet GetOutDegCnt_PDirNet ToGraph_PUndirNet SavePajek ToGraph_PDirNet MxDegree_PNGraph GetShortPath_PNEANet TTable_GetNodeTable DelNodes_PDirNet TStrUtil_GetCleanStr LoadPajek PrintGraphStatTable TStrHashF_OldGLib_GetPrimHashCd TLogRegFit TInt_Swap TIntPrV LoadConnListStr_PUndirNet GetMxWcc TNEANetEdgeI CommunityGirvanNewman GetTriads_PNEANet GetMxBiCon_PDirNet TLogNotify MakeUnDir_PNGraph TPredicateNode TFlt_GetRnd PlotHops_PNEANet GetBiCon GetPageRank_v1 TStr_PutFBase GetTreeRootNId_PNGraph ToGraph_PNGraph getMask performMapping plotTable getFullMapping getMappingDists plot_confusion_matrix getAssigns getValidMappings getScores getMotifResult getBaselineScore getTrueResult getScores saveScores loadAndPlotScores transformChunk runHyperParameterTests runTest runBICTests makeDir runNonMotifCASC dataset pickleObject getData performBaseline runHyperParameterTests runTest runBICTests makeDir runNonMotifCASC dataset pickleObject runHyperParameterTests runTest runBICTests makeDir runNonMotifCASC dataset pickleObject getScores saveScores loadAndPlotScores runTest runNonMotifTICC runHyperParameterTests test trial countNonOverlapping pick_random_block read_block create_macro_segment pick_random_activity_block create_entire_dataset read_activity runHyperParameterTests runTest runBICTests runNonMotifTICC makeDir dataset pickleObject performMapping getFullMapping getMappingDists maskMotifSegments getAssigns getValidMappings ADMMSolver hex_to_rgb computeClusterBIC find_matching computeBIC computeF1Score_delete updateClusters compute_confusion_matrix upperToFull computeF1_macro getTrainTestSplit computeLogOdds collapse computeMotifBigramProbs inflateMotifLengths greedy_assignv2 computeBigramProbs replaceRedundancy addToLogFreqProbs computeFinalMotifScores motifWorker replaceModules PerformAssignment MotifScore filterOverlapping generateExpandedMotif find_motifs getFrequencyProbs getMotifIndepProb getGarbageCol getMotifStats HMM MotifHMM testAssign1 GenerateFakeData test GetMotifs Rstr_max Test_rstrmax_one Test_rstrmax_a iTest_rstrmax_python Test_rstrmax_one_two Test_rstrmax_test1 Test_rstrmax cmpval direct_kark_sort LCP simple_kark_sort radixpass kark_sort dump close open PerformFullCASC savetxt CleanUp CASCSolver pickleObject savetxt array range createSegments print generate_data choice range len append int T randint GenRndGnm Edges zeros zeros range randint genRandInv T min eig identity genInvCov zeros abs range T reshape inv dot zeros range multivariate_normal print getMeanCov inv savetxt zeros sum range generate_inverse lstrip get __setattr__ 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_TStrHashF_Murmur3 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 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 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 list format arange product print yticks text xlabel astype tight_layout colorbar ylabel imshow title xticks max range len show list subplots table set_visible range range len getMappingDists getMask enumerate list heapify print sort tolist heappush heappop zip append range len list getMask print getFullMapping confusion_matrix figure plot_confusion_matrix f1_score accuracy_score getAssigns range range load items list open float getTrueResult Counter show plot print xlabel ylabel getMotifResult getBaselineScore ylim figure legend append f1_score getTrueResult getValidMappings range len getScores array savetxt update show plot print xlabel ylabel ylim savefig figure legend getScores range fillna print runNonMotifCASC runHyperParameterTests makeDir runTest append runNonMotifCASC print sort makeDir makedirs print loadtxt PerformFullCASC savetxt CleanUp CASCSolver pickleObject loadtxt loadtxt KMeans GaussianHMM savetxt labels_ GaussianMixture array predict fit print write open append items list items list arange xticks subplot list get_position xlim items loadtxt int float runNonMotifTICC runHyperParameterTests runTest TICCSolver PerformFullTICC append join tolist countNonOverlapping find append str read_block range len choice choice pick_random_activity_block range pick_random_block defaultdict print PCA mean create_macro_segment savetxt std range fit_transform runNonMotifTICC runNonMotifTICC makeDir print append range len maskMotifSegments append list array choice int T asarray sqrt diagonal zeros diag tuple len argmin min shape zeros range append shape range str print savetxt zeros range zeros range int len hmean zeros sum range items list sum mean items list sum get motifReq greedy_assignv2 find_motifs all print sort apply_async computeBigramProbs shape maxMotifs collapse getGarbageCol gamma beta range enumerate len items list sort computeBigramProbs MotifScore collapse append len len SolveAndReturn MotifScore append MotifHMM log computeLogOdds log add set range len isLocked items list defaultdict lock bitarray makeTentative copy set add append setall range enumerate len zeros range len replaceRedundancy append reverse len getMotifIndepProb max log add filterOverlapping GetMotifs getMotifStats exp append replaceModules sort getFrequencyProbs print getMotifIndepProb addToLogFreqProbs collapse cdf range SortedListWithKey len range len reshape getFrequencyProbs log zeros range log append tuple len enumerate items list log Counter float range len sum generateExpandedMotif len mean zeros std range enumerate normal rand min append sum array log negLLMatrix print SolveAndReturn GenerateFakeData MotifHMM range len test add_str go Rstr_max list items sort append array range sorted set dict kark_sort array len sorted set dict kark_sort array len extend radixpass array range max array range len | snap-stanford/masa | 3,681 |
snetbl/potatoes | ['outlier detection', 'anomaly detection'] | ['Anomaly Detection With Partitioning Overfitting Autoencoder Ensembles'] | src/tools.py run.py src/od_conf.py src/od_models.py src/od_tools.py eval_and_plot gen_small_fmnist_files run_small plot_file cmp_save_potatoes_f gen_small_mnist_files gen_data_files gen_mnist_files gen_fmnist_files gen_ds conf_cmp_potatoes_f gen_small_ds eval_ds_cols run iforest_problem LofModel cae_mnist cae2_mnist check_cae2_mnist Ae EpochDotBlock CsOva log if_mnist ocsvm_mnist check_pot_mnist IfModel ep_str RocAuc scae_mnist PotEns OF1 OcsvmModel ConvAe2 Potatoes check_ocsvm_mnist OdMetric do_ova_eval Ode ocsvm_mnist_tune ConvAe3 EvalConf nnb_mnist check_lof_mnist check_scae_mnist Aee2 NotGonnaMakeIt SimpleConvAe Ap roc_of1 OdEnsF exp_decay check_if_mnist ConvAe OdModel lof_mnist check_nnb_mnist check_cae_mnist pot_mnist OsQtl KPr NnbModel FlatAeV OdEns roc_of1_ova OsAvg Aee Cifar10 FMnist SineRnd p BinData LabData CircleRnd precs Cifar100 Sine50 Sine Mnist precs_t_f rcs f1s rcs_t_f f1s_t_f opt_f1 Circle opt_f1_t_f check_hist_of_dists argtopk close_pairs ewedge ex_fi_fun bounding_box check_hist_of_dm cartesian v flatten time_it v_ind_in hist_of_dists hist_of_dm v_in pipe dists bo_norms join IfModel p_join ae_of_c print l2 PotEns strftime ae_reg_c match OcsvmModel Potatoes cross_comb Aee len eval_save p_join print astype now unlink print_df Path conf_cmp_potatoes_f touch makedirs show str p_join parent print tight_layout facets_f subplots_adjust savefig head print npz_ova_bds p_join exists to_csv concat p_join strftime plot_file eval_ds_cols eval_and_plot gen_ds gen_small_ds eval_and_plot p strftime makedirs p eval eval_ova show str set_title suptitle DataFrame random subplots_adjust scatterplot IsolationForest array score_samples fit IfModel Mnist print if_mnist describe Mnist LofModel print describe lof_mnist OcsvmModel Mnist ocsvm_mnist print describe show subplots set_title suptitle print concat subplots_adjust melt assign repeat lineplot zip xticks DataFrame NnbModel Mnist print describe nnb_mnist SimpleConvAe Mnist roc_of1_ova print scae_mnist set_option describe ConvAe Mnist print describe set_option cae_mnist ConvAe2 Mnist print cae2_mnist set_option describe Potatoes Mnist print describe pot_mnist show join format arange p_join print to_csv strftime ova_fig tight_layout subplots_adjust repeat savefig tile len print assign cumsum sort_values assign cumsum sum sort_values assign rcs_t_f precs_t_f assign tuple shape range len flatten distance_matrix hist subplots show subplots set_title suptitle random tight_layout subplots_adjust hist_of_dm full hist subplots dists show subplots set_title suptitle random tight_layout subplots_adjust hist_of_dists full str task format print now arange len fun reduce arange argsort | snetbl/potatoes | 3,682 |
