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mariontan/YoloObjectTrack | ['multiple object tracking'] | ['Simple Online and Realtime Tracking'] | sort.py utils/utils.py loop_Ivan_object_tracker.py object_tracker.py utils/datasets.py utils/parse_config.py models.py Ivan_object_tracker.py loop_conf_Ivan_object_tracker.py detect_image detect_image detect_image YOLOLayer create_modules Darknet EmptyLayer detect_image KalmanBoxTracker iou Sort convert_bbox_to_z associate_detections_to_trackers convert_x_to_bbox parse_args ImageFolder ListDataset parse_data_config parse_model_config compute_ap build_targets bbox_iou_numpy to_categorical weights_init_normal load_classes bbox_iou non_max_suppression unsqueeze_ Variable Compose min type float round pop int YOLOLayer Sequential ZeroPad2d MaxPool2d add_module Conv2d ModuleList EmptyLayer Upsample append BatchNorm2d LeakyReLU sum enumerate minimum maximum float sqrt linear_assignment iou concatenate reshape append zeros empty enumerate add_argument ArgumentParser rstrip strip open startswith append split dict strip split open data normal_ __name__ constant_ concatenate size maximum sum range clamp min max minimum eps expand_dims maximum data sort new squeeze size shape unsqueeze cuda unique bbox_iou append max is_cuda cat enumerate int fill_ FloatTensor ones concatenate size range unsqueeze bbox_iou zeros argmax log | this is the test script: prep sciprt: https://github.com/mariontan/Yolo_prep_scripts_etc train script: https://github.com/mariontan/Traffic_Flow # PyTorch Object Detection and Tracking Object detection in images, and tracking across video frames Full story at: https://towardsdatascience.com/object-detection-and-tracking-in-pytorch-b3cf1a696a98 References: 1. YOLOv3: https://pjreddie.com/darknet/yolo/ 2. Erik Lindernoren's YOLO implementation: https://github.com/eriklindernoren/PyTorch-YOLOv3 | 2,900 |
mariru/dynamic_bernoulli_embeddings | ['word embeddings'] | ['Dynamic Bernoulli Embeddings for Language Evolution'] | src/main.py dat/step_2_split_data.py dat/step_3_create_data_stats.py src/args.py dat/step_1_count_words.py src/data.py src/models.py src/utils.py count_words parse_args bern_emb_data plot_with_labels make_dir variable_summaries glob zeros load enumerate add_argument ArgumentParser close scatter savefig figure annotate enumerate join isdir strftime sleep randint makedirs | ### Dynamic Bernoulli Embeddings for Language Evolution This repository contains scripts for running (dynamic) Bernoulli embeddings on text data. They have been run and tested on Linux. To execute, go into the source folder (`src/`) and run ```python main.py --dynamic True --fpath [path/to/data]``` substitute the path to the folder where you put the data for `[path/to/data]`. The data folder and files have to be structured in a specific format. For your convenience, we included some scripts that will help you preprocess the text data in `dat/src/`. For instructions on the required data format see `dat/README.md`. For all commandline options run: ```python main.py --help``` | 2,901 |
mariru/structured_embeddings | ['word embeddings'] | ['Structured Embedding Models for Grouped Data'] | dat/step_4_negative_samples.py src/main.py dat/step_2_split_data.py dat/step_3_create_data_stats.py src/args.py dat/step_1_count_words.py src/data.py src/models.py src/utils.py count_words negative_samples parse_args neural_network hierarchical_bern_emb_model define_model amortized_bern_emb_model emb_model bern_emb_model make_dir variable_summaries glob zeros load enumerate load join Session constant replace glob astype placeholder log multinomial int32 tile save run expand_dims read_csv values open print add_argument ArgumentParser slice reshape matmul tanh bern_emb_model hierarchical_bern_emb_model amortized_bern_emb_model join isdir strftime sleep randint makedirs | # Structured Embeddings This repository contains code for analyzing how word usage differs across related groups of text data. The corresponding publication is [M. Rudolph, F. Ruiz, S. Athey, D. Blei, **Structured Embedding Models for Grouped Data**, *Neural Information Processing Systems*, 2017](https://papers.nips.cc/paper/6629-structured-embedding-models-for-grouped-data) Also, check out the [NIPS 2017 Spotlight Video:](https://www.youtube.com/watch?v=p1yeMFwkp1c) [](https://www.youtube.com/watch?v=p1yeMFwkp1c) The code in this repository contains 3 models you can fit to grouped text data: - global Bernoulli embeddings (no variations between groups) - hierarchical Bernoulli embeddings | 2,902 |
mariusarvinte/wavelet-patch-detection | ['denoising'] | ['Detecting Patch Adversarial Attacks with Image Residuals'] | aux_evaluation.py LID/val_custom_network_lid.py gen_grayboxCW.py gen_PGD.py gen_exhaustivePGD.py LID/custom_network_lid.py LID/util.py LID/test_custom_network_lid.py test_cw_performance.py gen_whiteboxCW.py aux_models.py LID/extract_characteristics.py aux_attacks.py train_residual_detector.py gen_blackboxCW.py aux_residual_classifier.py PatchProjectedGradientDescent train_classifier binary_fpr_metric evaluate_classifier fpr_metric evaluate_joint_classifier binary_tpr_metric gaussian_patch_images attack_images tpr_metric get_residual copycat_model vgg19_model conv_2d detector_model ResidueClassifier SmallResidueClassifier ResidueLogitClassifier BinaryResidueClassifier UnknownClassifier get_kmeans get_bu get_lid main merge_and_generate_labels get_kd kmean_pca_batch train_lr get_data train_lr_rfeinman compute_roc_rfeinman get_mc_predictions get_layer_wise_activations kmean_batch get_noisy_samples normalize get_kmeans_random_batch cross_entropy compute_roc lid_term score_point get_deep_representations lid_adv_term mle_single flip get_lids_random_batch random_split block_split mle_batch get_model score_samples multiply greater cast less sum epsilon multiply greater cast less sum epsilon argmax epsilon multiply cast sum equal argmax epsilon multiply cast sum equal randint range copy int list tqdm eval ceil empty range append list copy tqdm isnan any range denoise_wavelet ResidueClassifier EarlyStopping Adam load_weights ModelCheckpoint compile fit argmax confusion_matrix predict exp concatenate sort zeros max predict get_residual Sequential model Activation add Sequential model Activation add Sequential model Activation add l2 Model Input summary l2 Model Input summary l2 Model Input summary l2 Model Input summary l2 Model Input summary print reshape asarray concatenate concatenate print predict_classes get_deep_representations warn merge_and_generate_labels range score_samples fit print merge_and_generate_labels mean concatenate print get_lids_random_batch merge_and_generate_labels concatenate print merge_and_generate_labels get_kmeans_random_batch concatenate batch_size get_data save get_lid get_kmeans dataset k_nearest load_model predict_classes get_noisy_samples attack get_kd concatenate mean get_bu zip load join evaluate print isfile len join print reshape transpose to_categorical astype call load_data loadmat Sequential Activation add reshape transpose multiply square matmul reduce_sum sqrt top_k log l2_normalize reshape transpose multiply square matmul reduce_sum sqrt top_k log shape reshape copy choice minimum normal zeros_like maximum warn flip range len list value function tqdm append range predict int value function ceil zeros float range extend asarray cdist reshape min apply_along_axis len asarray cdist min apply_along_axis len asarray cdist min apply_along_axis len asarray arange kmean_batch concatenate zeros fit_transform int list asarray print extend tqdm estimate ceil float range len int list asarray print extend tqdm estimate ceil float range len join asarray close map Pool concatenate scale len fit transpose fit concatenate show plot xlabel roc_curve ylabel title figure legend roc_auc_score show plot concatenate xlabel roc_curve ylabel title figure legend auc print int permutation print int concatenate | # Detecting Patch Adversarial Attacks with Image Residuals Marius Arvinte, Ahmed Tewfik and Sriram Vishwanath The University of Texas at Austin Requirements: Tensorflow 1.15, Keras 2.2.4, Cleverhans 3.0.1 | 2,903 |
mariyashcheg/kaggle-freesound-2019 | ['audio tagging'] | ['Convolutional Gated Recurrent Neural Network Incorporating Spatial Features for Audio Tagging'] | train.py data.py models.py find_lr.py utils.py main.py KFSDataset build_dataset load_data build_preprocessing main find_lr _parse_args main cyclic_learning_rate _parse_args ConvGRNN ConvGatedBlock ConvGRNN_simple cgrnn cgrnn_simple apply_wd grad_norm Trainer _one_sample_positive_class_precisions get_new_model_path lwlrap_accumulator join list print tqdm zip append read_csv values seed int arange shuffle KFSDataset len int add_argument ArgumentParser add_figure model grid zero_grad label_ranking_average_precision_score device log list exp defaultdict step binary_cross_entropy_with_logits append to plot param_groups xscale item float items backward add_scalar to_csv tqdm dict figure train numpy len seed join SummaryWriter model get_new_model_path size outpath Adam build_preprocessing parameters DataLoader find_lr datadir manual_seed build_dataset cgrnn cgrnn_simple makedirs Trainer save len range eval_epoch int print train_epoch dict cyclic_learning_rate epochs print print data add_ named_parameters named_parameters join list format sort match mkdir zip append listdir list cumsum astype zeros float range flatnonzero | # Kaggle Freesound 2019 (audio tagging) ## Архитектура В бейзлайне использовалась сеть ResNet на log-mel спектрограммах. Хотелось более явно учитывать тот факт, что звук - это временной ряд, последовательность событий, поэтому заменяем на сеть, в которой есть рекуррентные блоки. В ходе проекта были реализованы две архитектуры из следующих статей группы авторов, занимавших 1-2 места на Detection and Classification of Acoustic Scenes and Events (DCASE2016 и DCASE2017) и последовательно модифицировавших свою сеть: * "Convolutional Gated Recurrent Neural Network Incorporating Spatial Features for Audio Tagging" [https://arxiv.org/pdf/1702.07787.pdf](https://arxiv.org/pdf/1702.07787.pdf) * "Attention and Localization based on a Deep Convolutional Recurrent Model for Weakly Supervised Audio Tagging" [https://arxiv.org/pdf/1703.06052.pdf](https://arxiv.org/pdf/1703.06052.pdf) * "Large-Scale Weakly Supervised Audio Classification Using Gated Convolutional Neural Network" [https://arxiv.org/pdf/1710.00343.pdf](https://arxiv.org/pdf/1710.00343.pdf) ## Логи проекта Первым шагом была реализована как самая современная и вобравшая в себя все лучшее сеть из последней статьи. Сеть состоит из: | 2,904 |
markWJJ/rgat | ['graph attention'] | ['Relational Graph Attention Networks'] | rgat/ops/sparse_ops.py rgat/ops/math_ops.py rgat/layers/relational_graph_convolution.py rgat/layers/__init__.py examples/rdf/example.py examples/batching/example_eager.py examples/batching/example_static.py rgat/layers/relational_graph_attention_logits.py rgat/utils/graph_utils.py rgat/layers/basis_decomposition_dense.py setup.py examples/rdf/inputs.py rgat/layers/graph_utils.py rgat/layers/relational_graph_attention.py rgat/datasets/rdf.py main _built_relational_support _build_support get_batch_of_features_supports_values _built_relational_support _build_support main get_architecture get_batch_of_features_supports_values RGATNModel model_fn RGCNModel main get_relations_classes get_input_fn get_splits sp2tfsp _build_support get_dataset get_graph_file_path get_dataset_file_paths _read_tsv normalise_matrix _get_rdf_dataset_helper to_unicode RDFReader BasisDecompositionDense AttentionStyles AttentionModes HeadAggregation RelationalGraphAttention RelationalGraphAttentionLogits RelationalGraphConv batched_sparse_tensor_to_sparse_block_diagonal get_shape batched_sparse_dense_matmul indices_expand sparse_diagonal_matrix indices_expand_0 sparse_squeeze_0 _indices triple_from_coo batch_of_relational_supports_to_support relational_supports_to_support uniform cast int32 info seed items format batch_of_relational_supports_to_support SparseTensor sparse_reorder rgat_layer RGAT allclose concat set_random_seed info triple_from_coo get_batch_of_features_supports_values len astype sparse_reorder rgat_layer RGAT sparse_placeholder placeholder info concatenate run global_variables_initializer get_architecture Session get_dataset get_global_step model_class sparse_concat model minimize sparse_softmax_cross_entropy AdamOptimizer gather argmax RunConfig train_and_evaluate Estimator get_input_fn HParams get_relations_classes transpose tocoo validate sp2tfsp get_dataset get_splits argmax format todense shape info zeros array len join join format info join format isfile get_graph_file_path get_dataset_file_paths _get_rdf_dataset_helper dirname save info join sorted format tocsr ones csr_matrix transpose dict triples normalise_matrix split info empty array enumerate len list sorted format lil_matrix tocsr _build_support tolist _get_indices_names identity set RDFReader info relationList union len flatten zeros diags shape isinstance as_list enumerate len type format ValueError list format ValueError OrderedDict type keys | # Relational Graph Attention Networks A TensorFlow implementation of Relational Graph Attention Networks for semi-supervised node classification and graph classification tasks introduced in our paper [Relational Graph Attention Networks](https://openreview.net/forum?id=Bklzkh0qFm). It is compatible with static and eager execution modes. Contact [[email protected]](mailto:[email protected]) for comments and questions. <img align="left" src="images/argat.png"> ## Installation To install `rgat`, run: ``` $ pip install git+git://github.com/Babylonpartners/rgat.git ``` To allow both the CPU and GPU versions of TensorFlow, we have not listed Tensorflow as a requirement. You will need to install a version separately. The TensorFlow version must be >= 1.10. | 2,905 |
markovmodel/deep_gen_msm | ['time series'] | ['Deep Generative Markov State Models'] | prinz/deep_ed_0.py prinz/deep_ml_0.py EarlyStopping Net_G Net_P potential_function Net_P log_sum_exp EarlyStopping potential_function Net_G exp squeeze sum max | # DeepGenMSM Here, we collect the code necessary to reproduce the results of our Project "Deep generative Markov State models for dynamical systems". The folder Prinz contains the code for the 1D four well Prinz potential using PyTorch. The folder AlaDi contains the code for the Alanine Dipeptide molecule and the generation of new structures using Tensorflow. | 2,906 |
markvdw/convgp | ['gaussian processes'] | ['Convolutional Gaussian Processes'] | opt_tools/testing/test_helpers.py convgp/misvgp.py opt_tools/thin_hist.py exp_tools.py convgp/svconvgp.py convert_mf_full.py opt_tools/display.py convgp/__init__.py cifar.py opt_tools/examples/test-scipy-opt.py datasets/process_rectangles.py testing/test_svsumgp.py datasets/process_rectangles_images.py sumkern_mnist.py convgp/convkernels.py display.py datasets/process_cifar10.py mnist.py paper-plots.py opt_tools/__init__.py rectangles.py convgp/svsumgp.py opt_tools/examples/depr-test-scipy-opt.py opt_tools/testing/test_stopwatch.py testing/test_convkerns.py opt_tools/gpflow_tasks.py opt_tools/examples/depr-test-gpflow-opt.py mnist01.py opt_tools/helpers.py opt_tools/examples/test-gpflow-opt.py opt_tools/deprecated.py opt_tools/tasks.py CifarExperiment reshape_patches_for_plot GPflowMultiClassificationTrackerLml MnistExperiment calculate_large_batch_lml jugrun_experiment load_mnist CalculateFullLMLMixin CifarExperiment GPflowTrackLml suppress_print RectanglesImageExperiment ExperimentBase MnistExperiment Mnist01Experiment reshape_patches_for_plot RectanglesExperiment ChooseMnistExperiment ColourPatchConv WeightedConv ConvRBF WeightedConvRBF Conv WeightedColourPatchConv conditional WeightedMultiChannelConvGP MultiOutputInducingSVGP SVConvGP SVColourConvGP MeanFieldSVSumGP FullSVSumGP GPflowOptimisationHelper seq_exp_lin OptimisationLogger OptimisationIterationEvent OptimisationHelper GPflowMultiClassificationTracker GPflowRegressionTracker GPflowBinClassTracker GPflowBenchmarkTrackerBase GPflowOptimisationHelper seq_exp_lin NanError Stopwatch OptimisationHelper StoreOptimisationHistory DisplayOptimisation LogOptimisation OptimisationTimeout OptimisationIterationEvent Timeout GPflowLogOptimisation thin_hist f obj f TestGPflowHelper TestStopwatch TestWeightedConsistency TestGeneral TestConvRBF TestWeightedConv TestColourChannels TestEquivalence int fill shape sqrt floor nan ceil empty enumerate print experiment_name setup run vstack read_data_sets astype MinibatchData value collect compute_log_likelihood tqdm Y _kill_autoflow range X append matrix_triangular_solve transpose square reduce_sum matmul matrix_band_part stack cholesky tile expand_dims min astype unique sleep sleep | # convgp Code for running Gaussian processes with convolutional and symmetric structures. The code is currently being cleaned up and will be continuously published over the next week or so. Things that you can expect to come: - stored trained models, - code to replicate the figures in the paper, - detailed commands to replicate the exact experiments in the paper. ## Paper The accompanying paper can be found on [arXiv](https://arxiv.org/abs/1709.01894). ## Setup ### GPflow with inter-domain support | 2,907 |
marty90/netlytics | ['time series', 'anomaly detection'] | ['Automatic Anomaly Detection in the Cloud Via Statistical Learning'] | algos/remedy/code_4_spark_aggregate_res_asn.py connectors/bro_to_named_flows.py connectors/bind_to_DNS.py algos/clustering/BisectingKMeans.py core/__init__.py algos/__init__.py connectors/tstat_to_named_flows.py connectors/bro_to_DNS.py connectors/tstat_to_HTTP.py algos/remedy/Remedy_DNS_manipulations.py connectors/PCAP_to_DNS.py algos/pain/__init__.py algos/remedy/code_2_find_anomalies_res_dom.py core/connector.py algos/pain/pain.py algos/pain/pain_3_create_evaluation_windows.py algos/domain_traffic.py run_clustering.py connectors/tstat_to_DNS.py core/algo.py algos/remedy/code_3_calculate_params.py algos/save_Dataframe.py algos/remedy/code_5_find_anomalies_res_asn.py algos/pain/pain_4_create_model_matrix.py connectors/squid_to_HTTP.py algos/anomaly_detection/S_H_ESD.py algos/pain/pain_2_create_bags.py algos/remedy/code_8_filter_open_dns.py connectors/bro_to_HTTP.py algos/remedy/code_7_create_final_report.py core/anomaly_detection_algo.py algos/anomaly_detection/univariate_anomalies.py algos/WHAT.py algos/remedy/__init__.py algos/clustering/__init__.py core/clustering_algo.py run_anomaly_detection.py run_job.py connectors/squid_to_named_flows.py algos/anomaly_detection/__init__.py algos/clustering/KMeans.py algos/clustering/DBScan.py algos/remedy/code_6_spark_aggregate_res.py core/S_H_ESD.py algos/top_DNS_servers.py algos/pain/pain_1_create_observation_windows.py algos/pain/pain_5_use_model.py connectors/__init__.py core/utils.py run_query.py algos/clustering/GaussianMixture.py main my_import main my_import RowToStr main my_import main my_import mapRDD DomainTraffic mapRDD SaveDataFrame TopDNSServers mapRDD getBags mapLine mapLineClassify WHAT reduceBags filter_name getFlows is_valid_trigger filter_rules import_dict distance_window_bag S_H_ESD extract_features UnivariateAnomalies BisectingKMeans Spark_DBScan DBScan merge_clusters GaussianMixture KMeans Pain getBags filter_name mapLine create_bags getBags filter_name mapLine chunkIt get_clusters_nb create_model use_model calc_anomalies statistical_anomalies json_to_counter emit_tuples_SLD_COUNT reduce_tuples get3LD get2LD emit_tuples_SLD_ASN getGood2LD parse_line final_map is_valid_ipv4 reduce_tuples emit_tuples parse_line final_map is_valid_ipv4 get3LD get2LD getGood2LD find_anomalies json_to_counter reduce_tuples emit_tuples parse_line final_map is_valid_ipv4 create_report json_to_counter get_domain_info main emit_tuples_SLD_COUNT reduce_tuples get3LD get2LD emit_tuples_SLD_ASN getGood2LD parse_line final_map is_valid_ipv4 RemedyDNSManipulations getQueries Bind_To_DNS mapLine daterange mapLine daterange is_valid_ipv6 Bro_To_DNS is_valid_ip is_valid_ipv4 mapLine Bro_To_HTTP daterange is_valid_ipv6 is_valid_ip is_valid_ipv4 mapConn mapSSL mapLine daterange Bro_To_Named_Flows is_valid_ipv6 is_valid_ip is_valid_ipv4 mapHTTP define_rcode define_class repr_data parse_file define_type parse_packet daterange parse_ans PCAP_To_DNS count_labels parse_antypes define_protocol parse_anttls TSTAT_PCAP_To_DNS mapLine daterange is_valid_ipv6 parse_line is_valid_ip is_valid_ipv4 Squid_To_HTTP mapLine daterange Squid_To_Named_Flows is_valid_ipv6 parse_line is_valid_ip is_valid_ipv4 emit_keys daterange emit_tuples Tstat_To_DNS is_valid_ipv4 mapLine Tstat_To_HTTP filterLine daterange Tstat_To_Named_Flows mapLine filterLine daterange Algo AnomalyDetectionAlgo ClusteringAlgo Connector S_H_ESD generalizedESD get_dataset my_import transform ship_dir zipdir AD_algo_module setAppName add_argument makedirs SparkContext to_csv get_dataset realpath SparkSession loads my_import dirname ArgumentParser run transform vars parse_args join import_module getattr split clustering_algo_module map saveAsTextFile join list asDict isinstance dumps append algo_module createOrReplaceTempView print sql toPandas save name c_bytes s_bytes s_ip defaultdict log10 Counter len time_start s_ip name c_bytes s_pkt s_bytes c_port time_end c_ip s_port c_pkt list keys filter_name dict Counter sub time_start int s_ip name c_bytes s_pkt s_bytes c_port time_end c_ip s_port c_pkt items list sorted defaultdict max end filter_name Window start popleft keys append items sorted Counter distance_window_bag len read float asDict Row list defaultdict connected_components Graph len add set append range enumerate time_start s_ip name dumps append c_ip s_port dump defaultdict len Counter mean loads append std open sum sorted defaultdict items dump chunkIt set dict loads DataFrame open append float len load join str print Counter loads append max open int list defaultdict join values print statistical_anomalies Counter set add open splitlines split ceil float max append json_to_counter sum enumerate join str s_ip is_valid_ipv4 set query resp_code add lower getGood2LD answers startswith append pyasn split join str s_ip is_valid_ipv4 set query resp_code add lower getGood2LD answers append pyasn split str replace int split append split split split join str defaultdict s_ip is_valid_ipv4 Counter query resp_code answers startswith c_ip pyasn split load int items print len close splitlines open json_to_counter append split set add int fillna sort_index sort_values merge apply to_csv splitlines split append DataFrame json_to_counter read_csv len resolver iterrows get_domain_info set asn append DataFrame read_csv split pop join get dump json print strip set add open isfile append makedirs int range days split int total_seconds Row split float is_valid_ip AF_INET6 inet_pton int float split split split PcapReader name write close parse_packet NamedTemporaryFile flush define_class repr_data sport id parse_antypes parse_anttls qname qtype define_rcode qclass dst parse_ans define_type Row StringIO rcode float int time src dport filter split get get get repr_data ancount append rdata range int ttl ancount append range define_type ancount append type range join strftime parse_line sub int float append int float Row float split int fit_seasons adjust_seasons len argmax pop ppf std masked mean sqrt append median abs array range len load int get_DF fromJson output_type my_import connector_module set_schema open ship_dir date split str ZIP_DEFLATED zipdir close addPyFile ZipFile join write walk relpath Normalizer str createOrReplaceTempView otherwise withColumnRenamed sql dataType col schema StringIndexer OneHotEncoder withColumn map createDataFrame drop fit | NetLytics ========= NetLytics is a Hadoop-powered framework for performing advanced analytics on various kinds of networks logs. It is able to parse log files generated by popular network softwares implementing HTTP proxy, DNS server and passive sniffers; it can also parse raw PCAP files. It assumes that log files are stored on HDFS in a Hadoop based cluster. NetLytics uses log files to perform a wide range of advanced network analytics for traffic monitoring and security purposes. All code is written in Python and uses Apache Spark. For information about this Readme file and this tool please write to [[email protected]](mailto:[email protected]) # Table of Content | 2,908 |
masoudpz/AVID-Adversarial-Visual-Irregularity-Detection | ['anomaly detection'] | ['AVID: Adversarial Visual Irregularity Detection'] | unet/__init__.py unet/unet_model.py unet/unet_parts.py fake_data_loader.py solver.py main.py data_loader.py model.py ImageFolder get_loader fake_data_loader get_fake_loader main U_Generator deconv Generator Alex_disc our_Discriminator U_Discriminator conv Discriminator Solver UNet outconv up double_conv down inconv ImageFolder Compose DataLoader fake_data_loader DataLoader Compose sample get_loader model_path get_fake_loader train sample_path Solver makedirs append BatchNorm2d ConvTranspose2d append BatchNorm2d Conv2d | # AVID: Adversarial Visual Irregularity Detection This code repository includes the source code for the [Paper](https://link.springer.com/chapter/10.1007/978-3-030-20876-9_31): ``` AVID-Adversarial-Visual-Irregularity-Detection Mohammad Sabokrou, Masoud Pourreza, Mohsen Fayyaz, Rahim Entezari, Mahmood Fathy, Jürgen Gall, Ehsan Adeli ACCV'18 published paper on the springer ``` The experimentation framework is based on PyTorch;Ir_Mnist Dataset is available in train_mnist as train set and test_mnist_matrix as test set. The source code and dataset (MultiMNIST) are released under the MIT License. See the License file for details. # Requirements and References | 2,909 |
masqm/Faster-Mean-Shift | ['cell segmentation', 'instance segmentation', 'semantic segmentation'] | ['Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking'] | utils/image_tiler.py utils/io/common.py datasets/basic_dataset.py utils/io/text.py transformations/spatial/deformation.py transformations/intensity/np/normalize.py generators/generator_base.py tensorflow_train/utils/print_utils.py transformations/spatial/translation.py graph/run_graph.py bin/postprocess.py bin/color/tif_process.py tensorflow_train/layers/layers.py utils/timer.py graph/node.py bin/color/color.py transformations/intensity/sitk/normalize.py transformations/spatial/rotation.py generators/image_generator.py utils/sitk_np.py utils/io/image.py utils/np_image.py transformations/spatial/common.py bin/clustering.py transformations/intensity/sitk/shift_scale_clamp.py transformations/spatial/base.py tensorflow_train/networks/unet_base.py utils/random.py datasources/datasource_base.py datasources/image_datasource.py transformations/intensity/np/smooth.py utils/batch_seed.py transformations/intensity/base.py datasets/debug_image_dataset.py tensorflow_train/utils/data_format.py transformations/intensity/sitk/smooth.py utils/mean_shift_cosine_gpu.py datasources/cached_image_datasource.py datasets/dataset_base.py transformations/intensity/np/shift_scale_clamp.py tensorflow_train/layers/conv_lstm.py datasets/graph_dataset.py bin/dataset.py utils/sitk_image.py transformations/spatial/landmark.py generators/transformation_generator_base.py transformations/spatial/composite.py bin/segment_and_track.py transformations/spatial/scale.py transformations/spatial/flip.py tensorflow_train/layers/initializers.py tensorflow_train/layers/normalizers.py tensorflow_train/utils/tensorflow_util.py tensorflow_train/networks/unet_lstm_dynamic.py InstanceImageCreator calculate_label_overlap InstanceTracker InstanceMerger intersection_over_union intersection union Dataset eaualHist get_instance_tracker_parameters image_sizes_for_dataset_name get_instance_image_creator_parameters get_dataset_parameters MainLoop manipu_cv manipu_cv2 BasicDataset DatasetBase DebugImageDataset GraphDataset LRUCacheWithMissingFunction CachedImageDataSource DataSourceBase ImageDataSource GeneratorBase ImageGenerator TransformationGeneratorBase Node LambdaNode run_graph ConvLSTMCell ConvGRUCellIndy ConvGRUCell ConvGRUCellExtended conv2d_transpose mult conv3d_transpose_unknown_dim conv3d flatten pad_for_conv max_pool3d add conv2d upsample3d conv3d_transpose dropout upsample2d concat_flattened avg_pool3d max_pool2d dense alpha_dropout avg_pool2d conv3d_unknown_dim concat_channels batch_norm_dense layer_norm instance_norm batch_norm UnetBase3D UnetBase2D UnetBase UnetRecurrentWithStates UnetRecurrentCell2D ConvGRUCell UnetRecurrentCell UnetIntermediateGruWithStates2D network_single_frame_with_lstm_states get_tf_data_format get_tf_data_format_3d get_image_axes get_channel_size get_image_dimension get_tf_data_format_2d get_batch_channel_image_size get_image_size channels_first_to_channels_last get_image_size_3d get_image_size_2d get_channel_index_2d channels_last_to_channels_first get_tensor_shape get_channel_index get_channel_index_3d print_conv_parameters print_dense_parameters print_shape_parameters printable_normalization print_pool_parameters print_tensor_shape print_upsample_parameters printable_initializer printable_activation print_dropout_parameters masked_apply reduce_sum_weighted save_reduce_mean reduce_median reduce_mean_support_empty save_divide reduce_sum_masked create_placeholders print_progress_bar masked_bit create_reset_metric reduce_mean_weighted create_placeholder reduce_median_masked most_significant_bit get_reg_loss reduce_mean_masked IntensityTransformBase robust_min_max normalize_mr_robust normalize_robust scale_min_max normalize ShiftScaleClamp gaussian robust_min_max min_max normalize_robust scale_min_max normalize rescale clamp ShiftScaleClamp gaussian SpatialTransformBase create_composite flipped_dimensions Composite Output Deformation Input CenteredInput Fixed FlipTransformBase Random LandmarkTransformBase Center Fixed RotationTransformBase Random ScaleTransformBase Random Fixed OutputSpacingToUniformSpacing InputSpacingToUniformSpacing Fit RandomUniform UniformSpacingToInputSpacing FitFixedAr UniformSpacingToOutputSpacing Random Fixed OutputCenterToOrigin OriginToOutputCenter RandomFactorInput OutputCenterTransformBase TranslateTransformBase OriginToInputCenter InputCenterToOrigin meanshift_torch gaussian cos_batch get_weight TilerBase LandmarkTiler ImageTiler gpu_seed_generator MeanShiftCosine gpu_seed_adjust mean_shift_cosine estimate_bandwidth get_N find_maximum_coord_in_image nms_2d smooth_label_images merge_label_images draw_circle split_label_image argmax split_label_image_with_unknown_labels convex_hull split_connected_components split_by_axis dilation_square relabel_ascending find_quadratic_subpixel_maximum_in_image distance_transform dilation_circle center_of_mass roll_with_pad gallery largest_connected_component erosion_square find_maximum_in_image connected_component int_uniform bool_bernoulli float_gaussian float_uniform int_gaussian copy_information_additional_dim resample reduce_dimension split_vector_components merge_label_images rgba_to_rgb split_label_image argmax get_sitk_interpolator copy_information transform_np_output_to_sitk_input transform_np_output_to_input distance_transform accumulate merge_vector_components largest_connected_component apply_np_image_function image4Dto3D connected_component sitk_to_np_no_copy np_to_sitk sitk_list_to_np sitk_to_np Timer copy_files_to_folder create_directories_for_file_name create_directories read write_np write write_nd_np write_np_rgb read_meta_data load_dict_csv save_string_txt load_dict_idl save_list_txt save_list_csv load_list_csv save_dict_csv load_list union intersection count_nonzero max bitwise_and zeros bitwise_or range array equalizeHist zeros uint8 range zeros uint8 range update get list put dict LifoQueue parents values insert pad array get_channel_index print_conv_parameters activation pad_for_conv normalization normalization print_conv_parameters activation print_upsample_parameters average_pooling2d print_pool_parameters print_pool_parameters max_pooling2d print_conv_parameters activation pad_for_conv normalization normalization activation print_conv_parameters as_list print_conv_parameters conv3d activation pad_for_conv normalization get_variable as_list print_conv_parameters activation normalization conv3d_transpose get_variable print_upsample_parameters identity print_pool_parameters average_pooling3d max_pooling3d print_pool_parameters concat get_channel_index print_shape_parameters concat print_shape_parameters add_n print_shape_parameters multiply print_shape_parameters print_shape_parameters print_dropout_parameters print_dropout_parameters normalization print_dense_parameters activation get_tf_data_format_2d dtype zero_state relu print UnetRecurrentCell2D concat unet_recurrent_cell_1 UnetIntermediateGruWithStates2D unet_recurrent_cell_0 embeddings_normalization as_list as_list as_list as_list print as_list constant variance_scaling isinstance truncated_normal as_list format print printable_normalization printable_initializer printable_activation as_list format print printable_normalization printable_initializer printable_activation as_list format print as_list format print print format as_list format print print int float format float32 reduce_sum cast cast boolean_mask constant left_shift cast while_loop dtype constant UPDATE_OPS get_collection to_int32 boolean_mask op where scatter_nd shape sort size int robust_min_max min max robust_min_max MinimumMaximumImageFilter Execute ShiftScale float ClampImageFilter SetLowerBound SetUpperBound float Execute Transform AddTransform sitkIdentity range TransformPoint len reshape T norm cuda where get_weight numpy cos_batch append sum cuda int NearestNeighbors check_random_state check_array len kneighbors gen_batches fit shuffle list range e log flatten get_N kneighbors sorted meanshift_torch estimate_bandwidth ones shape sum range gpu_seed_generator gpu_seed_adjust size fill enumerate items fit zeros array len shape int32 unravel_index argmax array find_maximum_coord_in_image find_maximum_coord_in_image size astype float32 copy range sort asarray nonzero split append dtype astype append dtype tolist astype list zeros_like astype range enumerate len dtype tolist zeros_like stack argmax zeros int float32 shape sqrt ceil zeros enumerate len label astype append range bincount flatten connected_component argmax ones zeros circle ones shape circle append pad slice normal ndarray isinstance uniform ndarray isinstance normal ndarray isinstance uniform ndarray isinstance binomial ndarray isinstance SetOutputSpacing SetDefaultPixelValue Execute ResampleImageFilter SetOutputOrigin get_sitk_interpolator GetPixelID GetDimension SetTransform SetSize SetOutputPixelType SetInterpolator SetIndex VectorIndexSelectionCastImageFilter GetNumberOfComponentsPerPixel append range Execute ComposeImageFilter Execute split_vector_components GetSpacing reshape SetSpacing tolist GetDimension float32 SetOrigin zeros GetOrigin SetDirection GetSpacing SetSpacing GetDirection SetOrigin GetOrigin SetDirection list GetDimension GetSize list GetSize Extract GetDimension append range CopyInformation stack np_to_sitk np_to_sitk sitk_to_np_no_copy CopyInformation CopyInformation np_to_sitk GetInverse np_to_sitk GetSpacing split_by_axis GetSize resample SetSpacing append GetOrigin GetInverse np_to_sitk split_by_axis resample SetSpacing append GetObjectCount ConnectedComponentImageFilter FullyConnectedOn Execute ChangeLabelImageFilter GetNumberOfPixels SetChangeMap LabelShapeStatisticsImageFilter range Execute copy_information f sitk_to_np np_to_sitk append stack sitk_to_np_no_copy astype create_directories dirname makedirs create_directories copy GetImageFromArray transpose write GetImageFromArray transpose write create_directories_for_file_name ImageFileWriter Execute write ComposeImageFilter Execute ReadImage Compose VectorIndexSelectionCast GetNumberOfComponentsPerPixel SetFileName ReadImageInformation ImageFileReader LoadPrivateTagsOn create_directories_for_file_name create_directories_for_file_name create_directories_for_file_name create_directories_for_file_name | # Faster_Mean_Shift GPU accelerated Faster Mean-shift algorithm for speeding up the recurrent neural network (RNN) based pixel embedding framework for holistic cell segmentation and tracking. Here is a brief introduction on how to run it. A more commonly used Faster Mean-shift algorithm for Euclidean Distance clustering was developed. Please see [Faster-Mean-Shift-Euc](https://github.com/masqm/Faster-Mean-Shift-Euc). ## Environment Win10 VS2019 Anacoda 2020.02 The packages requirement please see [requirements.txt](https://github.com/masqm/Faster_Mean_Shift/blob/master/requirements.txt "requirements.txt") ## Preparation 1. Download the datasets from the [celltracking challenge](http://www.celltrackingchallenge.net/) and extract them under an ***input_path***. | 2,910 |