sniklaus/3d-ken-burns | ['depth estimation'] | ['3D Ken Burns Effect from a Single Image'] | benchmark-nyu.py models/disparity-adjustment.py common.py benchmark-ibims.py depthestim.py models/pointcloud-inpainting.py interface.py models/disparity-refinement.py models/disparity-estimation.py autozoom.py launch_kernel preprocess_kernel process_inpaint depth_to_points process_autozoom process_shift render_pointcloud process_kenburns spatial_filter process_load fill_disocclusion update_to autozoom update_mode index get_live load_image update_from get_result disparity_adjustment Disparity Basic Downsample Semantics Upsample disparity_estimation Basic Downsample disparity_refinement Upsample Refine Inpaint Basic Downsample pointcloud_inpainting Upsample view depth_to_points float disparity_adjustment disparity_refinement cuda minMaxLoc item disparity_estimation max view depth_to_points pointcloud_inpainting float cat cuda clone view repeat item render_pointcloud range uint8 view process_inpaint astype getRectSubPix resize append render_pointcloud float max fill_disocclusion str int join replace size search group stride split type_as expand view new_zeros unfold conv2d pad range fill_ nelement cat new_zeros clone process_load cuda ascontiguousarray process_inpaint process_autozoom int float index int float index str join BytesIO read gettempdir rmtree write_videofile getpid process_kenburns makedirs clip neg interpolate item append float max range int float min interpolate | sniklaus/3d-ken-burns | 3,683 |
sniklaus/pytorch-hed | ['boundary detection', 'edge detection'] | ['Holistically-Nested Edge Detection'] | run.py comparison/comparison.py | # pytorch-hed This is a personal reimplementation of Holistically-Nested Edge Detection [1] using PyTorch. Should you be making use of this work, please cite the paper accordingly. Also, make sure to adhere to the licensing terms of the authors. Should you be making use of this particular implementation, please acknowledge it appropriately [2]. <a href="https://arxiv.org/abs/1504.06375" rel="Paper"><img src="http://www.arxiv-sanity.com/static/thumbs/1504.06375v2.pdf.jpg" alt="Paper" width="100%"></a> For the original version of this work, please see: https://github.com/s9xie/hed <br /> For another reimplementation based on Caffe, please see: https://github.com/zeakey/hed ## usage To run it on your own image, use the following command. Please make sure to see their paper / the code for more details. ``` python run.py --model bsds500 --in ./images/sample.png --out ./out.png | 3,684 |
snorkel-team/snorkel-tutorials | ['autonomous driving'] | ['Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices'] | spouse/tf_model.py scripts/docker_launch.py spouse/spouse_demo.py spouse/preprocessors.py recsys/utils.py multitask/multitask_tutorial.py drybell/drybell_lfs_spark.py crowdsourcing/crowdsourcing_tutorial.py spam/01_spam_tutorial.py crowdsourcing/data.py spouse/utils.py drybell/drybell_lfs.py drybell/drybell_dask.py multitask/utils.py scripts/build.py drybell/drybell_spark.py spam/02_spam_data_augmentation_tutorial.py spam/utils.py visual_relation/utils.py spam/03_spam_data_slicing_tutorial.py getting_started/getting_started.py recsys/recsys_tutorial.py getting_started/utils.py visual_relation/visual_relation_tutorial.py scripts/get_tox_envs.py visual_relation/model.py make_worker_lf polarity_positive encode_text polarity_negative textblob_polarity worker_lf polarity_negative_2 load_data main article_mentions_person load_celebrity_knowledge_base body_contains_fortune combine_text person_in_db body_contains_fortune article_mentions_person person_in_db combine_text main lf_short_comment tf_replace_word_with_synonym lf_keyword_my lf_regex_check_out short_link lf_textblob_polarity get_synonyms load_unlabeled_spam_dataset make_square_dataset split_data make_circle_dataset make_inv_circle_dataset shared_first_author stars_in_review polarity_positive get_data_points_generator subjectivity_positive polarity_negative textblob_polarity get_data_tensors get_model download_and_process_data precision_batch process_interactions_data get_n_epochs maybe_download_files load_small_sample split_data f1_batch recall_batch load_data get_timestamp process_books_data process_reviews_data save_small_sample sync_py markdown check_links build_markdown_notebook sync get_scripts get_notebooks parse_web_yml check_notebook check_script cli test Notebook sync_notebook call_jupytext TutorialWebpage MarkdownHeader check_docker run_image build_image check_port docker_launch get_default_environments get_modified_paths get_changed_tox_envs textblob_subjectivity plot_probabilities_histogram keyword_lookup check short_comment make_keyword_lf plot_label_frequency check_out textblob_polarity has_person_nlp regex_check_out has_person textblob_sentiment change_person replace_adjective_with_synonym get_synonym swap_adjectives replace_token train_and_test replace_verb_with_synonym replace_noun_with_synonym make_keyword_sf keyword_lookup short_comment textblob_polarity regex_check_out short_link textblob_sentiment df_to_features load_spam_dataset get_keras_logreg get_keras_lstm get_keras_early_stopping featurize_df_tokens preview_tfs get_pytorch_mlp map_pad_or_truncate create_dict_dataloader get_left_tokens last_name get_person_text get_text_between get_person_last_names lf_other_relationship lf_married lf_husband_wife lf_distant_supervision lf_husband_wife_left_window lf_familial_relationship lf_distant_supervision_last_names lf_same_last_name get_text_between lf_family_left_window get_model get_feature_arrays bilstm get_n_epochs load_data create_model init_fc SceneGraphDataset get_op_sequence crop_img_arr FlatConcat union WordEmb vrd_to_pandas load_vrd_data flatten_vrd_relationship lf_not_person lf_area lf_ydist area lf_carry_subject lf_carry_object lf_dist lf_ride_object TextBlob tweet_text polarity tensor join set_index map drop rename sample tweet_id read_csv values run LabelModel set_index DaskLFApplier to_parquet apply repartition assign info read_parquet fit ents ents SparkLFApplier parquet rdd withColumn SQLContext array SparkContext union join get_synonyms choice split range len sorted columns reset_index chdir glob map drop lower rename append read_csv enumerate run uniform astype uniform astype uniform astype train_test_split intersection len review_text isinstance blob blob blob concatenate Input divide Model expand_dims sum compile padded_batch get_next download_and_process_data concat sample drop_duplicates merge download makedirs dict datetime split astype map dict load_data DataFrame size astype map index dict load_data DataFrame dict map DataFrame load_data drop list map maybe_download_files merge rename info process_books_data process_reviews_data values process_interactions_data sum round sum round recall_batch precision_batch get format endswith get_notebooks abspath Notebook append TutorialWebpage MarkdownHeader list lstrip Request urlopen info finditer append run join abspath info join abspath info check_links read py info ipynb run check_links returncode info exclude_all_output run append makedirs ipynb call_jupytext call_jupytext py get_scripts check_script get_notebooks check_notebook build_markdown_notebook parse_web_yml sync_notebook get_notebooks sync_py run run socket SOCK_STREAM AF_INET bind returncode run check_docker build_image check_port run_image get run run join print get_modified_paths set add any get_default_environments split TextBlob text subjectivity polarity any show xlabel ylabel hist sum xlabel show hist ylabel replace choice join sorted choice synsets doc text get_synonym choice replace_token doc text get_synonym choice replace_token doc text get_synonym choice replace_token get_keras_lstm fit sorted columns reset_index chdir glob concat len ones map drop sample lower rename append train_test_split read_csv enumerate run Sequential Adam add sigmoid Dense softmax compile Embedding Sequential add Dense LSTM compile hashing_trick append iterrows f OrderedDict transform todense fit_transform values LongTensor from_tensors FloatTensor range extend append join format get_person_text person_names join split person_lastnames person_names person_lastnames list map apply between_tokens array person2_right_tokens to_hash_bucket AdagradOptimizer bilstm min max xavier_uniform_ weight fill_ Operation init_fc Sequential get_op_sequence in_features parameters Task ModuleDict Linear append flatten_vrd_relationship load seed list isdir ones len choice vrd_to_pandas run listdir open | # Snorkel Tutorials   [](https://travis-ci.com/snorkel-team/snorkel-tutorials?branch=master) [](https://opensource.org/licenses/Apache-2.0) A collection of tutorials for [Snorkel](https://github.com/snorkel-team/snorkel). For more information, visit the [Snorkel website](https://snorkel.org). ## Contents * [Tutorials](#tutorials) * [Getting Started](#getting-started) | 3,685 |
snowkylin/ntm | ['one shot learning'] | ['One-shot Learning with Memory-Augmented Neural Networks'] | ntm/ntm_cell_v2.py ntm/ntm_cell.py model_v2.py one_shot_learning.py copy_task_v2.py utils.py copy_task.py ntm/mann_cell_v2.py ntm/mann_cell_2.py model.py ntm/mann_cell.py main train test train SequenceCrossEntropyLoss NTMCopyModel NTMOneShotLearningModel CopyModel main train test_f test generate_random_strings one_hot_encode five_hot_decode one_hot_decode baseN OmniglotDataLoader MANNCell MANNCell MANNCell NTMCell NTMCell add_argument test ArgumentParser parse_args train append max_seq_length range NTMCopyModel get_checkpoint_state test_seq_length Saver NTMCopyModel save_dir constant sequence_loss_func SequenceCrossEntropyLoss model generate_random_strings print CopyModel Adam gradient apply_gradients variables randint NTMOneShotLearningModel OmniglotDataLoader model NTMOneShotLearningModel OmniglotDataLoader five_hot_decode one_hot_decode range seq_length zeros iternext nditer shape reshape | snowkylin/ntm | 3,686 |
snu-mllab/parsimonious-blackbox-attack | ['combinatorial optimization'] | ['Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization'] | imagenet/attacks/parsimonious_attack.py cifar10/attacks/local_search_helper.py imagenet/main.py cifar10/attacks/__init__.py imagenet/tools/imagenet_labels.py imagenet/attacks/local_search_helper.py imagenet/tools/inception_v3_imagenet.py cifar10/attacks/parsimonious_attack.py cifar10/model.py cifar10/cifar10_input.py cifar10/main.py imagenet/attacks/__init__.py imagenet/tools/utils.py DataSubset AugmentedCIFAR10Data CIFAR10Data AugmentedDataSubset Model LocalSearchHelper ParsimoniousAttack LocalSearchHelper ParsimoniousAttack label_to_name _preprocess model _get_model get_image grad_clip_by_value pseudorandom_target one_hot image_of_class chunks py_func pseudorandom_target_image _grad_clip_by_value_grad grad_clip_by_norm softmax int_shape load_image _grad_clip_by_norm_grad optimistic_restore hms hasattr inception_v3_arg_scope inception_v3 default_image_size network_fn _preprocess default_image_size _get_model argmax optimistic_restore load open randint RandomState randint RandomState int height resize astype float32 repeat width crop open join sorted zeros restore sorted NewCheckpointReader Saver get_variable_to_shape_map exp max range len int get_default_graph float isinstance inputs float isinstance inputs | # Code for Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization This code is for reproducing the results in the paper, [Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization](https://arxiv.org/abs/1905.06635), accepted at ICML 2019. ## Citing this work ``` @inproceedings{moonICML19, title= {Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization}, author={Moon, Seungyong and An, Gaon and Song, Hyun Oh}, booktitle = {International Conference on Machine Learning (ICML)}, year={2019} } | 3,687 |
soCzech/TransNet | ['boundary detection'] | ['TransNet: A deep network for fast detection of common shot transitions'] | transnet_utils.py transnet.py TransNet TransNetParams scenes_from_predictions draw_video_with_predictions append uint8 astype enumerate fromarray line Draw concatenate reshape range len | soCzech/TransNet | 3,688 |
sobhanhemati/Efficient-Spectral-Hashing-ESH- | ['image retrieval'] | ['A non-alternating graph hashing algorithm for large scale image search'] | ESH_manifold/Efficient_SH_with_manifold_ours.py ESH_manifold/demo_ESH_manifold.py ESH_manifold/Datasets.py ESH_projected/utilities.py ESH_projected/Affinity_matrix.py ESH_manifold/out_of_sample.py ESH_projected/Efficient_SH.py ESH_projected/demo_ESH.py ESH_manifold/evaluate.py ESH_projected/Datasets.py ESH_projected/out_of_sample.py ESH_projected/evaluate.py ESH_manifold/utilities.py ESH_manifold/Affinity_matrix.py Affinity nuswide_vgg cifar10_vggfc7 colorectal_eff labelme_vggfc7 load_dataset get_feature_affinity grad_J compute_alpha ESH_generalized_manifold ESH_manifold initialize_W cost_fn generalized_grad_J out_of_sample_binary_codes projection_matrix one_hot_encode RRC normalize_Z to_Z Affinity nuswide_vgg cifar10_vggfc7 colorectal_eff labelme_vggfc7 load_dataset ESH_projected_grad get_feature_affinity compute_alpha initialize_W cost_fn out_of_sample_binary_codes projection_matrix one_hot_encode RRC normalize_Z to_Z cluster_centers_ RandomState to_Z fit join loadmat join loadmat join item join item dataset_loader normalize_Z T Variable eigsh norm subtract transpose square matmul trace abs transpose matmul norm subtract transpose square matmul trace abs get_feature_affinity constant grad_J print transpose inv identity gradient compute_alpha matmul assign trace eye initialize_W zeros abs range get_feature_affinity T constant Variable print transpose inv identity gradient compute_alpha matmul sqrt assign eye zeros range eigsh flatten sum array diag T projection_matrix to_Z unique len sqrt sum diag T one_hot_encode inv flatten shape eye exp inf cdist argmin float32 mean zeros sum range svd get_feature_affinity constant list print transpose gradient SGD compute_alpha matmul assign apply_gradients initialize_W zip zeros range | sobhanhemati/Efficient-Spectral-Hashing-ESH- | 3,689 |