masyagin1998/robin | ['image retrieval'] | ['ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents'] | src/unet/train.py src/unet/utils/img_processing.py src/dataset/stsl-download.py src/dataset/dataset.py src/unet/model/unet.py src/unet/binarize.py src/metrics/metrics.py split_img_overlay mkdir_s save_imgs process_img shuffle_imgs main parse_args main parse_args mkdir_s Metrics parse_args main meter main parse_args mkdir_s SaltPepperNoiseAugmentor create_callbacks dice_coef_loss parse_args jacard_coef GaussianNoiseAugmentor InvertPartAugmentor main dice_coef jacard_coef_loss Visualisation ParallelDataGenerator down_layer up_layer double_conv_layer unet add_border mkdir_s combine_imgs normalize_in postprocess_img process_unet_img binarize_img preprocess_img split_img normalize_gt append BORDER_CONSTANT copyMakeBorder join mkdir_s str imwrite enumerate split_img_overlay replace COLOR_BGR2GRAY save_imgs imread cvtColor seed join str rename randint listdir range len makedirs add_argument ArgumentParser copy2 str list map shuffle_imgs input parse_args mkdir_s format partial replace iglob shuffle enumerate join time print output rmtree save_imgs get_event_loop run_until_complete join remove replace system Metrics exit imwrite batchsize unet astype load_weights compile weights binarize_img float32 join mkdir_s debug TensorBoard AltModelCheckpoint append Visualisation flatten sum flatten sum multi_gpu_model save_weights augmentate seed val create_callbacks fit_generator ParallelDataGenerator int evaluate_generator train len double_conv_layer double_conv_layer down_layer Model up_layer Input astype float32 astype float32 BORDER_CONSTANT copyMakeBorder append zeros float shape add_border uint8 combine_imgs normalize_in astype split_img append array predict threshold THRESH_OTSU THRESH_BINARY process_unet_img postprocess_img preprocess_img | # robin <img src="static/logo/robin.png" height="150" width="150"> **robin** is a **RO**bust document image **BIN**arization tool, written in Python. - **robin** - fast document image binarization tool; - **metrics** - script for measuring the quality of binarization; - **dataset** - links for DIBCO 2009-2018, Palm Leaf Manuscript and my own datasets with original and ground-truth images; scripts for creating training data from datasets and downloading imagest from [STSL](http://stsl.ru/); - **articles** - selected binarization articles, that helped me a lot; - **weights** - pretrained weigths for **robin**; ## Tech **robin** uses a number of open source projects to work properly: | 2,911 |
mataney/BootstrappingRelationExtractors | ['relation extraction', 'data augmentation'] | ['Bootstrapping Relation Extractors using Syntactic Search by Examples'] | generation_outputs/annotate_like_search.py generation_outputs/prepare_entities_files.py scripts/filter_generations/filter_by_triggers.py scripts/filter_generations/filter_by_entities.py classification/re_processors.py run_lm_finetuning.py classification/docred_config.py scripts/search/test_download_search_examples.py classification/tacred.py models/mtb.py classification/split_train_pareto.py scripts/generation_preprocess/relation_canonical_form.py classification/test_docred.py run_generation.py classification/re_config.py classification/evaluation/docred_evaluation.py generation_outputs/switch_entities_of_gens.py classification/docred.py generation_outputs/convert_s_o_to_es.py scripts/search/patterns_from_generation.py scripts/search/download_patterns_config.py classification/test_tacred.py scripts/relations_ratio.py scripts/search/download_search_examples.py scripts/check_num_of_examples.py classification/evaluation/tacred_evaluation.py classification/evaluation/test_docred_evaluation.py classification/tacred_config.py scripts/generation_preprocess/create_tacred_datafiles.py run_classification.py set_seed evaluate main train load_and_cache_examples set_seed prepare_xlm_input prepare_transfoxl_input prepare_ctrl_input adjust_length_to_model main prepare_xlnet_input TextDataset set_seed evaluate train mask_tokens main _rotate_checkpoints load_and_cache_examples DocREDExample DocREDInputFeatures DocREDUtils DocREDProcessor simple_accuracy Processors wrap_text RelationMapping TitleNames REProcessor f1_ignore_negative_class input_features_factory convert_examples_to_features compute_metrics TACREDInputFeatures TACREDProcessor TACREDExample TestDocREDProcessor TestDocREDUtils TestDocREDExample TestTACREDProcessor TestTACREDExample main eval correct_entity_types gen_train_facts score has_pronouns read_json parse_arguments test_half test_two_different_relations test_full test_full_with_diff_evidences create_args test_confidence_works test_zero main main wrap_text find_subject_and_objects mark_just_one_entity read_entities_list main nationalities dates switch_dates read_ents_from_file cities switch_entity_but_not_pronouns religions get_similar_entities main switch_religions MTBClassificationHead RobertaForRelationClassification main main filter_out main filter_out specific_predicate_for_relation leave_some_relations_out skip_disallowed_pronouns truncate_noise anonymize mark_args clean_token main update_file_lengths get_file_names download_from_spike_search clean_special_tokens merge_and_save_examples merge_positive_examples_and_save remove_same_sent_id merge_negative_examples_and_save_given_relation read_entities_list main map_array_given_header seperate_entities entities_validator_for_relation query_params switch_ent_to_spike_syntax print_downloaded_examples pattern_to_add create_explanation_dictionary merge_patterns_with_triggers convert_wrapped_gen_to_query main query_params enough_similar_patterns populate_data test_seperate_entities_e1_before_and_after_e2 test_seperate_entities_some_e2_before_e1_some_not test_seperate_entities_all_e2_before_e1 test_seperate_entities_some_e1_before_e2_some_not test_seperate_entities_e2_before_and_after_e1 test_seperate_entities_e1_equal_to_e2 test_seperate_entities_all_e1_before_e2 seed manual_seed_all manual_seed gradient_accumulation_steps save_only_best model get_linear_schedule_with_warmup tuple clip_grad_norm_ zero_grad DataLoader DataParallel DistributedDataParallel max_grad_norm output_dir save max exists str initialize set_seed logging_steps load_state_dict master_params state_dict SummaryWriter format remove_tree close mean save_pretrained num_train_epochs info fp16 trange per_gpu_train_batch_size max_steps enumerate load join int n_gpu items evaluate model_name_or_path AdamW backward add_scalar print makedirs dumps copy_tree tqdm parameters step train_batch_size len tuple DataLoader DataParallel argmax max open eval_batch_size exp per_gpu_eval_batch_size compute_metrics append SequentialSampler update dump format eval info zip load_and_cache_examples join n_gpu makedirs tqdm numpy len pop join str ratio_negative_examples format load get_examples_by_set_type max_seq_length data_dir barrier get_labels type_independent_neg_sample convert_examples_to_features save info tensor num_positive_examples relation_name enable_attach from_pretrained resize_token_embeddings warning ArgumentParser device output_dir save num_positive_examples eval_all_checkpoints setLevel exists training_method basicConfig add_special_tokens set_seed list set_device get_labels device_count parse_args to WARN update ratio_negative_examples init_process_group lower save_pretrained info fp16 wait_for_attach task_name train join n_gpu evaluate model_name_or_path print add_argument makedirs barrier copy_tree dict type_independent_neg_sample bool load_and_cache_examples local_rank relation_name len encode info str list input xlm_language keys decode prepare_input out_file open length tolist model_type generate encode append get close adjust_length_to_model write num_return_sequences TextDataset join sorted format save_total_limit glob rmtree match output_dir info append max len mask_token bool convert_tokens_to_ids clone randint shape masked_fill_ tensor mlm_probability full len resize_token_embeddings device to _rotate_checkpoints output_dir device tensor to special_tokens_file block_size do_train max_len_single_sentence min insert join text input_features_class len input_features_factory info append label float enumerate encode_plus precision_recall_fscore_support load join dump list replace tuple set add exists open load dump sorted gold_dir sort pred_file gold_file set add eval output_file range enumerate len parse_args add_argument ArgumentParser remove_pronouns sorted dump output_file open float sum enumerate len Namespace create_args evaluation_main create_args evaluation_main create_args evaluation_main create_args evaluation_main create_args create_args evaluation_main wrap_text tqdm find_subject_and_objects mark_just_one_entity enumerate append sorted read_entities_list Counter set get_similar_entities findall sub sub sub startswith listdir remove annotate_text tqdm lower append from_env write_to_file e2_entities generation_file e1_entities read_file model_folder filter_out trigger_list_path one_form_per_relation leave_some_relations_out skip_disallowed_pronouns anonymize_tgt mark_relation_args replace truncate_noise mark_args clean_token src_and_tgt_one_file_with_go specific_predicate_for_relation anonymize lower insert min max len update_file_lengths merge_patterns get_file_names dataset merge_and_save_examples download download_from_spike_search append join get_relation_name_from_file_name listdir append items defaultdict read_entities_list items tqdm merge_positive_examples_and_save merge_negative_examples_and_save_given_relation entities_validator_for_relation writer join wrap_text reader print sort writerow close clean_special_tokens add split map_array_given_header next enumerate open writer join wrap_text reader print sort writerow close clean_special_tokens set add tqdm split chain map_array_given_header next enumerate open split items defaultdict print tqdm post download sum enumerate query_params makedirs split load items open relation print_downloaded_examples create_explanation_dictionary merge_patterns_with_triggers download_examples download_explanations reader replace print sort zip map_array_given_header keys values open get rstrip defaultdict relation sort tqdm post append query_params makedirs join items print set add sub findall split findall sub switch_ent_to_spike_syntax split populate_data populate_data populate_data populate_data populate_data populate_data populate_data | # Bootstrapping Relation Extractors Implementation of "Bootstrapping Relation Extractors using Syntactic Search by Examples". ## Classification ### Classification and Evaluation You can find how to run the classification and evluation script in `run_classification.sh`. ##### CMD: ``` bash run_classification.sh ``` Generation | 2,912 |
matejklemen/got-link-prediction | ['link prediction'] | ['Predicting kills in Game of Thrones using network properties'] | feature-extraction/features.py n2v_approach.py link_prediction.py data-collection/graph-creation.py baseline_index calculate_auc calculate_recall embed_link adamic_adar_index sample_negative_examples extract_features pref_index analyze_vectors find_edges_by_episode compute_index get_additional_features calculate_precision ml_approach display_results leiden_index print list TSNE plot print text exit title array figure savefig keys range fit_transform len append index_func number_of_nodes find_partition tuple size incident total_weight_from_comm hash ModularityVertexPartition find list zip choice sum sum int effective_op items add set add choice nodes set startswith get_additional_features deepcopy sorted atleast_2d list zip fit random sample_negative_examples extend shuffle find_edges_by_episode predict_proba remove_edges_from KNeighborsClassifier vector_size range enumerate len | # Kill prediction in Game of Thrones **Authors**: Jaka Stavanja, Matej Klemen This repository contains supporting code for our experiments with link prediction for the Game of Thrones TV show. The data used: - a *kills network*, constructed from deaths from first 6 seasons of the show (obtained from https://deathtimeline.com/ and additionally cleaned up) - nodes are characters and they are connected if one character killed the other, - a *social network* - nodes are characters and are connected if they appear closely in the books (used as auxiliary data; obtained from https://github.com/melaniewalsh/sample-social-network-datasets/). The paper and slides with a quick description of the problem and solutions are available in the root of the | 2,913 |
mateuszjurewicz/bornhack_ml_crashcourse | ['safe exploration', 'style transfer'] | ['A Neural Algorithm of Artistic Style', 'Concrete Problems in AI Safety'] | style_transfer.py get_model load_img deprocess_img get_content_loss get_style_loss gram_matrix compute_loss get_feature_representations run_style_transfer preprocess_input img_to_array ANTIALIAS size resize expand_dims max open astype copy int reshape matmul as_list gram_matrix load_img model get_content_loss get_style_loss float zip VGG19 initializer deprocess_img model assign clip_by_value compute_loss save Session run fromarray str set_session range close eval load_weights get_feature_representations load_img minimize Variable print global_variables_initializer get_model array |       # Machine Learning Crash Course | Bornhack 2019 ## Introduction This repository contains the code, slides and references for a 45 minute presentation delivered at the Bornhack 2019 conference. This readme is meant as a **one-stop shop for finding your way into the world of Machine Learning & Artificial Intelligence**. | 2,914 |
mathurinm/A5G | ['sparse learning'] | ['From safe screening rules to working sets for faster Lasso-type solvers'] | demo_lasso.py demo_multitask.py a5g/utils.py download_preprocess_finance.py demo_homotopy.py demo_sparse.py setup.py a5g/solver.py a5g/__init__.py demo_finance.py a5g/homotopy.py decompress_finance preprocess_finance download_finance lasso_path blitz_path lasso_path_mt sklearn_path_mt norml21 feature_prios preprocess_data mt_dual configure_plt primal norml2inf compute_alpha dual mt_primal mt_compute_alpha plot_res mt_feature_prios ST download BZ2Decompressor a5g_lasso_sparse data issparse time solver print indptr copy indices shape asfortranarray zeros max range len a5g_mt print copy shape any zeros sum max range len MultiTaskLasso norm time set_verbose print LassoProblem solve copy shape set_use_intercept set_tolerance zeros duality_gap range x dot T abs minimum dot T minimum T norm dot sqrt sum update rc set_style set_context yscale show list subplots arange set_xticklabels set_xlabel FormatStrFormatter tight_layout bar set_ylabel set_xticks set_major_formatter legend savefig range len issparse normalize copy sort_indices add_dummy_feature | # WARNING: This code is no longer maintained. We recommend you use the [celer package](https://github.com/mathurinm/celer) instead. # Aggressive gap screening rules for faster Lasso-type solvers This repository hosts the implementation of fast solvers for the Lasso and multi-task Lasso problem. The algorithms are in ```./a5g/*_fast.pyx```. Currently implemented are: * Lasso solver on dense and sparse data * Multi-task Lasso solver on dense data (aka Group Lasso with groups equal to rows) The algorithms are written in Cython, using calls to BLAS when possible. | 2,915 |
matsuren/HexRUNet_pytorch | ['semantic segmentation', 'autonomous driving'] | ['Orientation-aware Semantic Segmentation on Icosahedron Spheres'] | train.py utils/projection_helper.py models/unfold_nn.py models/hexrunet.py utils/geometry_helper.py dataloader/__init__.py main UnfoldIcoDataset ToTensor Normalize ResBlock HexRUNet_C UnfoldConv2d UnfoldMaxPool2d UnfoldUpsample UnfoldBatchNorm2d UnfoldReLU HexConv2d UnfoldAvgPool2d UnfoldVertex get_base_icosahedron distort_unfold_to_imgcoord get_rect_idxs calculate_weight get_unfold_imgcoord get_weight_alpha subdivision get_unfold_icosahedron VertexIdxManager unfold_subdivision distort_grid get_base_unfold unfold_padding get_icosahedron uv2xyz uv2img_idx img2ERP remap erp2sphere uv2proj_img_idx xyz2uv genuv batch_size model zero_grad pretrained DataLoader save device max StepLR add_text len Adam strftime epochs OrderedDict level load_state_dict set_postfix HexRUNet_C append parse_args to sum CrossEntropyLoss range SummaryWriter format Compose close UnfoldIcoDataset eval item vars keys enumerate MNIST load join criterion backward print add_scalar dumps tqdm parameters train step makedirs get_base_icosahedron subdivision get_base_unfold unfold_subdivision get_unfold_icosahedron distort_unfold_to_imgcoord distort_grid calculate_weight get_unfold_imgcoord get_icosahedron T reshape pi cross dot stack sin upsample range get_base_icosahedron pi sin append array range len UnfoldVertex VertexIdxManager copy get_next zip append range det norm tan partial arccos T copy dot stack np_round eye round array append range items sorted get_rect_idxs T add set append round log len ndarray isinstance copy from_numpy pad unsqueeze permute append numpy range arccos cross array sum unfold_padding clip uv2xyz arange float64 astype cos pi copy stack sin meshgrid xyz2uv cos sin sqrt arctan2 tan cos zeros map_coordinates range int pi remap roll uv2proj_img_idx round genuv clip pi uv2img_idx xyz2uv remap | ## HexRUNet PyTorch An unofficial PyTorch implementation of ICCV 2019 paper ["Orientation-Aware Semantic Segmentation on Icosahedron Spheres"](http://openaccess.thecvf.com/content_ICCV_2019/html/Zhang_Orientation-Aware_Semantic_Segmentation_on_Icosahedron_Spheres_ICCV_2019_paper.html). Only HexRUNet-C for Omni-MNIST is implemented right now. ## Requirements Python 3.6 or later is required. Python libraries: - PyTorch >= 1.3.1 - torchvision - tensorboard - tqdm - [igl](https://libigl.github.io/libigl-python-bindings/) | 2,916 |