sociodouble/Machine-Learning | ['unity'] | ['Unity: A General Platform for Intelligent Agents'] | ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_input_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/unity_to_external_pb2.py gym-unity/gym_unity/envs/__init__.py ml-agents/mlagents/trainers/learn.py ml-agents-envs/mlagents/envs/communicator_objects/custom_observation_pb2.py ml-agents/mlagents/trainers/meta_curriculum.py ml-agents/mlagents/trainers/tests/test_barracuda_converter.py ml-agents/mlagents/trainers/ppo/models.py gym-unity/gym_unity/__init__.py ml-agents/mlagents/trainers/trainer_controller.py ml-agents/mlagents/trainers/tests/test_curriculum.py ml-agents/mlagents/trainers/action_info.py ml-agents-envs/mlagents/envs/communicator.py ml-agents-envs/mlagents/envs/communicator_objects/custom_reset_parameters_pb2.py ml-agents/mlagents/trainers/tests/test_ppo.py ml-agents-envs/mlagents/envs/tests/test_rpc_communicator.py ml-agents-envs/setup.py ml-agents-envs/mlagents/envs/rpc_communicator.py ml-agents/mlagents/trainers/tests/test_trainer_controller.py ml-agents/setup.py ml-agents/mlagents/trainers/barracuda.py ml-agents-envs/mlagents/envs/tests/test_envs.py ml-agents/mlagents/trainers/ppo/trainer.py ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_output_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_initialization_output_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/unity_input_pb2.py ml-agents/mlagents/trainers/tests/test_meta_curriculum.py ml-agents/mlagents/trainers/bc/trainer.py ml-agents/mlagents/trainers/curriculum.py ml-agents-envs/mlagents/envs/communicator_objects/agent_action_proto_pb2.py ml-agents/mlagents/trainers/tests/test_policy.py ml-agents/mlagents/trainers/ppo/policy.py ml-agents-envs/mlagents/envs/communicator_objects/space_type_proto_pb2.py ml-agents/mlagents/trainers/tests/test_learn.py ml-agents-envs/mlagents/envs/communicator_objects/brain_parameters_proto_pb2.py ml-agents/mlagents/trainers/tests/test_demo_loader.py ml-agents/mlagents/trainers/models.py ml-agents/mlagents/trainers/__init__.py ml-agents-envs/mlagents/envs/communicator_objects/agent_info_proto_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/environment_parameters_proto_pb2.py ml-agents-envs/mlagents/envs/tests/test_subprocess_unity_environment.py ml-agents/mlagents/trainers/exception.py gym-unity/gym_unity/tests/test_gym.py ml-agents/mlagents/trainers/buffer.py ml-agents/mlagents/trainers/bc/online_trainer.py ml-agents-envs/mlagents/envs/communicator_objects/engine_configuration_proto_pb2.py ml-agents/mlagents/trainers/ppo/__init__.py ml-agents/mlagents/trainers/tensorflow_to_barracuda.py ml-agents-envs/mlagents/envs/communicator_objects/unity_to_external_pb2_grpc.py ml-agents/mlagents/trainers/policy.py ml-agents-envs/mlagents/envs/mock_communicator.py gym-unity/setup.py ml-agents-envs/mlagents/envs/communicator_objects/unity_message_pb2.py ml-agents-envs/mlagents/envs/environment.py ml-agents-envs/mlagents/envs/communicator_objects/custom_action_pb2.py ml-agents/mlagents/trainers/bc/policy.py ml-agents-envs/mlagents/envs/base_unity_environment.py ml-agents/mlagents/trainers/bc/__init__.py ml-agents-envs/mlagents/envs/communicator_objects/unity_output_pb2.py ml-agents-envs/mlagents/envs/exception.py gym-unity/gym_unity/envs/unity_env.py ml-agents-envs/mlagents/envs/communicator_objects/header_pb2.py ml-agents-envs/mlagents/envs/brain.py ml-agents-envs/mlagents/envs/communicator_objects/demonstration_meta_proto_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/resolution_proto_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/__init__.py ml-agents-envs/mlagents/envs/subprocess_environment.py ml-agents/mlagents/trainers/demo_loader.py ml-agents-envs/mlagents/envs/__init__.py ml-agents/mlagents/trainers/tests/test_trainer_metrics.py ml-agents/mlagents/trainers/tests/test_buffer.py ml-agents-envs/mlagents/envs/communicator_objects/command_proto_pb2.py ml-agents/mlagents/trainers/trainer.py ml-agents-envs/mlagents/envs/socket_communicator.py ml-agents/mlagents/trainers/bc/models.py ml-agents/mlagents/trainers/bc/offline_trainer.py ml-agents/mlagents/trainers/tests/test_bc.py ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_initialization_input_pb2.py ml-agents/mlagents/trainers/trainer_metrics.py UnityGymException ActionFlattener UnityEnv create_mock_vector_braininfo test_gym_wrapper test_multi_agent test_branched_flatten setup_mock_unityenvironment create_mock_brainparams ActionInfo BarracudaWriter compress Build sort lstm write fuse_batchnorm_weights trim gru Model summary Struct parse_args to_json rnn BufferException Buffer Curriculum make_demo_buffer load_demonstration demo_to_buffer CurriculumError MetaCurriculumError TrainerError create_environment_factory run_training prepare_for_docker_run try_create_meta_curriculum main load_config MetaCurriculum LearningModel Policy UnityPolicyException get_layer_shape pool_to_HW flatten process_layer process_model basic_lstm get_attr ModelBuilderContext order_by get_epsilon get_tensor_dtype replace_strings_in_list get_tensor_dims by_op remove_duplicates_from_list by_name convert strides_to_HW get_tensor_data gru UnityTrainerException Trainer TrainerController TrainerMetrics BehavioralCloningModel OfflineBCTrainer OnlineBCTrainer BCPolicy BCTrainer PPOModel PPOPolicy PPOTrainer get_gae discount_rewards test_barracuda_converter test_dc_bc_model test_cc_bc_model test_visual_cc_bc_model test_bc_policy_evaluate dummy_config test_visual_dc_bc_model assert_array test_buffer location default_reset_parameters test_init_curriculum_bad_curriculum_raises_error test_init_curriculum_happy_path test_increment_lesson test_get_config test_load_demo basic_options test_docker_target_path test_run_training test_init_meta_curriculum_happy_path test_increment_lessons_with_reward_buff_sizes default_reset_parameters MetaCurriculumTest test_increment_lessons measure_vals reward_buff_sizes test_set_all_curriculums_to_lesson_num test_get_config test_set_lesson_nums test_init_meta_curriculum_bad_curriculum_folder_raises_error more_reset_parameters basic_mock_brain test_take_action_returns_action_info_when_available basic_params test_take_action_returns_nones_on_missing_values test_take_action_returns_empty_with_no_agents test_rl_functions test_ppo_model_dc_vector_curio test_ppo_model_dc_vector_rnn test_ppo_model_cc_vector_rnn test_ppo_policy_evaluate test_ppo_model_cc_visual dummy_config test_ppo_model_dc_vector test_ppo_model_dc_visual test_ppo_model_cc_visual_curio test_ppo_model_dc_visual_curio test_ppo_model_cc_vector_curio test_ppo_model_cc_vector test_initialize_online_bc_trainer basic_trainer_controller assert_bc_trainer_constructed test_initialize_trainer_parameters_uses_defaults dummy_bad_config test_take_step_adds_experiences_to_trainer_and_trains test_initialize_trainer_parameters_override_defaults test_initialize_invalid_trainer_raises_exception test_start_learning_trains_until_max_steps_then_saves dummy_config dummy_offline_bc_config_with_override test_initialization_seed test_initialize_ppo_trainer test_start_learning_updates_meta_curriculum_lesson_number assert_ppo_trainer_constructed test_take_step_resets_env_on_global_done test_start_learning_trains_forever_if_no_train_model dummy_offline_bc_config trainer_controller_with_take_step_mocks trainer_controller_with_start_learning_mocks dummy_online_bc_config TestTrainerMetrics BaseUnityEnvironment safe_concat_np_ndarray BrainInfo BrainParameters safe_concat_lists Communicator UnityEnvironment UnityWorkerInUseException UnityException UnityTimeOutException UnityEnvironmentException UnityActionException MockCommunicator RpcCommunicator UnityToExternalServicerImplementation SocketCommunicator worker EnvironmentResponse EnvironmentCommand UnityEnvWorker SubprocessUnityEnvironment UnityToExternalServicer UnityToExternalStub add_UnityToExternalServicer_to_server test_initialization test_reset test_close test_step test_handles_bad_filename test_rpc_communicator_checks_port_on_create test_rpc_communicator_create_multiple_workers test_rpc_communicator_close mock_env_factory MockEnvWorker SubprocessEnvironmentTest create_mock_vector_braininfo sample UnityEnv setup_mock_unityenvironment step create_mock_brainparams create_mock_vector_braininfo UnityEnv setup_mock_unityenvironment step create_mock_brainparams setup_mock_unityenvironment create_mock_vector_braininfo create_mock_brainparams UnityEnv Mock list Mock array range join isdir print replaceFilenameExtension add_argument exit verbose source_file ArgumentParser target_file sqrt topologicalSort list hasattr layers addEdge Graph print inputs set len list hasattr layers print filter match trim_model compile data layers print tensors float16 replace layers dumps data dtype layers isinstance print name tensors inputs outputs shape zip array_without_brackets to_json globals Build tanh mad tanh mul Build concat add sigmoid sub mad _ tanh mul Build concat add sigmoid mad Buffer reset_local_buffers number_visual_observations append_update_buffer append range enumerate make_demo_buffer load_demonstration number_steps read suffix BrainParametersProto from_agent_proto DemonstrationMetaProto ParseFromString AgentInfoProto append from_proto _DecodeVarint32 start_learning int str format create_environment_factory TrainerController external_brains put try_create_meta_curriculum load_config SubprocessUnityEnvironment list MetaCurriculum reset_parameters keys chmod format basename isdir glob copyfile copytree prepare_for_docker_run replace int Process join docopt getLogger print run_training start Queue info append randint setLevel range endswith len HasField hasattr get_attr tensor_shape ndarray isinstance shape int_val bool_val float_val ListFields name ndarray isinstance str tensor_content ndarray product isinstance get_tensor_dtype print get_tensor_dims unpack int_val bool_val array float_val enter append add set name find_tensor_by_name split name lstm find_tensor_by_name find_forget_bias split get_layer_shape id Struct tensor hasattr name patch_data input_shapes out_shapes input get_attr append replace_strings_in_list tensors astype op zip enumerate print float32 patch_data_fn model_tensors map_ignored_layer_to_its_input co_argcount len items list get_tensors hasattr name print process_layer eval ModelBuilderContext layers verbose Struct process_model open compress node GraphDef Model dims_to_barracuda_shape insert get_tensor_dims inputs MessageToJson ParseFromString cleanup_layers read memories print sort write trim summary list zeros_like size reversed range asarray tolist discount_rewards join remove _get_candidate_names convert _get_default_tempdir dirname abspath isfile next BCPolicy evaluate close reset MockCommunicator reset_default_graph UnityEnvironment reset_default_graph reset_default_graph reset_default_graph reset_default_graph flatten list range len get_batch Buffer assert_array append_update_buffer make_mini_batch append reset_agent array range Curriculum Curriculum Curriculum make_demo_buffer load_demonstration dirname abspath MagicMock basic_options MagicMock MetaCurriculum assert_has_calls MetaCurriculumTest increment_lessons assert_called_with MetaCurriculumTest increment_lessons assert_called_with assert_not_called MetaCurriculumTest set_all_curriculums_to_lesson_num MetaCurriculumTest dict update MetaCurriculumTest MagicMock basic_mock_brain basic_params Policy BrainInfo get_action MagicMock basic_mock_brain basic_params Policy BrainInfo get_action MagicMock basic_mock_brain ActionInfo basic_params Policy BrainInfo get_action evaluate close reset MockCommunicator PPOPolicy reset_default_graph UnityEnvironment reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph assert_array_almost_equal array discount_rewards dummy_offline_bc_config TrainerController assert_called_with BrainInfoMock basic_trainer_controller assert_bc_trainer_constructed dummy_offline_bc_config summaries_dir model_path keep_checkpoints BrainInfoMock basic_trainer_controller assert_bc_trainer_constructed summaries_dir model_path keep_checkpoints dummy_offline_bc_config_with_override BrainInfoMock basic_trainer_controller assert_bc_trainer_constructed summaries_dir model_path keep_checkpoints dummy_online_bc_config BrainInfoMock basic_trainer_controller assert_ppo_trainer_constructed summaries_dir dummy_config model_path keep_checkpoints initialize_trainers BrainInfoMock dummy_bad_config basic_trainer_controller MagicMock basic_trainer_controller start_learning assert_called_once MagicMock assert_not_called dummy_config trainer_controller_with_start_learning_mocks assert_called_once_with start_learning assert_called_once MagicMock dummy_config trainer_controller_with_start_learning_mocks assert_called_once_with start_learning MagicMock dummy_config trainer_controller_with_start_learning_mocks assert_called_once_with lesson MagicMock basic_trainer_controller take_step assert_called_once MagicMock trainer_controller_with_take_step_mocks assert_called_once MagicMock ActionInfo take_step outputs assert_not_called trainer_controller_with_take_step_mocks assert_called_once_with extend copy external_brains global_done payload reset _send_response reset_parameters env_factory step method_handlers_generic_handler add_generic_rpc_handlers UnityEnvironment close MockCommunicator UnityEnvironment close MockCommunicator reset str local_done print agents step close reset MockCommunicator UnityEnvironment len UnityEnvironment close MockCommunicator close RpcCommunicator close RpcCommunicator close RpcCommunicator | <img src="docs/images/unity-wide.png" align="middle" width="3000"/> <img src="docs/images/image-banner.png" align="middle" width="3000"/> # Unity ML-Agents Toolkit (Beta) [](docs/Readme.md) [](LICENSE) **The Unity Machine Learning Agents Toolkit** (ML-Agents) is an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. We also provide implementations (based on TensorFlow) | 3,690 |
softsys4ai/athena | ['adversarial defense', 'denoising'] | ['ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial Defense'] | src/utils/augmentations.py src/models/networks/__init__.py src/models/utils/metrics.py src/utils/data.py src/attacks/evasion/fast_gradient.py src/models/sklearnwrapper.py src/models/pytorchwrapper.py src/attacks/evasion/one_pixel.py src/attacks/evasion/bim.py src/utils/model_utils.py src/experiment/craft_ae_cifar100.py src/__init__.py src/utils/csv_headers.py src/attacks/evasion/pgd.py src/models/athena.py src/utils/file.py src/attacks/craft_w_art.py src/utils/logger.py src/attacks/evasion/carlini_wagner_l2.py src/utils/archive.py src/attacks/evasion/hsja.py src/experiment/detector_cifar100.py src/models/utils/lr_scheduler.py src/utils/transformation.py src/attacks/evasion/distribution.py src/models/networks/wideresnet.py src/models/cifar100_cnn.py src/utils/diversity.py src/utils/measure.py src/data/data.py src/experiment/adversarial_train.py src/models/image_processor.py src/attacks/attacker_art.py src/utils/data_utils.py src/models/bart/bart_preprocess.py src/experiment/evaluate_cifar100.py src/models/bart/bart_train.py src/models/transformation.py src/experiment/subsampling.py src/attacks/utils.py src/models/keraswrapper.py src/models/cifar100_utils.py src/models/utils/estimator.py src/utils/config.py _fgsm _op _cw _zoo _spatial _df _pgd _get_norm_value _mim _jsma generate _hop_skip_jump _bim craft WHITEBOX_ATTACK get_norm_value BasicIterativeMethod CWL2 ZERO CarliniWagnerL2 sample_from_distribution TRANSFORMATION_DISTRIBUTION batch_sample_from_distribution FastGradientMethod HopSkipJump OnePixel generate ProjectedGradientDescent CutoutDefault get_augmented_aeloaders get_transformation_loaders Augmentation MyDataset get_dataloaders load_data show_gridimg main SubsetSampler get_augmentation pgd_adv_train generate_ae_zk generate_ae train_detector collect_predictions ENSEMBLE_STRATEGY Ensemble run_epoch get_translist_for_usenix train_and_eval load_pool load_model _shift _flip _noise_trans _segment_trans _distort_trans _rotate _augment_trans _denoise_trans _geometric_trans _cartoon_trans _filter_trans _transform_images _morph_trans _quant_trans transform _compression_trans _affine_trans generator_fit WeakDefense WeakDefense ScikitlearnClassifier WeakDefense ScikitlearnSVC cartoon_effect quantize affine_trans morph_trans segmentations compress transform_images rotate geometric_transformations cartoonify composite_transforms denoising main augment flip add_noise distort shift filter transform build_transformation_configs _get_str_transformation_type _get_transformation_list run_epoch get_translist_for_usenix train_and_eval conv_init conv3x3 WideBasic WideResNet get_model num_class cross_entropy_smooth Accumulator get_corrections accuracy error_rate SummaryWriterDummy adjust_learning_rate_resnet Accumulator