matsuren/crownconv360depth | ['depth estimation'] | ['360$^\\circ$ Depth Estimation from Multiple Fisheye Images with Origami Crown Representation of Icosahedron'] | train.py dataloader/omnistereo_dataset.py utils/__init__.py utils/projection_helper.py models/icosweepnet.py utils/feature_integration.py dataloader/icosahedron_dataset.py evaluation.py models/crown_nn.py models/icospherical_sweeping.py utils/geometry_helper.py models/feature_extraction.py dataloader/custom_transform.py models/__init__.py dataloader/omnimnist_dataset.py models/costvolume_regularization.py dataloader/__init__.py visualize_depth.py main run main run ToTensor RandomShift Normalize ColorJitter uv2xyz mapxy_fisheye_to_ico uv2img_idx ico_to_erp FisheyeToIcoDataset ico_to_erp_idx_weight xyz2uv genuv Normalize ToTensor OmniMNISTDataset load_invdepth load_image OmniStereoDataset load_poses add_tuple CostRegularization CrownBatchNorm2d CrownConv2d CrownBatchNorm3d CrownConv2dBNRelu CrownConv3dBNRelu CrownConv3d BasicBlock FeatureExtraction icospherical_sweep_grid get_KDTree IcoSphericalSweeping to_torch_grid triangle_interpolation DisparityRegression IcoSweepNet batch_to_list calculate_weight _index_select_volume vertex_feat_to_unfold_feat index_select get_count_for_integration _index_select_img col_row_integration unfold_feat_to_vertex_feat UnfoldVertex get_base_icosahedron distort_unfold_to_imgcoord get_rect_idxs get_unfold_imgcoord get_vertexid_to_loc subdivision get_unfold_icosahedron VertexIdxManager unfold_subdivision _weight_from_three_nearest distort_grid get_unfold_imgcoord_row weight_for_triangle_interpolation get_base_unfold get_icosahedron uv2xyz uv2img_idx img2ERP remap erp2sphere uv2proj_img_idx xyz2uv genuv apply_colormap evaluation_metrics InvDepthConverter model zero_grad numpy save OrderedDict set_postfix append to sum range cat format evaluation_metrics eval mkdir item info enumerate ndisp join backward print smooth_l1_loss tqdm invdepth_to_index cpu train step len batch_size pretrained DataLoader DataParallel pose_dict device setLevel run InvDepthConverter addHandler level load_state_dict dirname parse_args to ocams IcoSweepNet format Compose OmniStereoDataset val_list info vars ndisp load FileHandler join root_dir FisheyeToIcoDataset dumps idepth_level ico_to_erp cam_list zip add_image make_grid add_scalar vertex_feat_to_unfold_feat apply_colormap idepth_level train_list save StepLR add_text Adam strftime epochs copytree range SummaryWriter replace close items error depth_level parameters step makedirs int T reshape inv dot pad world2cam get_icosahedron len meshgrid stack arange pi cos sin sqrt arctan2 clip pi uv2xyz reshape cKDTree query weight_for_triangle_interpolation get_icosahedron genuv ico_to_erp_idx_weight squeeze reshape eps imread IMREAD_ANYDEPTH imread COLOR_BGR2GRAY cvtColor COLOR_BGR2RGB list inv map as_matrix eye append split get_icosahedron stack T reshape float inv get_KDTree deg2rad dot query weight_for_triangle_interpolation frombuffer get_icosahedron to device zeros get_unfold_imgcoord_row get_unfold_imgcoord range arccos get_unfold_imgcoord concatenate cross shape sum array clip get_icosahedron size batch_to_list int calculate_weight get_unfold_imgcoord log2 get_unfold_imgcoord_row get_count_for_integration device zeros to range unsqueeze_ int get_unfold_imgcoord get_unfold_imgcoord_row device log vertex_feat_to_unfold_feat unfold_feat_to_vertex_feat index_select reshape device index_select reshape device get_base_icosahedron subdivision get_base_unfold unfold_subdivision join distort_unfold_to_imgcoord savez get_unfold_icosahedron distort_grid realpath dirname exists get_unfold_imgcoord shape stack ravel full enumerate T reshape pi cross dot stack sin upsample range get_base_icosahedron pi sin append array range len UnfoldVertex VertexIdxManager copy get_next zip append range det norm tan partial arccos T copy dot stack np_round eye round array append range items sorted get_rect_idxs T add set append round log len range get_unfold_imgcoord _weight_from_three_nearest cross norm uv2xyz float64 astype cos copy sin xyz2uv tan cos zeros map_coordinates range int pi remap roll uv2proj_img_idx round genuv uv2img_idx xyz2uv remap uint8 transpose squeeze astype max isinstance flatten shape mean sqrt zip append numpy cat | # IcoSweepNet using CrownConv [](https://arxiv.org/abs/2007.06891)   PyTorch implementation of our IROS 2020 paper [360° Depth Estimation from Multiple Fisheye Images with Origami Crown Representation of Icosahedron](#). The preprint is available in [arXiv](https://arxiv.org/abs/2007.06891). [](https://youtu.be/_vVD-zDMvyM) ## Publication Ren Komatsu, Hiromitsu Fujii, Yusuke Tamura, Atsushi Yamashita and Hajime Asama, "360° Depth Estimation from Multiple Fisheye Images with Origami Crown Representation of Icosahedron", Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2020), 2020. | 2,917 |
matthew-liu/beta-vae | ['style transfer'] | ['Deep Feature Consistent Variational Autoencoder'] | analyze.py train.py models.py preprocess.py utils.py get_z get_attr_ims linear_interpolate plot_loss latent_arithmetic generate get_average_z DFCVAE BetaVAE get_attributes ImageDiskLoader split_dataset get_ims ImageMemoryLoader get_attr train test restore show_images plot restore_latest write_log save read_log to eval to eval eval linspace get_z zeros eval unsqueeze latent_size eval linspace plot xlabel ylabel title savefig figure legend zip randint get_attr ImageDiskLoader len append append im_transform crop open time format model backward print len zero_grad dataset item to step loss enumerate ctime print eval load Parameter data str int sorted isinstance list print join size set copy_ add keys is_available prod state_dict glob int sorted restore sorted remove glob print makedirs dirname state_dict open dump dirname makedirs exists show subplot str axis imshow title savefig figure enumerate len show xlabel ylabel title figure | # Face Generation Using Variational Autoencoders This repo contains training code for two different VAEs implemented with Pytorch. <br /> I used the [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) Dataset for training, with 182637 training images and 19962 testing images. <br /> Trained model can be found in [/checkpoints](/checkpoints).  ## Model structures: #### [β-VAE [1]](https://openreview.net/pdf?id=Sy2fzU9gl):  #### [DFC-VAE [2]](https://arxiv.org/abs/1610.00291):  | 2,918 |
matthew-norton/Diametrical_Learning | ['generalization bounds'] | ['Diametrical Risk Minimization: Theory and Computations'] | models/fc_net.py models/resnet.py fast_random.py run_MNIST_FC.py drm_train_test.py models/wrap_net.py utils.py datastore.py run_CIFAR10_FC.py run_CIFAR10_resnet20.py get_MNIST get_CIFAR10 MultithreadedRNG plot_net save_nets Simple_Net resnet110 resnet20 ResNet LambdaLayer resnet44 resnet1202 resnet56 resnet32 _weights_init BasicBlock Wrapper_Net SubLoader DataLoader Compose SubLoader DataLoader Compose save set_size_inches format plot xlabel close ylabel title savefig figure legend enumerate weight kaiming_normal_ __name__ | # SGD-based Diametrical Risk Minimization (DRM) in Pytorch This is an implementation of the SGD-DRM algorithm from paper [1] : "Diametrical Risk Minimization: Theory and Computations", M. Norton and J. O. Royset. https://arxiv.org/abs/1910.10844. This implementation can be customized to apply SGD-DRM to any network architecture (via pytorch) and loss function (see note in implementation details below). It also uses a fast multithreaded sampling scheme to make the gamma-neighborhood sampling steps of the algorithm more efficient. Requirements: - Python 3.6+ - pytorch 1.2+ - torchvision 0.4+ - numpy 1.17+ - matplotlib 3.1.1+ (for utils.plot_net ) - seaborn 0.9+ (for drm_train_test.sample_neighborhood_losses) | 2,919 |
matthewwicker/IterativeSalienceOcclusion | ['autonomous navigation'] | ['Robustness of 3D Deep Learning in an Adversarial Setting'] | VoxNet.py ModelNet.py ISO.py ModelTraining/KITTI_Train_Point.py ModelTraining/VoxNetTrain.py PointNet.py ModelTraining/PointNetTrain.py ModelTraining/KITTI_Train_Vox.py | # Iterative Salience Occlusion Code Repository to check robustness of 3D Deep Learning (Volumetric and PointNet) to occlusion attacks. This repository reproduces the results reported in the CVPR2019 paper: ['Robustness of 3D Deep Learning in an Adversarial Setting'](https://arxiv.org/abs/1904.00923) This repository also contains all of the code and data for completing the following tasks: * Training of PointNet and VoxNet architectures on ModelNet and KITTI * Evaluating trained architectures with random and saliency occlusion * Plotting/Displaying 3D files from the dataset + their critical sets * Reproducing Plots from the paper. Things that can be requested via email: | 2,920 |
mattpoggi/mono-uncertainty | ['depth estimation', 'monocular depth estimation'] | ['On the uncertainty of self-supervised monocular depth estimation'] | networks/__init__.py generate_maps.py evaluate.py extended_options.py networks/decoder.py UncertaintyOptions batch_post_process_disparity get_mono_ratio evaluate MyDataParallel DepthUncertaintyDecoder meshgrid shape linspace imwrite uint16 num_layers DataLoader output_dir max cuda eval_split len load_weights_folder load_state_dict ResnetEncoder eval_stereo expanduser range imsave state_dict update format concatenate readlines astype ProgressBar qual KITTIRAWDataset DepthUncertaintyDecoder eval get_mono_ratio bootstraps eval_mono load join num_ch_enc snapshots print data_path makedirs | # On the uncertainty of <br> self-supervised monocular depth estimation Demo code of "On the uncertainty of self-supervised monocular depth estimation", [Matteo Poggi](https://vision.disi.unibo.it/~mpoggi/), [Filippo Aleotti](https://filippoaleotti.github.io/website/), [Fabio Tosi](https://vision.disi.unibo.it/~ftosi/) and [Stefano Mattoccia](https://vision.disi.unibo.it/~smatt/), CVPR 2020. **At the moment, we do not plan to release training code.** [[Paper]](https://mattpoggi.github.io/assets/papers/poggi2020cvpr.pdf) - [[Poster]](https://mattpoggi.github.io/assets/papers/poggi2020cvpr_poster.pdf) - [[Youtube Video]](https://www.youtube.com/watch?v=bxVPXqf4zt4) <p align="center"> <img src=https://mattpoggi.github.io/assets/img/uncertainty/poggi2020cvpr.gif> </p> ## Citation ```shell @inproceedings{Poggi_CVPR_2020, | 2,921 |
maunzzz/cross-season-segmentation | ['semantic segmentation'] | ['A Cross-Season Correspondence Dataset for Robust Semantic Segmentation'] | utils/validator.py layers/corr_class_loss.py eval/segment_images_in_folder.py utils/corr_transforms.py utils/convert_vistas_to_cityscapes.py datasets/vistas.py utils/misc.py eval/evaluate_segmented_images.py utils/segmentor.py train/train_many.py utils/transforms.py layers/feature_loss.py datasets/dataset_configs.py utils/result_visualization/parse_log.py utils/joint_transforms.py datasets/cityscapes.py datasets/correspondences.py eval/segment_and_evaluate_several.py train/train_with_correspondences.py models/model_configs.py models/pspnet.py utils/result_visualization/plot_log.py models/resnet.py make_dataset CityScapes remap_mask colorize_mask make_dataset correspondences_collate refine_correspondence_sample Correspondences CityscapesConfig WilddashConfig CmuConfig DatasetConfig RobotcarConfig VistasConfig make_dataset remap_mask colorize_mask Vistas evaluate_segmented_images evaluate_segmented_images_for_experiments segment_images_in_folder_for_experiments segment_images_in_folder CorrClassLoss FeatureLoss PspnetCityscapesConfig ModelConfig _DilatedFCN _PyramidPoolModule PSPNet _Bottleneck _ResBlock _ConvBatchNormReLU train_with_correspondences get_path_of_startnet train_with_correspondences_experiment generate_name_of_result_folder convert_folder CorrResize Compose CorrRandomCrop RandomHorizontallyFlip RandomPerspective SlidingCropOld CorrRandomCrop CenterCrop RandomSizedCrop FreeScale RandomRotate Compose Scale Resize SlidingCropImageOnly RandomCrop SlidingCrop CorrResize RandomSized rename_key_of_ordered_dict add_bias PolyLR get_global_opts get_root get_upsampling_weight collect_gt_from_slices _fast_hist clean_log_before_continuing absorb_bn replace_root search_absorbe_bn is_absorbing initialize_weights is_bn get_latest_network_name rename_keys_to_match replace_suffix check_mkdir evaluate AverageMeter evaluate_incremental freeze_bn Segmentor DeNormalize FreeScale Scale RandomVerticalFlip RandomGaussianBlur FlipChannels MaskToTensor RGB2BGR Validator parse_log plot_log convert putpalette reshape size array getdata join list replace endswith strip isfile append walk stack squeeze_ list view size int64 append zeros tensor type max range join list asarray items replace zip print copy evaluate_incremental isfile zeros walk open join val_seg_folder seg_file_ending replace test_seg_folder CityscapesConfig WilddashConfig evaluate_segmented_images CmuConfig id_to_trainid RobotcarConfig VistasConfig n_classes SlidingCropImageOnly device input_transform pre_validation_transform list load_state_dict to walk run_and_save replace eval zip rename_keys_to_match load time check_mkdir print PspnetCityscapesConfig Segmentor vis_test_im_folder join val_im_folder CityscapesConfig WilddashConfig print test_im_folder CmuConfig segment_images_in_folder RobotcarConfig VistasConfig im_file_ending train_seg_folder correspondence_im_path rstrip val_im_folder val_seg_folder FeatureLoss train_im_folder zero_grad SGD SlidingCropImageOnly DataLoader Validator device RobotcarConfig CityScapes MaskToTensor input_transform refine_correspondence_sample pre_validation_transform open seg_file_ending list str seg_loss_fct run clean_log_before_continuing step load_state_dict iter append to VistasConfig next sum im_file_ending range update SummaryWriter CityscapesConfig size Compose corr_loss_fct close Correspondences eval get_latest_network_name avg item float net rename_keys_to_match load join int CorrClassLoss enumerate softm check_mkdir backward print add_scalar PspnetCityscapesConfig write Softmax2d AverageMeter CmuConfig freeze_bn train correspondence_path split join get_global_opts get_global_opts get_path_of_startnet generate_name_of_result_folder train_with_correspondences fromarray join ones_like asarray items uint8 astype mkdir save open update join get_root exists rename_key_of_ordered_dict data Parameter affine pow_ register_buffer add_ mul_ expand_as type register_parameter absorb_bn children data Parameter type modules isinstance BatchNorm3d eval modules BatchNorm1d BatchNorm2d makedirs fill_ isinstance modules zero_ BatchNorm2d weight kaiming_normal zeros abs reshape mean sum diag zip mean zip zeros sum diag int match listdir group transpose_ squeeze_ item zip zeros compile compile show subplot items join plot parse_log xlabel print ylabel tight_layout realpath title savefig figure legend zip append dirname | # A Cross-Season Correspondence Dataset for Robust Semantic Segmentation This is an implementation of the work published in A Cross-Season Correspondence Dataset for Robust Semantic Segmentation (https://arxiv.org/abs/1903.06916) ## Resources The datasets used in the paper are available at visuallocalization.net ## Trained Models https://drive.google.com/open?id=14joxT0XFreW1WX3M8oTiCV69hZTiJTMV ## Installation A Dockerfile is provided, either build a docker image using this or refer to the requirements listed in the file. ## Usage - Download Cityscapes and Mapillary Vistas | 2,922 |