cross_entropy_smooth SummaryWriterDummy accuracy autoaug_paper_cifar10 fa_reduced_svhn autoaug2arsaug float_parameter no_duplicates policy_decoder arsaug_policy autoaug_policy int_parameter fa_resnet50_rimagenet remove_deplicates fa_reduced_cifar10 Rotate Solarize Contrast SamplePairing TranslateY augment_list Brightness ShearX TranslateYAbs Cutout Lighting Invert get_augment Posterize2 AutoContrast TranslateXAbs Posterize Color TranslateX Flip Equalize ShearY CutoutAbs apply_augment Sharpness MODE DATA ATTACK MODEL TRANSFORMATION IdealModelEvalHeaders get_dataloader subsampling load_mnist channels_first channels_last set_channels_last set_channels_first channels_first channels_last rescale set_channels_last probs2labels set_channels_first edit_distance_error read_list_from_txt load_from_json dump_to_csv CSV_ORIENT dump_to_json load_from_csv get_logger add_filehandler frobenius_norm wrap load save SEGMENT_TRANSFORMATIONS get_flip_direction DISTORT_RESAMPLE_MEHTOD get_filter_op DISTORT_TRANSFORMATIONS MORPH_TRANSFORMATIONS CARTOON_ADAPTIVE_METHODS get_compress_encoder DENOISE_TRANSFORMATIONS NOISE_TRANSFORMATIONS TRANSFORMATION AUGMENT_TRANSFORMATIONS COMPRESS_FORMAT get_cartoon_adpative_method GEOMETRIC_TRANSFORMATIONS get_morph_op FILTER_TRANSFORMATION CARTOON_THRESH_METHODS FLIP_DIRECTION get_distort_resample get_cartoon_thresh_method get_geometric_op get format print lower device get FastGradientMethod get print lower CarliniLInfMethod CarliniL2Method get _get_norm_value ProjectedGradientDescent get BasicIterativeMethod _get_norm_value get SaliencyMapMethod get DeepFool get SpatialTransformation get HopSkipJump _get_norm_value get ZooAttack int inf get format inf print CarliniL2Method DeepFool ProjectedGradientDescent generate CarliniLInfMethod FastGradientMethod BasicIterativeMethod SaliencyMapMethod int inf attack_params get normal value format reshape astype float32 choice shape uniform set_channels_last randint clip attack_params attack_params attack_all OnePixel attack_params format print reshape to_categorical astype float32 rescale shape upper transform set_channels_first Compose load_model MyDataset rescale get_dataloaders DataLoader load_data transform set_channels_first get_augmentation autoaug_paper_cifar10 arsaug_policy autoaug_policy DataLoader fa_reduced_cifar10 get_augmentation list Augmentation len SubsetRandomSampler append next range get process_batch asarray StratifiedShuffleSplit insert debug CutoutDefault targets SubsetSampler set_channels_first isinstance print MyDataset load_data split autoaug_paper_cifar10 arsaug_policy autoaug_policy DataLoader fa_reduced_cifar10 get_augmentation list Augmentation len SubsetRandomSampler append next range get format StratifiedShuffleSplit insert debug CutoutDefault targets SubsetSampler isinstance print MyDataset load_data split show imshow make_grid permute load_data join format print ProjectedGradientDescent AdversarialTrainer save fit get monotonic show format asarray join exists print reshape min close error_rate imshow title save generate range predict SGD warning save max OrderedDict shape load_state_dict run_epoch CrossEntropyLoss range get format product replace GradualWarmupScheduler eval info CosineAnnealingLR train load reporter print dict parameters isnan adjust_learning_rate_resnet load_data empty_cache get_model add_scalar get join load print save predict model clip_grad_norm_ zero_grad set_description list Accumulator len set_postfix get format add_dict float items backward accuracy tqdm parameters loss_fn step add_scalar num_class SGD warning save round monotonic OrderedDict load_state_dict run_epoch CrossEntropyLoss range get format product replace GradualWarmupScheduler debug eval info CosineAnnealingLR train load reporter print dict parameters get_dataloaders adjust_learning_rate_resnet isnan empty_cache get_model add_scalar compress_png_compression_1 samplewise_std_norm compress_png_compression_8 feature_std_norm noise_poisson shift_left clean geo_swirl append morph_dilation denoise_tv_bregman morph_closing flip_horizontal filter_median compress_png_compression_5 morph_opening denoise_wavelet denoise_nl_fast morph_erosion cartoon_mean_type3 affine_horizontal_stretch filter_rank shift_down compress_jpeg_quality_80 get load format isfile print set_device PyTorchWD SGD parameters eval load_state_dict startswith device is_available get_model CrossEntropyLoss get join list format load_model print len startswith keys split isinstance astype float32 expand_dims clip get warpAffine reshape getRotationMatrix2D shape stack append get warpAffine reshape float32 shape stack append get reshape shape stack append flip get warpAffine getAffineTransform reshape float32 shape stack append get uint8 value ones tuple reshape morphologyEx shape stack append get_morph_op get reshape shape stack flow ImageDataGenerator append fit bitwise_and bilateralFilter COLOR_GRAY2RGB medianBlur shape adaptiveThreshold append range get value asarray COLOR_RGB2GRAY stack get_cartoon_adpative_method pyrDown pyrUp uint8 reshape get_cartoon_thresh_method cvtColor get COLOR_GRAY2RGB dtype COLOR_RGB2GRAY COLOR_RGB2LAB MiniBatchKMeans reshape astype shape stack append fit_predict COLOR_Lab2RGB cvtColor roll resize clip COLOR_GRAY2RGB fromarray shape append range shift_func get COLOR_RGB2GRAY mean stack hsv2rgb int rgb2hsv reshape get_distort_resample array cvtColor get reshape random_noise shape stack append get COLOR_GRAY2RGB COLOR_RGB2GRAY reshape disk op float32 shape stack append get_filter_op cvtColor get PNG format COLOR_RGB2GRAY print reshape get_compress_encoder imencode shape imdecode stack append quit cvtColor get reshape denoise_bilateral mean shape dict estimate_sigma denoise_nl_means denoise_wavelet stack denoise_tv_chambolle append double denoise_tv_bregman get COLOR_GRAY2RGB COLOR_RGB2GRAY reshape radon op float32 shape stack linspace append cvtColor get_geometric_op get COLOR_GRAY2RGB COLOR_RGB2GRAY reshape disk gradient shape lower stack append median cvtColor watershed randint import_module hasattr __name__ warpAffine format print reshape getRotationMatrix2D shape stack append DEBUG int warpAffine format print reshape float32 stack append DEBUG format print reshape shape stack append DEBUG warpAffine format getAffineTransform print reshape float32 shape stack append DEBUG uint8 format print ones reshape dilate morphologyEx MORPH_CLOSE MORPH_GRADIENT shape stack erode append DEBUG MORPH_OPEN format zeros_like print reshape fit shape stack flow ImageDataGenerator DEBUG append len bitwise_and bilateralFilter DEBUG COLOR_GRAY2RGB medianBlur shape adaptiveThreshold append range get asarray COLOR_RGB2GRAY ADAPTIVE_THRESH_MEAN_C stack pyrDown pyrUp uint8 print reshape cvtColor format print ADAPTIVE_THRESH_MEAN_C shape DEBUG ADAPTIVE_THRESH_GAUSSIAN_C COLOR_GRAY2RGB int COLOR_RGB2GRAY COLOR_RGB2LAB MiniBatchKMeans reshape copy shape stack append fit_predict range COLOR_Lab2RGB cvtColor roll resize clip COLOR_GRAY2RGB fromarray shape append range COLOR_RGB2GRAY copy mean stack hsv2rgb int rgb2hsv reshape shift array cvtColor meijering minimum_filter maximum_filter scharr COLOR_GRAY2RGB shape skeletonize sato append prewitt COLOR_RGB2GRAY disk stack median_filter hessian thin gaussian_filter invert frangi entropy reshape roberts rank_filter float32 sobel cvtColor reshape random_noise shape stack append int format COLOR_RGB2GRAY print reshape imencode shape imdecode stack append quit cvtColor reshape denoise_bilateral dict shape denoise_nl_means mean estimate_sigma denoise_wavelet stack denoise_tv_chambolle append double denoise_tv_bregman swirl COLOR_GRAY2RGB COLOR_RGB2GRAY reshape iradon radon float32 iradon_sart shape stack linspace append max cvtColor COLOR_GRAY2RGB COLOR_RGB2GRAY reshape disk gradient shape stack append median cvtColor watershed print quantize set_cur_transformation_type affine_trans