maunzzz/fine-grained-segmentation-networks | ['visual localization', 'autonomous driving'] | ['Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization', 'Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization'] | utils/validator.py eval/context.py layers/corr_class_loss.py utils/context.py utils/corr_transforms.py utils/convert_vistas_to_cityscapes.py datasets/vistas.py models/resnet_orig.py utils/misc.py utils/segmentor.py utils/transforms.py layers/feature_loss.py datasets/dataset_configs.py clustering/cluster_tools.py eval/find_non_stationary_cluster.py utils/result_visualization/parse_log.py clustering/utils/context.py utils/joint_transforms.py clustering/setup_cluster_dataset.py clustering/utils/save_feature_positions.py datasets/cityscapes.py datasets/correspondences.py train/train_with_clustering.py clustering/context.py datasets/merged.py models/model_configs.py clustering/utils/write_lists_for_corr.py models/pspnet.py train/train_many_clustering.py utils/result_visualization/plot_log.py clustering/clustering.py train/context.py eval/cluster_images_in_folder.py clustering/utils/write_reference_im_list.py models/resnet.py layers/cluster_correspondence_loss.py Kmeans arrange_clustering preprocess_features run_kmeans assign_cluster_ids_to_correspondence_points extract_features_for_reference_nocorr save_cluster_features_as_segmentations create_interpol_weights extract_features_for_reference save_feature_positions write_lists_for_corr write_reference_im_list make_dataset CityScapes colorize_mask make_dataset correspondences_collate refine_correspondence_sample Correspondences CityscapesConfig CmuConfig DatasetConfig RobotcarConfig VistasConfig Merged make_dataset colorize_mask Vistas cluster_images_in_folder_for_experiments cluster_images_in_folder find_non_stationary_clusters ClusterCorrespondenceLoss CorrClassLoss FeatureLoss PspnetCityscapesConfig ModelConfig _DilatedFCN PSPNetClustering _PyramidPoolModule PSPNet _Bottleneck _ResBlock _ConvBatchNormReLU conv1x1 Bottleneck conv3x3 resnet101 ResNetForPsp BasicBlock train_with_clustering generate_name_of_result_folder reinit_last_layers train_with_clustering_experiment init_last_layers get_path_of_startnet convert_folder CorrResize Compose CorrRandomCrop CorrResizeOneIm RandomHorizontallyFlip RandomPerspective ResizeImOnly SlidingCropOld CenterCrop RandomSizedCrop FreeScale RandomRotate Compose Scale Resize RandomCropDiffSize RandomCrop SlidingCrop SlidingCropImageOnly RandomSized create_palette rename_key_of_ordered_dict add_bias load_resnet101_weights fast_hist PolyLR get_global_opts get_root get_upsampling_weight collect_gt_from_slices clean_log_before_continuing fast_hist_cluster log_and_print absorb_bn replace_root search_absorbe_bn is_absorbing initialize_weights colorize is_bn get_latest_network_name get_network_name_from_iteration remap_mask rename_keys_to_match replace_suffix check_mkdir evaluate add_color_to_image AverageMeter evaluate_incremental freeze_bn im_to_ext_name create_interpol_weights FeatureExtractor Segmentor DeNormalize FreeScale Scale RandomVerticalFlip RandomGaussianBlur FlipChannels MaskToTensor RGB2BGR ClusterValidator Validator CorrValidator parse_log_clustering plot_log_clustering norm vector_to_array reshape transpose astype apply_py dot shape PCAMatrix b train Clustering IndexFlatL2 vector_to_array obj format print reshape GpuIndexFlatConfig search GpuIndexFlatL2 shape StandardGpuResources train argsort extend enumerate len zeros round range device round list create_interpol_weights append to range concatenate size astype choice zip item enumerate time print min float32 len pre_inference_transform_with_corrs SlidingCropImageOnly device round input_transform squeeze_ list create_interpol_weights append to range LongTensor concatenate size hstack astype choice stack swapaxes zip sliding_crop item unique int time print convert File min float32 array preprocess_features view size transpose squeeze float32 int64 numpy assign device append to type range join ones convert save zip join items check_mkdir reference_feature_poitions print File close CmuConfig unique create_dataset RobotcarConfig im_to_ext_name correspondence_path array append seed list min write correspondence_train_list_file ceil CmuConfig choice set close open correspondence_val_list_file RobotcarConfig range len join items reference_image_list print File set CmuConfig add RobotcarConfig correspondence_path array convert putpalette join list replace endswith strip isfile append walk stack squeeze_ list view size int64 append zeros tensor type max range n_classes SlidingCropImageOnly device input_transform pre_validation_transform list load_state_dict to walk run_and_save replace eval zip rename_keys_to_match load time check_mkdir print PspnetCityscapesConfig Segmentor join int val_im_folder CityscapesConfig train_im_folder print search group test_im_folder CmuConfig cluster_images_in_folder RobotcarConfig VistasConfig im_file_ending correspondence_im_path arange search SlidingCropImageOnly save device RobotcarConfig input_transform exists pre_validation_transform str copyfile shape load_state_dict to range format run_and_save group Correspondences eval load int join check_mkdir print PspnetCityscapesConfig Merged write CmuConfig Segmentor histogram argwhere zeros correspondence_path array len ResNetForPsp zeros device normal_ zero_ rstrip FeatureLoss init_network SGD feature_loss_fct RobotcarConfig str load_state_dict iter Compose item get_network_name_from_iteration join assign_cluster_ids_to_correspondence_points File step zero_grad DataLoader extract_features_for_reference append to Correspondences eval float rename_keys_to_match set_index reshape reinit_last_layers train correspondence_path array correspondence_im_path assign input_transform seg_loss_fct clean_log_before_continuing next range update avg swapaxes init_last_layers net load check_mkdir print loss_fct PspnetCityscapesConfig Merged write AverageMeter freeze_bn CorrValidator histogram numpy add_scalar split cluster_imfeatures load_resnet101_weights vstack device open run preprocess_features CrossEntropyLoss SummaryWriter format replace close get_latest_network_name Kmeans int items deepcopy backward CmuConfig create_dataset cpu zeros join get_global_opts get_global_opts train_with_clustering get_path_of_startnet generate_name_of_result_folder fromarray join ones_like asarray items uint8 astype mkdir save open update join get_root exists rename_key_of_ordered_dict data Parameter affine pow_ register_buffer add_ mul_ expand_as type register_parameter absorb_bn children data Parameter type modules isinstance BatchNorm3d eval modules BatchNorm1d BatchNorm2d makedirs fill_ isinstance modules zero_ BatchNorm2d weight kaiming_normal zeros abs reshape reshape zeros_like mean zip append sum diag mean zip zeros sum diag int match listdir group int match listdir group transpose_ squeeze_ item zip zeros compile split data keys load_state_dict state_dict reshape size array getdata print write putpalette add_color_to_image convert create_palette list range map list map shape zeros array range compile zeros_like max show subplot list axvline ylabel title scatter savefig dirname legend range plot group tight_layout close realpath parse_log_clustering enumerate int join xlabel print match figure zeros len | # Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization This is an implementation of the work published in Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization (https://arxiv.org/abs/1908.06387) ## Resources The datasets used in the paper is published at visuallocalization.net ## Trained Models https://drive.google.com/drive/folders/1ks_HDb3ipNlJsNiTbLNSvqSIZ8Is8xCk ## Installation A Dockerfile is provided, either build a docker image using this or refer to the requirements listed in the file. In addition, a requirements.txt is provided. ## Usage | 2,923 |
mauriceqch/pcc_geo_cnn_v2 | ['mixed reality', 'autonomous driving'] | ['Improved Deep Point Cloud Geometry Compression'] | src/test_model_transforms.py src/utils/o3d.py src/model_opt.py src/utils/matplotlib_utils.py src/ds_select_largest.py src/ut_tensorboard_plots.py src/utils/colorbar.py src/tr_train_all.py src/utils/pc_metric.py src/utils/experiment.py src/tr_train.py src/ev_compare.py src/ev_run_experiment.py src/utils/pc_to_camera_params.py src/utils/test_bd.py src/ut_run_render.py src/utils/patch_gaussian_conditional.py src/model_syntax.py src/test_model_opt.py src/ut_build_paper.py src/decompress_octree.py src/model_types.py src/utils/focal_loss.py src/utils/pc_to_img.py src/ev_experiment.py src/model_configs.py src/compress_octree.py src/mp_report.py src/ds_mesh_to_pc.py src/utils/bd.py src/test_model_syntax.py src/utils/mpeg_parsing.py src/ev_run_compare.py src/ds_pc_octree_blocks.py src/utils/octree_coding.py src/utils/pc_io.py src/map_color.py src/utils/parallel_process.py src/model_transforms.py src/mp_run.py write_pcs compress read_pcs decompress process arr_to_pc process run read_json build_curves flatten run_pcerror print_progress run_experiment run_compare run_experiment run_mapcolor ModelConfig ModelConfigType compute_optimal_thresholds build_points_threshold load_compressed_file save_compressed_file to_bytes read_from_buffer scalar_to_bytes AnalysisBlock HyperAnalysisTransform TransformType SynthesisTransformV2 AnalysisTransformProgressiveV2 ResidualLayer SynthesisTransformProgressiveV2 AnalysisTransformV1 SynthesisTransformV1 SynthesisBlock get_channel_axis HyperSynthesisTransform SequentialLayer AnalysisTransformV2 get_normals_if input_fn CompressionModelV1 select_best_per_opt_metric CompressionModelV2 CompressionModel pc_to_tf quantize_tensor v2_summaries ModelType process_x add_channels binary_classification_summaries sparse_to_dense v1_summaries run find run_gen_report run_mpeg_experiment get_n_points assert_pa_list_equal test_build_points_threshold test_compute_optimal_thresholds test_save_load TestModelTransforms tf_to_np train lmbda_to_str get_log_path get_model_dir write_table_main mpeg_path_color model_path_color arr_to_pil get_model_dir preprocess bdrate bdsnr get_colorbar assert_exists index_by_id timing build_logger focal_loss default_rc_params markers_cycle set_lims render_legend load_rc_params linestyles_cycle parse_pcerror parse_decoded_log parse_bin_log pc_to_img np_to_o3d pc_to_camera_params trim_img_bbox split_octree partition_octree departition_octree compute_new_bbox partition_octree_rec parallel_process safe_close is_file Popen decompress patch_gaussian_conditional add_prefix_to_dict build pa_to_df load_points get_shape_data pc_to_df load_pc write_df get_files write_pc df_to_pc d1_res sum_d2 assign_attr psnr validate_opt_metrics d1 d2_res compute_metrics sum_d1 compute_d1_res_ba d2 test_bdsnr exp_x test_bdrate join pa_to_df makedirs write_df enumerate load_points get_shape_data opt_metrics zip concatenate resolution validate_opt_metrics copy octree_level build input_files Saver info global_variables_initializer ConfigProto data_format build input_files Saver info global_variables_initializer ConfigProto len mesh dest source round drop_duplicates max values vg_size get_sample debug astype mean splitext join from_file min to_file split makedirs DataFrame astype range PyntCloud columns partition_octree arr_to_pc level dtypes enumerate to_string subplots grid query bdf DataFrame values iterrows savefig legend append plot debug set_xlim isfinite copy set tight_layout zip info enumerate join print to_csv locator_params markers_cycle set_lims linestyles_cycle set_ylim build_curves read_json search sort_values set info append DataFrame keys range compile len join info open join parse_pcerror all st_size info parallel_process assert_exists len validate_opt_metrics flatten print_progress points zip append abs exists makedirs open join makedirs open v2 v1 ModelConfig append astype enumerate len argmin astype validate_opt_metrics build_points_threshold cKDTree info append len iinfo array uint8 uint16 to_bytes scalar_to_bytes len int uint8 read read_from_buffer append range pad ones_like SparseTensor set_shape float32 cast to_dense uint8 round cast clip_by_value histogram scalar histogram scalar count_nonzero scalar zeros info cKDTree dict pformat zip append argmax glob join join parse_bin_log parse_pcerror print points exists find join assert_exists makedirs assert_array_equal range len list assert_pa_list_equal build_points_threshold array enumerate sqrt compute_optimal_thresholds array BytesIO load_compressed_file save_compressed_file zip assert_array_equal input_fn checkpoint_dir get_output_types batch_size train_glob get_next string Saver get_files touch from_string_handle data_format resolution placeholder build get_output_shapes load_points get_shape_data FileWriter alpha info gamma join lmbda make_one_shot_iterator global_variables_initializer array max join str set_index reshape len query mean append round array range values enumerate asarray unique list integrate PchipInterpolator min map polyfit polyval preprocess polyint max log list exp integrate PchipInterpolator min map polyfit polyval preprocess polyint max log isinstance axes colorbar imshow set_visible figure Normalize get_cmap array stdout setFormatter getLogger addHandler StreamHandler Formatter setLevel INFO FileHandler ones_like zeros_like equal where clip items plot draw add_subplot tight_layout markers_cycle figure legend zip append linestyles_cycle range len float f isinstance zip int search group float MULTILINE int group float group Vector3dVector PointCloud create_window Visualizer add_geometry convert_to_pinhole_camera_parameters get_view_control destroy_window run StringIO getvalue size new difference getbbox mode copy append pad enumerate split_octree asarray arange zeros_like log2 max pad ceil uint32 append asarray size astype unique enumerate int join argsort assert_array_equal zeros array partition_octree_rec len pop asarray copy pad append zeros range compute_new_bbox len close is_file constant arange add_weight _scale scale_table TensorShape lower_bound scale_bound astype identity add_prefix_to_dict shape expand_dims abs max assert_is_compatible_with _standardized_cumulative values DataFrame astype range points debug from_file df_to_pc points write_df pc_to_df to_file PyntCloud array astype len zeros expand_dims enumerate cKDTree query psnr assign_attr sum_d2 min cKDTree query sum_d1 max exp_x | # Improved Deep Point Cloud Geometry Compression <p align="center"> <img src="image.png?raw=true" alt="Comparison samples"/> </p> * **Authors**: [Maurice Quach](https://scholar.google.com/citations?user=atvnc2MAAAAJ), [Giuseppe Valenzise](https://scholar.google.com/citations?user=7ftDv4gAAAAJ) and [Frederic Dufaux](https://scholar.google.com/citations?user=ziqjbTIAAAAJ) * **Affiliation**: Université Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des signaux et systèmes, 91190 Gif-sur-Yvette, France * **Funding**: ANR ReVeRy national fund (REVERY ANR-17-CE23-0020) | 2,924 |
mauriciomani/NST_instagram | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | google_api.py neural_style_transfer.py download_images main images_extraction append_csv clip_0_1 reading_json image_read random_selection final_caption StyleContentModel tensor_to_image gram_matrix vgg_layers main style_content_loss get print choice execute append next_chunk int get_media print FileIO progress MediaIoBaseDownload download_images images_extraction run_local_server from_client_secrets_file build Request refresh exists float32 convert_image_dtype read_file cast int32 resize max decode_image VGG19 Model shape cast float32 einsum add_n array randint uniform str float str format print clear_session image_read random_selection save resize str login append_csv train_step check_numerics Adam upload_photo range extractor format StyleContentModel final_caption today choice Bot time constant remove ANTIALIAS print Variable len | # NST INSTAGRAM <p align="center"> <img src="images/examples.png" /> </p> This app integrates Google Drive API, Tensorflow and Instagram Bot to automatically extract images from Google Drive folder, specifically paintings and your own pictures so we can train a **Neural Style Transfer** algorithm and upload pictures on Instagram, scheduled by crontab. Just as a parenthesis, thoug is cool to have your NST photos uploaded to Instagram, the **main goal of this app is to store data on best hyperparameters** (using logs.csv file). <p align="center"> <img src="images/pipeline.png" /> </p> # Connecting with Google Drive | 2,925 |
maurock/neural_transfer_style | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | main.py deprocess_image Evaluator style_loss_per_layer gram_matrix eval_loss_and_grads content_loss total_style_loss total_variation_loss save preprocess_image total_loss run expand_dims preprocess_input img_to_array load_img reshape astype dot transpose batch_flatten permute_dimensions gram_matrix variable style_loss_per_layer square variable reshape astype f_outputs fromarray save_img function gradients variable flatten iterations save VGG19 total_loss str placeholder fmin_l_bfgs_b range concatenate size Evaluator copy preprocess_image int print deprocess_image dict loss | # Neural Style Transfer using Deep Learning Transfer painting styles to chosen images. This project reproduces the paper "A Neural Algorithm of Artistic Style" by Gatys et al. (https://arxiv.org/abs/1508.06576). ## Run To run the neural style transfer script: - place the base image (the image to modify) in `reference_images\base_image` - place the style image (the image from which the style is transferred) in `reference_images\style_image` - run the script `main.py` in the main directory After each iteration, an image is generated. Run example: | 2,926 |
maxblumental/variational-drouput | ['sparse learning'] | ['Variational Dropout Sparsifies Deep Neural Networks'] | blumental/data_loading.py blumental/variational_dropout.py load_mnist Sgvb SparseLinear MNIST Compose train_labels to test_labels | # Variational Dropout and Neural Networks Sparsification In this project we demonstrate how variational dropout can sparsify weight matrices of a fully connected neural network. The idea is taken from [here]. Visualization of sparsity: <p align="center"> <img height="318" src="https://github.com/maxblumental/network-sparsification/blob/master/movie.gif"/> </p> Team: - Ilia Luchnikov - Marsel Faizullin - Maxim Blumental | 2,927 |