morph_trans segmentations clip list compress rotate geometric_transformations CUR_TRANS_TYPE format cartoonify denoising augment flip add_noise deepcopy distort print shift filter transform_images load format print copy transform isinstance choice uniform randint _get_str_transformation_type load get split bias xavier_uniform_ weight __name__ constant_ to cuda WideResNet DataParallel topk size t eq mul_ expand_as append sum max sum float asarray asarray cuda LogSoftmax append join add set append augment_list range int int int size min copy uniform rectangle max get_augment clean format print reshape to_categorical astype shape load_data channels_last transpose transpose channels_first Tensor DataLoader TensorDataset LongTensor int monotonic format asarray join print extend shuffle save sample len min max len min intersection print format len print format clear setFormatter getLogger addHandler StreamHandler setLevel addHandler setFormatter setLevel FileHandler format print reshape zip float abs get print format print format | # Adversarial Defense as a Framework [Machine learning systems](https://pooyanjamshidi.github.io/mls/) have achieved impressive success in a wide range of domains like computer vision and natural langurage processing. However, their vulnerability to adversarial examples can lead to a series of consequences, especially in security-critical tasks. For example, an object detector on a self-driving vehicle may incorrectly recognize [an stop sign as a speed limit](https://spectrum.ieee.org/cars-that-think/transportation/sensors/slight-street-sign-modifications-can-fool-machine-learning-algorithms). The threat of the adversarial examples has inspired a sizable body of research on various defense techniques. With the assumption on the specific known attack(s), most of the existing defenses, although effective against particular attacks, can be circumvented under slightly different conditions, either a stronger adaptive adversary or in some cases even weak (but different) adversaries. In order to stop the `arms race` between the attacks and defenses, we wonder > How can we, instead, design a defense, not as a technique, but as a framework that one can construct a specific defense considering the niche tradeoff space of robustness one may want to achieve as well as the cost one is willing to pay to achieve that level of robustness? [**ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial Defense**](https://softsys4ai.github.io/athena/) \ [Ying Meng](https://meng2010.github.io/), [Jianhai Su](https://oceank.github.io/), [Jason M O'Kane](https://www.cse.sc.edu/~jokane/), [Pooyan Jamshidi](https://pooyanjamshidi.github.io/) <table> <tr> <td><center><a href="https://arxiv.org/abs/2001.00308"><img height="100" width="78" src="www/athena_preprint.png" style="border:1px solid" style="border:1px solid black"><br>arXiv Preprint</a></center></td> <td><center><a href="https://github.com/softsys4ai/athena"><img height="100" width="78" src="www/athena_website.png" style="border:1px solid" style="border:1px solid black"><br>Website</a></center></td> | 3,691 |
sohaib50k/RESIST-Iris-template-reconstruction | ['iris recognition'] | ['Resist : Reconstruction of irises from templates'] | ThirdEye implementation/EER.py Attention.py ThirdEye implementation/helperFunc.py instance.py SpectralNormalizationKeras.py Attention InstanceNormalization DenseSN ConvSN1D _ConvSN ConvSN2D ConvSN3D ConvSN2DTranspose EmbeddingSN checkThreshT checkThresh prepareFeatures featureSelect range range show matshow print len write close corrcoef open resize append zeros imread flip range predict split norm len dot append range split | sohaib50k/RESIST-Iris-template-reconstruction | 3,692 |
sohaib50k/ThirdEye---Iris-recognition-using-triplets | ['iris recognition'] | ['ThirdEye: Triplet Based Iris Recognition without Normalization'] | helperFunc.py Attention.py EER.py Attention checkThreshT checkThresh prepareFeatures featureSelect range range show matshow print corrcoef resize append zeros imread flip range predict len norm len dot append range split | # ThirdEye---Iris-recognition-using-triplets Iris recognition using variants of the triplet loss. For starters the implementation uses Keras and Tensorflow, other needed packages include numpy, opencv, matplotlib among others. The losses borrow heavily from the [omoindrot triplet loss implementations](https://github.com/omoindrot/tensorflow-triplet-loss). # Training process The recognition pipeline works on the outputs of the [segmentation pipeline](https://github.com/sohaib50k/Unconstrained-iris-segmentation-using-Mask-R-CNN). It needs square images with irises in the center. The background is set to black. This implementation uses a custom Keras generator, the data format is in this format: * Training data * class1 * iris1_class1 * iris2_class1 | 3,693 |
sola-st/IdBench | ['word embeddings'] | ['IdBench: Evaluating Semantic Representations of Identifier Names in Source Code'] | train_w2v.py prepare_embedding_input.py compute_correlations.py pretrained_embeddings.py create_model plot_correlations extract_id_features read_and_clean_pairs subtokens plot_correlations_all add_combined_prediction compute_correlations write_to_ft_format read_token_files look_up_word cos_sim load_embedding train_w2v finditer append check subtokens len Pipeline id2 id1 iterrows create_model fit extract_id_features extend getattr append enumerate append items list correlation subplots arange set_xticklabels set_yticks set_xlabel tight_layout bar set_ylabel set_xticks savefig setp legend get_majorticklabels set_ylim len update plot_correlations compute_correlations print list getattr read_csv print join format len join endswith norm time format print LineSentence Word2Vec save train round build_vocab | sola-st/IdBench | 3,694 |
solaris33/EAST-tf2 | ['optical character recognition', 'scene text detection', 'curved text detection'] | ['EAST: An Efficient and Accurate Scene Text Detector'] | losses.py train.py lanms/.ycm_extra_conf.py lanms/__init__.py eval.py lanms/__main__.py locality_aware_nms.py data_processor.py model.py threadsafe_generator generator restore_rectangle_rbox line_cross_point get_text_file resize_image sort_rectangle count_samples all generate_rbox check_and_validate_polys restore_rectangle line_verticle shrink_poly crop_area fit_line load_data_process val_generator pad_image get_images polygon_area point_dist_to_line load_annotation threadsafe_iter load_data rectangle_from_parallelogram get_images sort_poly resize_image detect main standard_nms weighted_merge nms_locality intersection dice_loss rbox_loss resize_output_shape resize_bilinear EAST_model main train_step GetCompilationInfoForFile IsHeaderFile MakeRelativePathsInFlagsAbsolute FlagsForFile DirectoryOfThisScript merge_quadrangle_n9 glob join format extend polygon_area print zip append clip min astype choice shape int32 zeros range max clip arctan2 polyfit print norm arccos line_verticle fit_line dot line_cross_point sum arctan print argmin argmax concatenate reshape transpose zeros array fillPoly line_cross_point sort_rectangle ones argmin append sum range astype fit_line zip enumerate norm point_dist_to_line min argwhere zeros array rectangle_from_parallelogram replace randint copy shape zeros max shape float resize arange subplots input_size get_text_file resize resize_image abs show ones shape imshow generate_rbox check_and_validate_polys append training_data_path imread format crop_area close shuffle tight_layout choice add_artist astype pad_image zeros get_images Polygon print text set_yticks min float32 set_xticks load_annotation randint array arange input_size get_text_file resize_image shape generate_rbox check_and_validate_polys append training_data_path imread format astype shuffle pad_image get_images print float32 load_annotation validation_data_path array format print input_size shape load_annotation get_text_file check_and_validate_polys generate_rbox resize_image imread pad_image get join get_images print close nb_workers array validation_data_path Pool len test_data_path endswith print append walk len int time format zeros_like print reshape fillPoly astype argwhere int32 zeros restore_rectangle merge_quadrangle_n9 enumerate sum argmin imwrite output_dir resize_image restore basename gpu_num predict format latest_checkpoint detect model_path int get_images time join print reshape Checkpoint EAST_model step makedirs reshape area Polygon append array append weighted_merge minimum reduce_sum minimum cos split list trainable_variables list gradient apply_gradients zip generator checkpoint_path batch_size input_size save CheckpointManager count_samples ExponentialDecay init_learning_rate train_step dice_loss Adam assign_add next mkdir Variable rbox_loss summary create_file_writer east append join startswith IsHeaderFile compiler_flags_ exists compiler_flags_ GetCompilationInfoForFile compiler_working_dir_ MakeRelativePathsInFlagsAbsolute DirectoryOfThisScript nms_impl array copy | solaris33/EAST-tf2 | 3,695 |