maxfrei750/synthPIC4Python | ['semantic segmentation'] | ['FibeR-CNN: Expanding Mask R-CNN to Improve Image-Based Fiber Analysis'] | recipes/sopat_catalyst.py recipes/carbon_nano_tubes_sem/carbon_nano_tubes_sem.py recipe_utilities.py system_utilities.py blender/scene.py spline_utilities.py setup_synthpic.py blender/utilities.py render.py blender/particles.py _noise_to_image generate_uniform_noise_image set_random_seed generate_gaussian_noise_image Timer get_random_string main render print_help get_blender_version_string get_blender_folder_path activate_addon download_blender get_blender_python_folder_path get_os_architecture get_os_name delete_old_blender activate_addons is_os_64bit get_external_module_folder_path install_dependencies get_blender_executable_path get_blender_version_number_string get_blender_python_executable_path _prepare_spline_interpolation calculate_spline_length _remove_duplicate_vertices execute execute_and_print is_hair ensure_double_iterability place delete get_hair_diameter set_hair_diameter set_size show make_rigid randomize_shape select_only get_size set_smooth_shading load_primitive is_iterable place_randomly duplicate get_hair_spline_vertices hide set_random_hair_length_factor set_hair_length_factor create_raw_dummy_mesh ensure_iterability generate_lognormal_fraction get_hair_spline_keypoints relax_collisions rotate_randomly _gather_spline_data get_space_boundaries render_object_masks create_diffuse_color_material setup_workbench_renderer replace_material apply_default_settings set_resolution _horizontally_mirror_keypoints _separate_keypoint_coordinates save_spline_data render_to_variable render_occlusion_masks set_background_color enable_all_rendering_devices _prepare_spline_data_for_saving TemporaryState save_annotation_file _filter_keypoints_outside_of_image render_to_file _write_spline_data_to_file _offset_keypoints purge_unused_data create_clutter_fraction generate_samples render_image setup_scene post_process_particle_layer place_clutter_on_fibers place_fibers_randomly compose_layers save_image create_particle_fraction save_output_data load_primitive create_image_layers create_geometry create_fiber_fraction ascii_lowercase digits fromarray convert BICUBIC resize enhance seed _noise_to_image ceil randn seed _noise_to_image rand ceil seed print exit format print download_blender exit abspath input get_blender_executable_path execute_and_print install_dependencies render getopt exit print_help get_os_architecture get_blender_version_number_string get_os_name realpath dirname get_blender_folder_path get_os_name get_blender_folder_path get_blender_version_number_string get_os_name get_blender_python_folder_path join print get_blender_python_folder_path realpath get_os_name rmtree dirname execute_and_print get_blender_python_executable_path get_blender_version_string join remove urlretrieve unpack_archive print get_os_name get_external_module_folder_path get_blender_version_number_string get_blender_version_string get_external_module_folder_path get_blender_folder_path rmtree print exists get_blender_executable_path execute_and_print print activate_addon splprep len T _remove_duplicate_vertices _prepare_spline_interpolation readline wait close iter Popen print rstrip execute iter select_all select_set ensure_iterability seed randn vertex_normals vertices icosphere len ensure_iterability hide append abspath particle_systems copy animation_data_clear link enumerate purge_unused_data ensure_iterability is_hair location tuple select_only convert randint ensure_iterability data name select_only modifier_convert delete active vertices append ensure_iterability get_hair_spline_vertices ensure_iterability root_radius append tip_radius radius_scale ensure_iterability random ensure_iterability ensure_iterability tuple ensure_iterability append scale ensure_iterability polygons ensure_iterability unlink link world_add ensure_iterability make_rigid bake_all frame_set upper free_bake_all world_add ensure_iterability tuple randint rotate_randomly ensure_iterability tuple ensure_double_iterability zip ensure_iterability tuple rand pi ensure_iterability duplicate list format randomize_shape hide set_size lognormal append range log max cameras preferences devices scene upper enable_all_rendering_devices str render filepath join gettempdir render_to_file get_random_string open remove makedirs dirname ensure_iterability hide render_to_file Path objects setup_workbench_renderer enumerate ensure_iterability clear append replace_material create_diffuse_color_material render_to_file Path objects setup_workbench_renderer enumerate ensure_iterability _gather_spline_data _prepare_spline_data_for_saving get_space_boundaries zip _write_spline_data_to_file empty enumerate join to_csv _horizontally_mirror_keypoints _separate_keypoint_coordinates _filter_keypoints_outside_of_image DataFrame _offset_keypoints get_hair_spline_keypoints get_hair_diameter query remove materials meshes images textures int lognormal create_particle_fraction ceil count randint show duplicate list format randomize_shape hide set_hair_diameter uniform set_hair_length_factor load_primitive append range rotate_randomly range set_random_seed save_spline_data save_image makedirs create_image_layers compose_layers place zip get_hair_spline_vertices choice choices len create_clutter_fraction place_fibers_randomly place_clutter_on_fibers uniform create_fiber_fraction set_resolution apply_default_settings join convert save blend alpha_composite post_process_particle_layer generate_gaussian_noise_image render_to_variable GaussianBlur filter enhance get_space_boundaries place_randomly join | [](https://doi.org/10.1016/j.powtec.2020.08.034) [](https://arxiv.org/abs/2006.04552) [](https://github.com/maxfrei750/FiberAnnotator/blob/master/LICENSE) [](https://github.com/maxfrei750/synthPIC4Python) [](https://gitlab.com/maxfrei750/synthPIC4Python)  # synthPIC4Python The *synthetic Particle Image Creator (synthPIC) for Python* is a toolbox to create synthetic training and benchmark data for image based particle analysis methods. As of now, it is in a very early stage of development. Therefore, there is currently very little documentation and every current feature might be subjected to heavy changes in the future. In principle, synthPIC4Python is simply a set of scripts, .blend-files and concepts to control [Blender](https://www.blender.org/), to have it render large sets of randomized images, depicting large quantities of objects. There is no limitation concerning the shape of the objects, as they are controlled only by the so-called primitives in the .blend-files that the user supplies. However, it was created with the use for particle images in mind. Apart from the images themselves, synthPIC4Python also offers easy solutions to render instance masks, so that the resulting data can be used for the training of various segmentation and detection algorithms (e.g. Faster R-CNN, Mask R-CNN, FibeR-CNN, U-Net etc.). ## Table of Contents | 2,928 |
maxiwoj/car_racer_ml_agents | ['unity'] | ['Unity: A General Platform for Intelligent Agents'] | ml-agents/mlagents/trainers/components/reward_signals/curiosity/model.py ml-agents-envs/mlagents_envs/communicator_objects/command_pb2.py ml-agents/mlagents/trainers/run_experiment.py ml-agents-envs/mlagents_envs/mock_communicator.py ml-agents/mlagents/trainers/policy/__init__.py ml-agents-envs/mlagents_envs/communicator_objects/unity_to_external_pb2.py ml-agents-envs/mlagents_envs/communicator.py gym-unity/gym_unity/envs/__init__.py ml-agents-envs/mlagents_envs/communicator_objects/brain_parameters_pb2.py ml-agents/mlagents/trainers/learn.py ml-agents/mlagents/trainers/tests/test_sampler_class.py ml-agents/mlagents/trainers/meta_curriculum.py ml-agents/mlagents/trainers/tests/test_barracuda_converter.py ml-agents-envs/mlagents_envs/side_channel/raw_bytes_channel.py ml-agents/mlagents/trainers/trainer/trainer.py gym-unity/gym_unity/__init__.py ml-agents-envs/mlagents_envs/side_channel/__init__.py utils/validate_meta_files.py ml-agents/mlagents/trainers/trainer_controller.py ml-agents/mlagents/trainers/components/bc/model.py ml-agents/mlagents/trainers/tests/test_curriculum.py ml-agents/mlagents/trainers/action_info.py ml-agents/mlagents/trainers/tests/test_ppo.py ml-agents/mlagents/tf_utils/__init__.py ml-agents/mlagents/trainers/components/reward_signals/__init__.py ml-agents-envs/setup.py ml-agents-envs/mlagents_envs/side_channel/engine_configuration_channel.py ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_output_pb2.py ml-agents/mlagents/trainers/tests/mock_brain.py ml-agents/mlagents/trainers/tests/test_bcmodule.py ml-agents/mlagents/trainers/tests/test_trainer_controller.py ml-agents-envs/mlagents_envs/side_channel/incoming_message.py ml-agents/mlagents/trainers/components/reward_signals/reward_signal_factory.py ml-agents-envs/mlagents_envs/rpc_utils.py ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_initialization_output_pb2.py ml-agents/setup.py ml-agents/mlagents/trainers/barracuda.py ml-agents/mlagents/trainers/optimizer/tf_optimizer.py ml-agents/mlagents/trainers/env_manager.py ml-agents/mlagents/trainers/ppo/trainer.py ml-agents/mlagents/trainers/policy/policy.py ml-agents-envs/mlagents_envs/communicator_objects/agent_action_pb2.py ml-agents/mlagents/model_serialization.py ml-agents-envs/mlagents_envs/tests/test_rpc_communicator.py ml-agents-envs/mlagents_envs/tests/test_envs.py ml-agents/mlagents/trainers/brain.py utils/validate_inits.py ml-agents-envs/mlagents_envs/side_channel/float_properties_channel.py ml-agents/mlagents/trainers/tests/test_meta_curriculum.py ml-agents/mlagents/trainers/components/reward_signals/curiosity/signal.py ml-agents/mlagents/trainers/simple_env_manager.py ml-agents-envs/mlagents_envs/side_channel/outgoing_message.py ml-agents-envs/mlagents_envs/exception.py ml-agents/mlagents/trainers/curriculum.py ml-agents/mlagents/trainers/tests/test_policy.py ml-agents/mlagents/trainers/trainer/__init__.py ml-agents-envs/mlagents_envs/communicator_objects/unity_message_pb2.py ml-agents/mlagents/trainers/tests/test_learn.py ml-agents/mlagents/trainers/policy/nn_policy.py ml-agents-envs/mlagents_envs/communicator_objects/agent_info_pb2.py ml-agents/mlagents/trainers/tests/test_demo_loader.py ml-agents-envs/mlagents_envs/communicator_objects/observation_pb2.py utils/validate_versions.py ml-agents-envs/mlagents_envs/tests/test_rpc_utils.py ml-agents/mlagents/trainers/models.py ml-agents-envs/mlagents_envs/tests/test_timers.py ml-agents/mlagents/trainers/__init__.py ml-agents-envs/mlagents_envs/communicator_objects/custom_reset_parameters_pb2.py ml-agents-envs/mlagents_envs/communicator_objects/agent_info_action_pair_pb2.py ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_input_pb2.py ml-agents/mlagents/trainers/tests/test_nn_policy.py ml-agents-envs/mlagents_envs/timers.py ml-agents/mlagents/trainers/tests/test_simple_rl.py ml-agents/mlagents/trainers/exception.py ml-agents/mlagents/trainers/tests/test_distributions.py gym-unity/gym_unity/tests/test_gym.py utils/make_readme_table.py ml-agents/mlagents/tf_utils/tf.py ml-agents/mlagents/trainers/tests/test_ghost.py ml-agents/mlagents/trainers/buffer.py ml-agents-envs/mlagents_envs/side_channel/side_channel.py ml-agents/mlagents/trainers/tests/test_subprocess_env_manager.py ml-agents/mlagents/trainers/subprocess_env_manager.py ml-agents/mlagents/trainers/tensorflow_to_barracuda.py ml-agents/mlagents/trainers/agent_processor.py ml-agents-envs/mlagents_envs/communicator_objects/engine_configuration_pb2.py ml-agents/mlagents/trainers/tests/test_rl_trainer.py ml-agents-envs/mlagents_envs/rpc_communicator.py ml-agents-envs/mlagents_envs/communicator_objects/demonstration_meta_pb2.py ml-agents-envs/mlagents_envs/__init__.py gym-unity/setup.py ml-agents/mlagents/trainers/behavior_id_utils.py ml-agents/mlagents/trainers/sac/network.py ml-agents/mlagents/trainers/distributions.py ml-agents/mlagents/trainers/policy/tf_policy.py ml-agents/mlagents/trainers/optimizer/__init__.py ml-agents/mlagents/trainers/tests/simple_test_envs.py ml-agents/mlagents/trainers/tests/__init__.py ml-agents-envs/mlagents_envs/communicator_objects/unity_output_pb2.py ml-agents-envs/mlagents_envs/communicator_objects/space_type_pb2.py ml-agents/mlagents/trainers/trainer_util.py ml-agents/mlagents/trainers/tests/test_trainer_util.py ml-agents-envs/mlagents_envs/logging_util.py ml-agents/mlagents/trainers/components/reward_signals/extrinsic/signal.py ml-agents/mlagents/trainers/sac/trainer.py ml-agents/mlagents/trainers/sampler_class.py ml-agents/tests/yamato/training_int_tests.py ml-agents/mlagents/trainers/tests/test_sac.py ml-agents/mlagents/trainers/trajectory.py ml-agents/mlagents/trainers/ppo/optimizer.py ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_initialization_input_pb2.py ml-agents-envs/mlagents_envs/base_env.py ml-agents-envs/mlagents_envs/communicator_objects/header_pb2.py ml-agents/mlagents/trainers/tests/test_stats.py ml-agents/mlagents/trainers/components/reward_signals/gail/model.py ml-agents/mlagents/trainers/tests/test_reward_signals.py ml-agents/mlagents/trainers/components/reward_signals/gail/signal.py ml-agents-envs/mlagents_envs/tests/test_side_channel.py ml-agents/mlagents/trainers/sac/optimizer.py ml-agents/tests/yamato/standalone_build_tests.py ml-agents-envs/mlagents_envs/environment.py ml-agents/mlagents/trainers/demo_loader.py ml-agents/mlagents/trainers/ghost/trainer.py ml-agents/tests/yamato/editmode_tests.py ml-agents/mlagents/trainers/components/bc/module.py ml-agents-envs/mlagents_envs/communicator_objects/unity_input_pb2.py ml-agents/mlagents/trainers/tests/test_buffer.py ml-agents/mlagents/trainers/trainer/rl_trainer.py ml-agents/mlagents/trainers/tests/test_agent_processor.py ml-agents-envs/mlagents_envs/communicator_objects/unity_to_external_pb2_grpc.py ml-agents/mlagents/trainers/brain_conversion_utils.py ml-agents/tests/yamato/yamato_utils.py ml-agents/mlagents/trainers/stats.py ml-agents/mlagents/trainers/tests/test_trajectory.py ml-agents/mlagents/trainers/optimizer/optimizer.py VerifyVersionCommand AgentIdIndexMapper AgentIdIndexMapperSlow ActionFlattener UnityEnv UnityGymException test_sanitize_action_shuffled_id test_sanitize_action_one_agent_done test_sanitize_action_new_agent_done test_sanitize_action_single_agent_multiple_done test_gym_wrapper test_multi_agent test_agent_id_index_mapper create_mock_group_spec test_branched_flatten setup_mock_unityenvironment test_gym_wrapper_visual create_mock_vector_step_result VerifyVersionCommand _get_frozen_graph_node_names export_policy_model _make_onnx_node_for_constant _make_frozen_graph _get_output_node_names _get_input_node_names convert_frozen_to_onnx _enforce_onnx_conversion SerializationSettings _process_graph set_warnings_enabled generate_session_config ActionInfo AgentManager AgentProcessor AgentManagerQueue BarracudaWriter fuse print_known_operations compress Build sort lstm write fuse_batchnorm_weights trim mean gru Model summary Struct parse_args to_json rnn BehaviorIdentifiers BrainParameters CameraResolution group_spec_to_brain_parameters get_global_agent_id BufferException AgentBuffer Curriculum make_demo_buffer load_demonstration demo_to_buffer get_demo_files OutputDistribution DiscreteOutputDistribution MultiCategoricalDistribution GaussianDistribution EnvManager EnvironmentStep SamplerException TrainerConfigError CurriculumError TrainerError MetaCurriculumError CurriculumLoadingError UnityTrainerException CurriculumConfigError RunOptions write_timing_tree create_sampler_manager create_environment_factory parse_command_line run_training prepare_for_docker_run try_create_meta_curriculum run_cli main _create_parser get_version_string MetaCurriculum EncoderType NormalizerTensors ModelUtils LearningRateSchedule main parse_command_line MultiRangeUniformSampler UniformSampler SamplerFactory SamplerManager GaussianSampler Sampler SimpleEnvManager StatsWriter StatsSummary StatsReporter GaugeWriter TensorboardWriter CSVWriter worker EnvironmentResponse UnityEnvWorker StepResponse SubprocessEnvManager EnvironmentCommand get_layer_shape pool_to_HW flatten sqr_diff process_layer process_model get_layer_rank slow_but_stable_topological_sort get_attr basic_lstm ModelBuilderContext order_by get_epsilon get_tensor_dtype replace_strings_in_list debug embody by_op get_tensor_dims strided_slice remove_duplicates_from_list axis_to_barracuda by_name locate_actual_output_node convert strides_to_HW get_tensor_data very_slow_but_stable_topological_sort gru TrainerController TrainerFactory initialize_trainer load_config _load_config AgentExperience Trajectory SplitObservations BCModel BCModule create_reward_signal RewardSignal CuriosityModel CuriosityRewardSignal ExtrinsicRewardSignal GAILModel GAILRewardSignal compute_elo_rating_changes GhostTrainer Optimizer TFOptimizer NNPolicy Policy TFPolicy UnityPolicyException PPOOptimizer PPOTrainer get_gae discount_rewards SACPolicyNetwork SACTargetNetwork SACNetwork SACOptimizer SACTrainer create_mock_pushblock_brain create_mock_batchedstep simulate_rollout make_brain_parameters setup_mock_brain make_fake_trajectory create_mock_banana_brain create_batchedstep_from_brainparams create_mock_brainparams create_mock_3dball_brain clamp Memory1DEnvironment Simple1DEnvironment test_end_episode test_agent_deletion test_agent_manager_queue test_agentprocessor test_agent_manager create_mock_brain create_mock_policy test_barracuda_converter test_policy_conversion dummy_config test_bcmodule_rnn_update test_bcmodule_update test_bcmodule_constant_lr_update ppo_dummy_config test_bcmodule_dc_visual_update create_bc_module test_bcmodule_defaults test_bcmodule_rnn_dc_update test_buffer_sample construct_fake_buffer test_num_experiences assert_array fakerandint test_buffer test_buffer_truncate test_curriculum_load_invalid_json default_reset_parameters test_load_bad_curriculum_file_raises_error test_curriculum_load_missing_file test_get_parameters test_init_curriculum_happy_path test_increment_lesson test_curriculum_load_good test_edge_cases test_load_demo test_load_demo_dir test_multicategorical_distribution test_tanh_distribution dummy_config test_gaussian_distribution test_load_and_set dummy_config test_publish_queue test_process_trajectory basic_options test_docker_target_path test_run_training test_bad_env_path test_commandline_args test_env_args test_increment_lessons_with_reward_buff_sizes test_increment_lessons measure_vals reward_buff_sizes test_set_all_curriculums_to_lesson_num test_get_config test_set_lesson_nums test_simple_metacurriculum test_curriculum_config test_min_visual_size create_policy_mock test_normalization dummy_config test_policy_evaluate 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 FakePolicy test_trainer_increment_step test_trainer_update_policy test_ppo_optimizer_update test_ppo_optimizer_update_curiosity test_process_trajectory test_rl_functions test_add_get_policy test_bad_config _create_fake_trajectory _create_ppo_optimizer_ops_mock dummy_config test_ppo_optimizer_update_gail test_ppo_get_value_estimates test_gail_dc_visual sac_dummy_config reward_signal_update reward_signal_eval test_extrinsic test_curiosity_cc test_gail_rnn test_gail_cc ppo_dummy_config test_curiosity_dc curiosity_dummy_config test_curiosity_visual test_curiosity_rnn create_optimizer_mock gail_dummy_config FakeTrainer create_rl_trainer dummy_config test_rl_trainer create_mock_brain test_advance test_clear_update_buffer test_sac_update_reward_signals test_add_get_policy test_bad_config create_sac_optimizer_mock test_sac_optimizer_update dummy_config test_process_trajectory test_sac_save_load_buffer test_empty_samplers sampler_config_1 check_value_in_intervals incorrect_uniform_sampler test_incorrect_sampler test_sampler_config_1 sampler_config_2 incorrect_sampler_config test_incorrect_uniform_sampler test_sampler_config_2 test_visual_sac generate_config test_simple_sac default_reward_processor test_simple_ghost test_visual_advanced_ppo test_simple_ppo test_simple_ghost_fails test_visual_ppo test_recurrent_ppo DebugWriter test_visual_advanced_sac _check_environment_trains test_tensorboard_writer test_stat_reporter_add_summary_write test_gauge_stat_writer_sanitize test_stat_reporter_text test_csv_writer MockEnvWorker mock_env_factory SubprocessEnvManagerTest create_worker_mock test_subprocess_env_endtoend simple_env_factory test_initialization_seed test_start_learning_trains_until_max_steps_then_saves