solivr/tf-crnn | ['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'] | tf_crnn/preprocessing.py tf_crnn/data_handler.py tf_crnn/config.py doc/conf.py evaluation.py prediction.py hlp/alphabet_helpers.py setup.py tf_crnn/__init__.py hlp/prepare_iam.py tf_crnn/model.py tf_crnn/callbacks.py hlp/string_data_manager.py training.py hlp/csv_helpers.py hlp/numbers_mnist_generator.py evaluation prediction training get_alphabet_units_from_input_data generate_alphabet_file get_abbreviations_from_csv csv_filtering_chars_from_labels csv_rel2abs_path_convertor generate_random_image_numbers prepare_iam_data format_string_for_tf_split map_accentuated_characters_in_dataframe map_accentuated_characters_in_string tf_crnn_label_formatting lower_abbreviation_in_string add_abbreviation_brackets CustomSavingCallback CustomPredictionSaverCallback CustomLoaderCallback LRTensorBoard Alphabet CONST Params import_params_from_json padding_inputs_width dataset_generator augment_data random_rotation get_resized_width get_model_inference get_model_train ConvBlock get_crnn_output _compute_length_inputs data_preprocessing _convert_label_to_dense_codes preprocess_csv join str max dataset_generator evaluate print CustomLoaderCallback PREPROCESSING_FOLDER get_model_train preprocess_csv output_model_dir from_json_file join str dataset_generator get_model_inference CustomPredictionSaverCallback from_json_file max predict data_preprocessing LRTensorBoard ReduceLROnPlateau output_model_dir Params max str dataset_generator TensorBoard CustomSavingCallback append restore_model n_epochs join EarlyStopping makedirs CustomLoaderCallback get_model_train fit list read_csv unique apply list concatenate dict get_alphabet_units_from_input_data unique append join list format tqdm abspath split list validation join list next_batch format imsave reshape hstack map test tqdm mkdir append randint train range read_data_sets extract join create_experiment_csv generate_alphabet_file print glob tf_crnn_label_formatting download generate_splits_txt makedirs items list join iter next range count join list next replace enumerate split to_csv read_csv apply split split pop list format print keys less_equal divide shape cast int32 round greater_equal logical_and divide cast int32 pad_fn replicate_fn less simple_resize get_resized_width interleave from_tensor_slices map AUTOTUNE as_list cnn_stride_size cnn_kernel_size list cnn_features_list rnn_units conv rnn zip cnn_batch_norm cnn_pool_size input_shape compile input_channels Adam Model Input get_crnn_output input_shape input_channels identity Model load_weights Input get_crnn_output append list maximum len minimum get_image_shape_without_loading string_split_delimiter list format max_chars_per_string codes print to_csv apply dict alphabet_units _convert_label_to_dense_codes zip to_list DataFrame read_csv len join format PREPROCESSING_FOLDER preprocess_csv csv_files_train output_model_dir csv_files_eval makedirs | solivr/tf-crnn | 3,696 |
somnath-banerjee/Code-Mixed_SentimentAnalysis | ['sentiment analysis'] | ['LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis'] | code/config.py code/train.py code/model.py code/utils.py Config fastText Dataset evaluate_model model text extend flatten is_available f1_score numpy cuda enumerate | somnath-banerjee/Code-Mixed_SentimentAnalysis | 3,697 |
songNew/3_Gaze_MPII | ['gaze estimation'] | ['Appearance-Based Gaze Estimation in the Wild', 'MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation', "It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation"] | fit_screen_logit.py fit_screen_point.py ptgaze/common/__init__.py ptgaze/config/defaults.py normalize_data.py ptgaze/models/mpiigaze/resnet_preact.py ptgaze/models/mpiifacegaze/resnet_simple.py ptgaze/types.py ptgaze/common/visualizer.py ptgaze/common/face_parts.py ptgaze/common/face.py ptgaze/head_pose_estimation/head_pose_normalizer.py ptgaze/models/mpiifacegaze/backbones/__init__.py ptgaze/__init__.py ptgaze/models/mpiifacegaze/backbones/resnet_simple.py ptgaze/common/face_model.py ptgaze/common/camera.py ptgaze/utils.py fit_screen_point_two_eye.py ptgaze/gaze_estimator.py ptgaze/demo.py ptgaze/models/__init__.py ptgaze/common/eye.py ptgaze/head_pose_estimation/__init__.py setup.py ptgaze/head_pose_estimation/face_landmark_estimator.py ptgaze/main.py ptgaze/transforms.py ptgaze/config/__init__.py ptgaze/__main__.py plot_pr estimateHeadPose normalizeData _get_long_description _get_requirements Demo GazeEstimator main _create_mpiifacegaze_transform _create_mpiigaze_transform create_transform GazeEstimationMethod _check_path_all _set_eye_default_camera _download_eye_model _expanduser _generate_dummy_camera_params _check_path _download_face_model _set_face_default_camera _update_camera_config _download_dlib_pretrained_model _expanduser_all update_config update_default_config Camera Eye Face FaceModel FaceParts FacePartsName Visualizer get_default_config LandmarkEstimator HeadPoseNormalizer _normalize_vector create_model Model Model create_backbone initialize_weights Model BasicBlock show plot xlabel grid ylabel ylim title figure fill_between xlim solvePnP norm T equalizeHist COLOR_BGR2GRAY print reshape inv dot cross append warpPerspective array cvtColor parent merge_from_file config DEBUG update_config debug add_argument get_default_config ArgumentParser info Demo parse_args run setLevel update_default_config Lambda Compose mpiifacegaze_face_size Lambda Compose mpiifacegaze_gray auto model as_posix debug download_url_to_file mkdir expanduser exists debug download_url_to_file expanduser mkdir debug download_url_to_file expanduser mkdir get video_path VideoCapture CAP_PROP_FRAME_HEIGHT as_posix debug image_path expanduser imread CAP_PROP_FRAME_WIDTH release _generate_dummy_camera_params as_posix debug warn resolve expanduser use_camera as_posix debug parent as_posix debug parent video_path _expanduser model camera_params image_path normalized_camera_params output_dir checkpoint Path video_path image_path _check_path _download_eye_model ext as_posix debug no_screen _download_face_model warn image output_dir device face_detector video _check_path_all _set_eye_default_camera _set_face_default_camera _update_camera_config _download_dlib_pretrained_model freeze _expanduser_all auto lower import_module Model device to name import_module isinstance ones_ Conv2d zeros_ bias kaiming_normal_ BatchNorm2d weight Linear | songNew/3_Gaze_MPII | 3,698 |
songguoli87/HMGP | ['cross modal retrieval'] | ['Harmonized Multimodal Learning with Gaussian Process Latent Variable Models'] | gpflow/gplvm.py gpflow/model.py PCA_reduce GPLVM BayesianGPLVM GPModel Model eigh T cov | songguoli87/HMGP | 3,699 |
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