basic_trainer_controller trainer_controller_with_take_step_mocks test_advance_adds_experiences_to_trainer_and_trains trainer_controller_with_start_learning_mocks test_start_learning_trains_forever_if_no_train_model test_initialize_ppo_trainer test_handles_no_default_section test_load_config_invalid_yaml test_initialize_invalid_trainer_raises_exception dummy_bad_config dummy_config test_load_config_missing_file test_load_config_valid_yaml test_initialize_trainer_parameters_override_defaults test_raise_if_no_config_for_brain dummy_config_with_override test_trajectory_to_agentbuffer test_split_obs np_zeros_no_float64 np_array_no_float64 _check_no_float64 np_ones_no_float64 RLTrainer Trainer main clean_previous_results TestResults parse_results main main override_config_file init_venv get_unity_executable_path get_base_path run_standalone_build VerifyVersionCommand StepResult ActionType AgentGroupSpec BatchedStepResult BaseEnv Communicator UnityEnvironment UnityObservationException UnityWorkerInUseException UnityException UnityCommunicationException UnityTimeOutException UnitySideChannelException UnityEnvironmentException UnityActionException get_logger set_log_level MockCommunicator RpcCommunicator UnityToExternalServicerImplementation agent_group_spec_from_proto _generate_split_indices process_pixels _raise_on_nan_and_inf observation_to_np_array batched_step_result_from_proto _process_vector_observation _process_visual_observation TimerNode hierarchical_timer get_timer_root get_timer_tree reset_timers set_gauge timed GaugeNode TimerStack UnityToExternalProtoServicer add_UnityToExternalProtoServicer_to_server UnityToExternalProtoStub EngineConfigurationChannel EngineConfig FloatPropertiesChannel IncomingMessage OutgoingMessage RawBytesChannel SideChannel test_initialization test_reset test_returncode_to_signal_name 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 test_batched_step_result_from_proto generate_compressed_proto_obs test_agent_group_spec_from_proto test_batched_step_result_from_proto_raises_on_nan test_vector_observation test_action_masking_continuous test_process_visual_observation test_action_masking_discrete_1 test_process_pixels test_process_visual_observation_bad_shape test_action_masking_discrete test_action_masking_discrete_2 generate_compressed_data test_process_pixels_gray generate_list_agent_proto generate_uncompressed_proto_obs test_batched_step_result_from_proto_raises_on_infinite test_raw_bytes test_int_channel test_message_float_list IntChannel test_message_bool test_message_string test_float_properties test_message_int32 test_message_float32 test_timers decorated_func table_line validate_packages main NonTrivialPEP420PackageFinder main set_academy_version_string extract_version_string set_package_version check_versions set_version sample UnityEnv create_mock_group_spec create_mock_vector_step_result setup_mock_unityenvironment step UnityEnv create_mock_group_spec create_mock_vector_step_result setup_mock_unityenvironment step setup_mock_unityenvironment UnityEnv create_mock_group_spec create_mock_vector_step_result sample UnityEnv create_mock_group_spec create_mock_vector_step_result setup_mock_unityenvironment step _sanitize_info zip agent_id UnityEnv reward create_mock_group_spec create_mock_vector_step_result setup_mock_unityenvironment array range _sanitize_info zip agent_id UnityEnv create_mock_group_spec create_mock_vector_step_result setup_mock_unityenvironment array range _sanitize_info zip agent_id UnityEnv create_mock_group_spec create_mock_vector_step_result setup_mock_unityenvironment array range _sanitize_info zip agent_id UnityEnv create_mock_group_spec create_mock_vector_step_result setup_mock_unityenvironment array range tuple CONTINUOUS range DISCRETE array range get_id_permutation mark_agent_done set_initial_agents mapper_cls register_new_agent_id enumerate convert_to_barracuda convert convert_to_onnx _make_frozen_graph _enforce_onnx_conversion convert_frozen_to_onnx info model_path items tf_optimize make_model node _make_onnx_node_for_constant extend _get_output_node_names _get_input_node_names info append brain_name optimize_graph TensorProto _get_frozen_graph_node_names add _get_frozen_graph_node_names name add node set brain_name info set_verbosity ConfigProto 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 array_equal pool reduce Build tanh mad tanh mul Build concat add sigmoid sub mad _ tanh mul Build concat add sigmoid mad print sorted keys is_action_discrete sum get_agent_step_result resequence_and_append done obs from_observations reward batched_step_result_from_proto vector_actions AgentBuffer append reset_agent vector_observations array visual_observations enumerate make_demo_buffer load_demonstration group_spec_to_brain_parameters isdir isfile get_demo_files add_argument_group add_argument ArgumentParser parse_args start_learning pop SamplerManager MetaCurriculum set_all_curricula_to_lesson_num chmod format basename isdir glob copyfile copytree validate_environment_path prepare_for_docker_run seed _asdict print debug run_training dumps randint set_log_level set_warnings_enabled cpu DEBUG INFO get_version_string parse_command_line run_cli add_argument ArgumentParser RunOptions experiment_config_path load_config FloatPropertiesChannel get_property_dict_copy get_timer_root reset_timers put _send_response StepResponse _generate_all_results set_actions set_property action set_configuration EngineConfigurationChannel external_brains payload items EnvironmentResponse reset step endswith len print HasField hasattr get_attr isinstance 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 Build mul sub insert Build tolist append range len locate_actual_output_node name find_tensor_by_name split locate_actual_output_node name lstm find_tensor_by_name find_forget_bias split get_layer_shape id Struct tensor get_layer_rank layer_ranks hasattr name patch_data rank input_shapes out_shapes input get_attr append replace_strings_in_list tensors embody astype op inputs zip enumerate print float32 patch_data_fn model_tensors map_ignored_layer_to_its_input co_argcount len items hasattr get_tensors name print process_layer eval slow_but_stable_topological_sort ModelBuilderContext sort assign_ids pop range insert len layers verbose Struct process_model open print_known_operations fuse compress node GraphDef Model dims_to_barracuda_shape insert get_tensor_dims inputs MessageToJson ParseFromString cleanup_layers read memories print sort write trim summary print_supported_ops update str min_lesson_length format SACTrainer GhostTrainer copy warning PPOTrainer get check_config rcls pow size range reversed zeros_like append discount_rewards Mock CameraResolution arange ones append array range ones AgentExperience append zeros sum range len vector_action_space_size pop number_visual_observations to_agentbuffer make_fake_trajectory vector_observation_space_size create_mock_brainparams create_mock_brainparams create_mock_brainparams create_mock_brainparams create_mock_brainparams zeros Mock Mock create_mock_batchedstep ActionInfo publish_trajectory_queue range AgentProcessor empty create_mock_policy add_experiences Mock create_mock_batchedstep assert_has_calls ActionInfo publish_trajectory_queue range call append AgentProcessor empty create_mock_policy add_experiences Mock create_mock_batchedstep assert_has_calls ActionInfo end_episode publish_trajectory_queue range call append AgentProcessor empty create_mock_policy add_experiences AgentManager create_mock_policy Mock get_nowait AgentManagerQueue put join remove _get_candidate_names convert _get_default_tempdir dirname abspath isfile next join str export_policy_model save_model sess graph create_policy_mock SerializationSettings model_path reset_default_graph brain_name initialize_or_load dirname abspath NNPolicy create_bc_module ppo_dummy_config create_mock_3dball_brain update items ppo_dummy_config create_bc_module create_mock_3dball_brain update items ppo_dummy_config current_lr create_bc_module create_mock_3dball_brain update items ppo_dummy_config create_bc_module create_mock_3dball_brain update items ppo_dummy_config create_mock_banana_brain create_bc_module update items ppo_dummy_config create_mock_banana_brain create_bc_module flatten list range len append range AgentBuffer resequence_and_append get_batch construct_fake_buffer assert_array make_mini_batch AgentBuffer reset_agent array resequence_and_append sample_mini_batch construct_fake_buffer AgentBuffer resequence_and_append construct_fake_buffer AgentBuffer resequence_and_append construct_fake_buffer AgentBuffer truncate values Curriculum Curriculum Curriculum dumps StringIO StringIO load_demonstration demo_to_buffer dirname abspath load_demonstration demo_to_buffer dirname abspath dirname abspath create_tf_graph brain_name setup_mock_brain load_weights init_load_weights zip assert_array_equal get_weights PPOTrainer create_policy GhostTrainer PPOTrainer subscribe_trajectory_queue advance put make_fake_trajectory BrainParameters AgentManagerQueue add_policy brain_name create_policy GhostTrainer PPOTrainer simulate_rollout get_nowait advance _swap_snapshots setup_mock_brain publish_policy_queue BrainParameters AgentManagerQueue add_policy brain_name create_policy MagicMock basic_options MagicMock parse_command_line parse_command_line MetaCurriculum increment_lessons Mock MetaCurriculum assert_called_with increment_lessons assert_not_called MetaCurriculum assert_called_with MetaCurriculum set_all_curricula_to_lesson_num MetaCurriculum Simple1DEnvironment safe_load loads MetaCurriculum _check_environment_trains setup_mock_brain NNPolicy list evaluate brain agent_id create_policy_mock create_batchedstep_from_brainparams reset_default_graph NNPolicy update_normalization to_agentbuffer make_fake_trajectory BrainParameters zeros range run MagicMock AgentGroupSpec basic_mock_brain basic_params get_action empty FakePolicy MagicMock basic_mock_brain BatchedStepResult basic_params get_action array FakePolicy MagicMock basic_mock_brain ActionInfo BatchedStepResult basic_params get_action array FakePolicy setup_mock_brain PPOOptimizer NNPolicy make_fake_trajectory update brain simulate_rollout _create_ppo_optimizer_ops_mock reset_default_graph update brain simulate_rollout _create_ppo_optimizer_ops_mock reset_default_graph update brain simulate_rollout _create_ppo_optimizer_ops_mock reset_default_graph items get_trajectory_value_estimates to_agentbuffer _create_fake_trajectory _create_ppo_optimizer_ops_mock next_obs reset_default_graph assert_array_almost_equal array discount_rewards Mock brain_name _increment_step BrainParameters assert_called_with add_policy PPOTrainer _update_policy simulate_rollout brain_name setup_mock_brain add_policy PPOTrainer create_policy values Mock brain_name make_brain_parameters add_policy PPOTrainer make_brain_parameters update NNPolicy setup_mock_brain PPOOptimizer SACOptimizer simulate_rollout evaluate_batch brain brain simulate_rollout prepare_update _execute_model update_dict make_mini_batch policy create_optimizer_mock reward_signal_eval reward_signal_update reward_signal_update reward_signal_eval dirname abspath create_optimizer_mock create_optimizer_mock reward_signal_eval reward_signal_update create_optimizer_mock reward_signal_eval reward_signal_update create_optimizer_mock reward_signal_eval reward_signal_update create_optimizer_mock reward_signal_eval reward_signal_update create_optimizer_mock reward_signal_eval reward_signal_update create_optimizer_mock reward_signal_eval reward_signal_update FakeTrainer dummy_config create_mock_brain end_episode episode_steps create_rl_trainer values clear_update_buffer items construct_fake_buffer create_rl_trainer create_rl_trainer subscribe_trajectory_queue advance put make_fake_trajectory AgentManagerQueue range setup_mock_brain SACOptimizer NNPolicy update brain simulate_rollout create_sac_optimizer_mock reset_default_graph brain simulate_rollout create_sac_optimizer_mock update_reward_signals reset_default_graph str SACTrainer save_model brain simulate_rollout num_experiences setup_mock_brain add_policy brain_name create_policy SACTrainer SACTrainer make_brain_parameters SamplerManager sample_all sampler_config_1 sampler_config_2 SamplerManager SamplerManager sample_all incorrect_uniform_sampler incorrect_sampler_config update safe_load print format _check_environment_trains Simple1DEnvironment generate_config _check_environment_trains Simple1DEnvironment generate_config _check_environment_trains Simple1DEnvironment generate_config Memory1DEnvironment _check_environment_trains generate_config _check_environment_trains Simple1DEnvironment generate_config _check_environment_trains Simple1DEnvironment generate_config _check_environment_trains Simple1DEnvironment generate_config _check_environment_trains Simple1DEnvironment generate_config _check_environment_trains Simple1DEnvironment generate_config clear assert_called_once_with Mock get_stats_summaries add_stat add_writer StatsReporter float range write_stats clear Mock write_text add_writer StatsReporter assert_called_once_with Simple1DEnvironment generate_config close SubprocessEnvManager simple_env_factory _check_environment_trains default_config TrainerController assert_called_with MagicMock start_learning assert_called_once MagicMock assert_not_called start_learning assert_called_once MagicMock MagicMock assert_called_once MagicMock advance add assert_not_called BrainParametersMock BrainParametersMock TrainerFactory BrainParameters brain_name generate TrainerFactory BrainParameters _load_config StringIO append from_observations range ones items to_agentbuffer add set make_fake_trajectory extract_stack filename get __old_np_array _check_no_float64 get _check_no_float64 __old_np_zeros get __old_np_ones _check_no_float64 join remove mkdir rmdir exists documentElement getAttribute parse join clean_previous_results parse_results get_unity_executable_path print exit returncode get_base_path copy2 run_standalone_build override_config_file init_venv exists run exists get_unity_executable_path print check_call update values add setLevel getLogger basicConfig setLevel tuple vector_action_size mean reshape array data compressed_data reshape process_pixels shape array mean isnan array _raise_on_nan_and_inf sum is_action_discrete _generate_split_indices ones discrete_action_branches len astype _raise_on_nan_and_inf any cast split append _process_vector_observation bool _process_visual_observation array observation_shapes enumerate range len perf_counter push reset method_handlers_generic_handler add_generic_rpc_handlers UnityEnvironment close MockCommunicator obs n_agents close get_agent_group_spec get_step_result reset MockCommunicator zip UnityEnvironment observation_shapes obs zip ones n_agents step close get_agent_group_spec get_step_result MockCommunicator set_actions zeros UnityEnvironment observation_shapes UnityEnvironment close MockCommunicator close RpcCommunicator close RpcCommunicator close RpcCommunicator list extend ObservationProto AgentInfoProto append prod range len fromarray uint8 BytesIO astype save ObservationProto generate_compressed_data extend shape ObservationProto shape tolist extend generate_compressed_data process_pixels rand generate_compressed_data process_pixels rand _process_vector_observation generate_list_agent_proto enumerate generate_compressed_proto_obs rand extend AgentInfoProto _process_visual_observation generate_uncompressed_proto_obs generate_compressed_proto_obs rand AgentInfoProto extend AgentGroupSpec CONTINUOUS batched_step_result_from_proto generate_list_agent_proto range AgentGroupSpec batched_step_result_from_proto DISCRETE generate_list_agent_proto action_mask AgentGroupSpec batched_step_result_from_proto DISCRETE generate_list_agent_proto action_mask AgentGroupSpec batched_step_result_from_proto DISCRETE generate_list_agent_proto action_mask AgentGroupSpec CONTINUOUS batched_step_result_from_proto generate_list_agent_proto action_mask BrainParametersProto agent_group_spec_from_proto extend CONTINUOUS generate_list_agent_proto AgentGroupSpec CONTINUOUS generate_list_agent_proto AgentGroupSpec _parse_side_channel_message _generate_side_channel_data send_int IntChannel FloatPropertiesChannel _parse_side_channel_message _generate_side_channel_data get_property set_property uuid4 _parse_side_channel_message _generate_side_channel_data RawBytesChannel encode send_raw_data get_and_clear_received_messages len buffer read_bool append write_bool IncomingMessage range OutgoingMessage buffer write_int32 read_int32 IncomingMessage OutgoingMessage IncomingMessage write_float32 buffer read_float32 OutgoingMessage read_string write_string buffer IncomingMessage OutgoingMessage IncomingMessage buffer OutgoingMessage read_float32_list write_float32_list set_gauge print find_packages find validate_packages replace endswith add set walk join print extract_version_string set values print join set_academy_version_string set_package_version enumerate split | # Car Racer with Unity ML-Agents ## Preview  ## Setup To setup the project follow the [installation guide](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Getting-Started.md) on the official ML-Agents repository. During the installation use the versions of the tools specified below: - Unity 2019.3 - mlagents 0.15.1 ## Usage ### Training To train the model use the command `mlagents-learn` | 2,929 |
maxjiang93/ugscnn | ['semantic segmentation'] | ['Spherical CNNs on Unstructured Grids'] | experiments/exp3_2d3ds/loader.py experiments/time/ops.py baseline/exp3_2d3ds/loader.py baseline/exp3_2d3ds/models/duc_hdc.py experiments/exp1_sphere_mnist/train.py baseline/exp3_2d3ds/utils/__init__.py experiments/exp3_2d3ds/train.py experiments/exp2_modelnet40/test.py experiments/exp2_modelnet40/train.py experiments/exp4_sphere_climate/loader.py gen_mesh.py experiments/exp1_sphere_mnist/model.py experiments/exp2_modelnet40/dataset.py experiments/exp4_sphere_climate/model.py meshcnn/mesh.py baseline/exp3_2d3ds/resize.py baseline/exp3_2d3ds/utils/transforms.py experiments/exp4_sphere_climate/partition.py baseline/exp3_2d3ds/utils/misc.py experiments/exp2_modelnet40/model.py baseline/exp3_2d3ds/train.py baseline/exp3_2d3ds/models/fcn8s.py baseline/exp3_2d3ds/utils/joint_transforms.py experiments/time/time.py experiments/time/utils.py experiments/exp3_2d3ds/test.py baseline/exp3_2d3ds/models/vgg.py experiments/exp3_2d3ds/stats.py meshcnn/utils.py experiments/exp1_sphere_mnist/viewData.py experiments/exp4_sphere_climate/train.py meshcnn/mesh_utils.py baseline/exp3_2d3ds/test.py experiments/time/model.py experiments/exp1_sphere_mnist/prepare_data.py meshcnn/ops.py experiments/exp4_sphere_climate/stats.py baseline/exp3_2d3ds/model.py experiments/exp3_2d3ds/model.py experiments/exp2_modelnet40/normalize.py baseline/exp3_2d3ds/models/resnet.py baseline/exp3_2d3ds/models/u_net.py baseline/exp3_2d3ds/models/__init__.py experiments/exp4_sphere_climate/test.py main SemSegLoader Up Up_intp Down UNet UNet_intp test plot_confusion_matrix export iou_score main accuracy test save_checkpoint iou_score main train _DenseUpsamplingConvModule ResNetDUC ResNetDUCHDC FCN8s ResNet resnet50 Bottleneck resnet152 conv3x3 resnet34 resnet18 BasicBlock resnet101 _DecoderBlock _EncoderBlock UNet vgg19 VGG vgg16_bn vgg19_bn vgg11_bn make_layers vgg11 vgg13 vgg13_bn vgg16 RandomHorizontallyFlip SlidingCropOld CenterCrop RandomSizedCrop FreeScale RandomRotate Compose Scale RandomCrop SlidingCrop RandomSized initialize_weights Conv2dDeformable _fast_hist check_mkdir evaluate CrossEntropyLoss2d AverageMeter PolyLR FocalLoss2d get_upsampling_weight sliced_forward DeNormalize FreeScale RandomVerticalFlip RandomGaussianBlur FlipChannels MaskToTensor DownSamp ResBlock Model project_2d_on_sphere sample_bilinear rand_rotation_matrix project_sphere_on_xy_plane rotate_grid sample_within_bounds linspace meshgrid main get_projection_grid main train test test1 test2 test3 test4 rnd_rot make_sgrid render_model ToMesh ProjectOnSphere rotmat CacheNPY ModelNet Model_tiny Model compute_stats StatsRecorder normalize_data main main S2D3DSegLoader Up Down SphericalUNet compute_stats StatsRecorder test plot_confusion_matrix export iou_score main accuracy test save_checkpoint iou_score main train ClimateSegLoader Up Down SphericalUNet compute_stats StatsRecorder accuracy test plot_confusion_matrix export iou_score main accuracy test save_checkpoint average_precision iou_score main train Model_tiny DownSamp ResBlock Model ResBlock DownSamp MeshConv_transpose MeshConv _MeshConv xyz2latlong sparse2tensor spmatmul MNIST_S2_Loader interp_r2tos2 icosphere export_spheres float_to_int e2p p2e hashable_rows unique_rows decimal_to_digits ResBlock DownSamp MeshConv_transpose MeshConv _MeshConv xyz2latlong sparse2tensor spmatmul MNIST_S2_Loader interp_r2tos2 append export_spheres range format arange product print yticks text xlabel astype tight_layout colorbar ylabel imshow title xticks max range len append range item format partition print mean dict eval zip zeros to tabulate len savez concatenate print export_file eval ckpt in_ch UNet DataParallel DataLoader ArgumentParser device export seed FCN8s repr export_file parse_args to load_my_state_dict format test manual_seed data_folder load print SemSegLoader add_argument len copyfile remove save info histc item append float range format model backward dataset zero_grad item info to step enumerate cross_entropy len info arange getLogger model SGD save_checkpoint DEBUG copy2 setLevel StepLR addHandler Adam epochs nv_max blackout_id isin sum StreamHandler resume mkdir decay info FileHandler join log_dir ResNetDUCHDC parameters argwhere train step makedirs load_url ResNet load_state_dict load_url ResNet load_state_dict load_url ResNet load_state_dict load_url ResNet load_state_dict load_url ResNet load_state_dict Conv2d load_url make_layers VGG load_state_dict load_url make_layers VGG load_state_dict load_url make_layers VGG load_state_dict load_url make_layers VGG load_state_dict load_url make_layers VGG load_state_dict load_url make_layers VGG load_state_dict load_url make_layers VGG load_state_dict load_url make_layers VGG load_state_dict mkdir fill_ isinstance modules zero_ BatchNorm2d weight kaiming_normal zeros abs reshape nanmean zip zeros sum diag arange pi cos pi dot sqrt uniform sin array array einsum meshgrid cos sin abs copy zeros sample_within_bounds astype uint8 sample_bilinear reshape project_sphere_on_xy_plane astype project_2d_on_sphere float64 cos pi interp_r2tos2 get_projection_grid open ndarray rand_rotation_matrix rotate_grid output_file sin mesh_file dump astype stack zip chunk_size MNIST min tqdm dot numpy array nll_loss dataset MNIST_S2_Loader Model datafile print icosphere nv nf load colormap COLOR_MAP_TYPE_VIRIDIS clear MatrixXd set_colors background_color set_mesh print reshape launch Viewer dot p2e sum array open load faces colormap COLOR_MAP_TYPE_VIRIDIS MatrixXd clear set_colors set_mesh print launch len Viewer choice p2e vertices icosphere open icosphere max colormap set_colors MatrixXd set_mesh shape vertices faces COLOR_MAP_TYPE_VIRIDIS launch Viewer mean load clear print min p2e std dot z reshape change_coordinates rotmat meshgrid einsum norm ones reshape sqrt stack zeros einsum intersects_id rotmat rand arccos pi load join sorted update T glob print StatsRecorder tqdm mean std load join sorted T replace glob tqdm save test_step CacheNPY name SourceFileLoader ModuleType mean get_num_threads enumerate int exec_module set_num_threads Model_tiny ModelNet save cuda locals train_step OrderedDict perf_counter deepcopy list label_binarize astype sum len S2D3DSegLoader SphericalUNet concatenate confusion_matrix savefig figure plot_confusion_matrix ravel cuda partition ClimateSegLoader reshape numpy label_binarize balance balance data LongTensor FloatTensor sqrt arctan2 xyz2latlong T concatenate astype pi linspace RegularGridInterpolator join format export_mesh_info icosphere makedirs hashable_rows unique int T dtype float_to_int reshape floor bitwise_xor zeros enumerate len astype warn asanyarray max __name__ decimal_to_digits int abs log10 clip issparse T ndarray MatrixXd isinstance tocoo fromcoo fromCOO SparseMatrixd SparseMatrixi toCOO MatrixXd isinstance MatrixXb MatrixXi SparseMatrixd array SparseMatrixi size list permute view | ## UGSCNN: Spherical CNNs on Unstructured Grids By: [Chiyu "Max" Jiang](http://maxjiang.ml/), [Jingwei Huang](http://stanford.edu/~jingweih/), [Karthik Kashinath](http://www.nersc.gov/about/nersc-staff/data-analytics-services/karthik-kashinath/), [Prabhat](http://www.nersc.gov/about/nersc-staff/data-analytics-services/prabhat/), [Philip Marcus](http://www.me.berkeley.edu/people/faculty/philip-s-marcus), [Matthias Niessner](http://niessnerlab.org/) \[[Project Website](http://www.maxjiang.ml/proj/ugscnn)\] \[[Paper](https://openreview.net/pdf?id=Bkl-43C9FQ)\]  ### Introduction This repository is based on our ICLR 2019 paper: [UGSCNN: Spherical CNNs on Unstructured Grids](https://openreview.net/pdf?id=Bkl-43C9FQ). The [project webpage](http://www.maxjiang.ml/proj/ugscnn) presents an overview of the project. In this project, we present an alternative convolution kernel for deploying CNNs on unstructured grids, using parameterized differential operators. More specifically we evaluate this method for the spherical domain that is discretized using the icosahedral spherical mesh. Our unique convolution kernel parameterization scheme achieves high parameter efficiency compared to competing methods. We evaluate our model for classification as well as semantic segmentation tasks. Please see `experiments/` for detailed examples. Our deep learning code base is written using [PyTorch](https://pytorch.org/) in Python 3, in conjunction with standard ML packages such as [Scikit-Learn](http://scikit-learn.org/stable/) and [Numpy](http://www.numpy.org/). | 2,930 |
mayank-git-hub/Text-Recognition | ['scene text detection', 'text classification', 'instance segmentation', 'semantic segmentation'] | ['PixelLink: Detecting Scene Text via Instance Segmentation'] | src/model/generic_model.py src/loader/dete_loader.py src/Dlmodel/TrainTestR.py src/prepare_metadata/prepare_metadata.py src/prepare_metadata/meta_synth.py src/model/crnn.py src/loader/reco_loader.py src/model/model_loader.py src/prepare_metadata/meta_artificial.py src/loader/mnist.py src/prepare_metadata/meta_ic15.py src/model/u_net_resnet_50_encoder.py src/model/resnet_own.py src/helper/utils.py src/Dlmodel/TestRD.py src/Dlmodel/TestOneImageR.py src/helper/profiler.py src/model/unet.py src/model/trial.py src/prepare_metadata/meta_coco.py src/loader/square.py src/Dlmodel/TrainTestD.py src/helper/read_yaml.py src/Dlmodel/TestOneImageD.py src/Dlmodel/TestOneImageRD.py main.py src/model/unet_parts.py src/pipeline_manager.py src/model/densenet.py src/model/u_net_resnet_50_parts.py src/loader/scale_two.py src/loader/art.py src/Dlmodel/Dlmodel.py src/helper/logger.py src/prepare_metadata/meta_own.py src/loader/generic_dataloader.py src/prepare_metadata/meta_ic13.py prepare_metadata test_one_r train_r train_d fscore test_entire_folder_d test_one_rd test_entire_folder_r test_entire_folder_rd main test_one_d test_d test_r prepare_metadata test_one_r train_r train_d test_entire_folder_d test_one_rd PipelineManager test_entire_folder_r test_entire_folder_rd test_one_d test_d test_r Dlmodel TestOneImageDClass TestOneImageRClass TestOneImageRDClass TrainTestR TrainTestD TrainTestR Logger Profiler read_yaml scores line one_hot overlap_remove inside_point get_connected_components remove_small_boxes FocalLoss line_intersection get_rotated_bbox get_f_score homographic_rotation precision_recall_fscore strLabelConverter intersection_union ArtificialGen DeteLoader own_DataLoader trainLoader RecoDataloader scale_two square CRNN_orig CRNN_resnet BidirectionalLSTM densenet161 DenseNet densenet169 densenet201 _DenseLayer _DenseBlock _Transition densenet121 model load_weights load_model conv1x1 resnext50_32x4d ResNet resnet50 resnext101_32x8d Bottleneck resnet152 conv3x3 resnet34 resnet18 BasicBlock resnet101 own UNet Up DoubleConv Inconv Down JustUp OutConv UNetWithResnet50Encoder UpBlockForUNetWithResNet50 ConvBlock Bridge MetaArtificial MetaCoco MetaIC13 MetaIC15 MetaOwn MetaSynth float get_f_score dump plot profiler info dump plot profiler info start_testing profiler start_testing profiler test_one_image_r profiler print test_one_image_r profiler open dump test_one_image_rd profiler mkdir profiler info test_one_image_d profiler print mkdir profiler create_annot profiler info set int64 astype int64 astype pointPolygonTest float64 astype inside_point int64 line_intersection convexHull contourArea range float64 astype bool sum range intersection_union len zeros array range intersection_union reshape astype int64 append minAreaRect range len get_rotated_bbox contourArea append range len FILLED overlap_remove where RETR_LIST remove_small_boxes clf list exp shape pad range imsave add_edges_from Graph findContours astype copy mkdir zip flip uint8 connected_components drawContours CHAIN_APPROX_SIMPLE print reshape float32 add_nodes_from zeros len load scores endswith print lower append listdir exists enumerate open isinstance view size get_device to deg2rad where getPerspectiveTransform show squeeze perspectiveTransform shape imshow append range warpAffine astype T uint8 print reshape min float32 dot int32 array len list DenseNet group load_url match load_state_dict keys compile list DenseNet group load_url match load_state_dict keys compile list DenseNet group load_url match load_state_dict keys compile list DenseNet group load_url match load_state_dict keys compile OrderedDict items list load_state_dict load load_weights cuda CRNN load_url ResNet load_state_dict load_url ResNet load_state_dict load_url ResNet load_state_dict load_url ResNet load_state_dict load_url ResNet load_state_dict ResNet ResNet format print Sequential MaxPool2d add_module summary convRelu cuda | # Pytorch Implementation of [Pixel-LINK](https://arxiv.org/pdf/1801.01315.pdf) ## A brief abstract of your project including the problem statement and solution approach We are attempting to detect all kinds of text in the wild. The technique used for text detection is based on the paper PixelLink: Detecting Scene Text via Instance Segmentation (https://arxiv.org/abs/1801.01315) by Deng et al. The text instances present in the scene images lie very close to each other, and it is challenging to distinguish them using semantic segmentation. So, there is a need of instance segmentation. The approach consists of two key steps: a) Linking of pixels in the same text instance - Segmentation step, b) Text bounding box extraction using the linking done. There are two kinds of predictions getting done here at each pixel level in the image: a) Text/non-text prediction, b) Link prediction. This approach sets it apart from other kinds of methodologies used so far for text detection. Before PixelLink, the SOTA approaches on text detection does two kinds of prediction: a) Text/non-text prediction, b) Location Regression. Here both of these predictions are made at one go taking many fewer number of iterations and less training data. | 2,931 |
mayu-ot/hidden-challenges-MR | ['moment retrieval'] | ['Uncovering Hidden Challenges in Query-Based Video Moment Retrieval'] | src/settings.py src/toolbox/data_converters.py src/toolbox/baseline.py src/toolbox/utils.py src/data/make_dataset.py src/toolbox/visualization.py src/experiments/blind_baselines.py src/toolbox/eval.py main save_vfeat_h5 eval_model display_score load_charades_dataset plot_performance_summary train_prior_only main load_dataset load_activitynet_dataset train_action_aware_blind train predict_job SegmentGeneratorKDE predict CharadesSTA2Instances ActivityNetCap2Instances categorize_results evaluate accumulate_metrics get_first_action summarize_results_per_class location_error _tiou _load_top_actions _nms sentence2token plot_ranking_comparison plot_performance_per_class plot_performance_per_duration getLogger to_csv info DataFrame read_csv merge read_csv info getLogger load ActivityNetCap2Instances open CharadesSTA2Instances read_csv load_activitynet_dataset load_charades_dataset SegmentGeneratorKDE fit _load_top_actions SegmentGeneratorKDE fit train_action_aware_blind train_prior_only load_dataset text get_height get_width get_x arange display_score bar title despine figure legend dirname savefig xticks makedirs evaluate accumulate_metrics predict load_dataset print train eval_model plot_performance_summary sentence2token hstack copy _nms sample sum len tqdm append items zip append tuple iterrows tolist minimum maximum print tqdm any append _tiou append items print append sentence2token cat_fn append items sum len _nlp is_stop lemma_ append append maximum minimum list arange bar despine figure legend xticks keys len plot barh set_yticks set_xlim add_subplot text GridSpec axis figure get_xlim keys invert_xaxis set_ylim append hist legend zip | hidden-challenges-MR ============================== Codes of our paper "Uncovering Hidden Challenges in Query-Based Video Moment Retrieval" (BMVC'20) [[Project page](https://mayu-ot.github.io/hidden-challenges-MR/) | [arXiv](https://arxiv.org/abs/2009.00325) | [YouTube](https://www.youtube.com/watch?v=A_W50Zz6TuE&feature=emb_title) | [BMVC virtual](https://www.bmvc2020-conference.com/conference/papers/paper_0306.html)] ## Dependencies Docker (recommended) ```shell $ docker build -t hidden-challenges-mr . ``` or | 2,932 |
mayukh18/Hybrid-Style-Siamese-Network | ['style transfer', 'image retrieval'] | ['Hybrid Style Siamese Network: Incorporating style loss in complementary apparels retrieval'] | hssn.py utils.py TripletGenerator Data_augmentation SiameseNet ImDataset ValidationGenerator HybridTripletLoss jsonf MAPScorer printx Data_augmentation norm list index argsort append range print str | # Hybrid Style Siamese Network This is the code for the paper: Hybrid Style Siamese Network: Incorporating style loss in complementary apparels retrieval <br> Mayukh Bhattacharyya, Sayan Nag <br> Computer Vision for Fashion, Art and Design, CVPR 2020 [[Paper](https://arxiv.org/abs/1912.05014)][[Video Presentation](https://sites.google.com/view/cvcreative2020/program/paper-4-hybrid-style-siamese-network)] Hybrid Style Siamese Network incorporates style loss into triplet loss, in order to aid in complementary images retrieval. In the paper, it had been used for the application of complementary apparels retrieval. The performance in regards to the retrieval of complementary items in terms of MAP scores. | 2,933 |
mberr/ea-active-learning | ['entity alignment', 'active learning'] | ['Active Learning for Entity Alignment'] | src/kgm/utils/__init__.py executables/collate_results.py tests/utils/test_common.py src/kgm/__init__.py src/kgm/eval/matching.py src/kgm/eval/__init__.py src/kgm/data/__init__.py src/kgm/models/matching/__init__.py src/kgm/utils/mlflow_utils.py tests/modules/test_embeddings.py src/kgm/models/matching/gcn_align.py src/kgm/modules/similarity.py src/kgm/modules/embeddings/init/__init__.py src/kgm/utils/common.py src/kgm/data/knowledge_graph.py src/kgm/modules/embeddings/__init__.py tests/base.py tests/modules/test_similarity.py src/kgm/active_learning/__init__.py src/kgm/eval/common.py src/kgm/active_learning/bayesian.py tests/test_active_learning.py tests/utils/test_torch_utils.py src/kgm/eval/active_learning.py src/kgm/modules/embeddings/init/base.py src/kgm/active_learning/base.py src/kgm/modules/graph.py src/kgm/training/active_learning.py src/kgm/modules/__init__.py src/kgm/modules/embeddings/base.py src/kgm/utils/torch_utils.py src/kgm/modules/embeddings/norm.py src/kgm/active_learning/similarity.py src/kgm/training/matching.py src/kgm/training/__init__.py tests/modules/test_losses.py tests/training/test_matching_training.py tests/models/test_matching_models.py src/kgm/models/matching/base.py src/kgm/modules/losses.py src/kgm/models/__init__.py src/kgm/data/edge_modifiers.py tests/training/test_active_learning_training.py tests/data/test_knowledge_graph.py tests/modules/test_graph.py src/kgm/active_learning/graph.py src/kgm/active_learning/learning.py executables/evaluate_active_learning_heuristic.py _plots _auc_table active_learning_heuristic_experiment main _common_part evaluate_heuristic AlignmentOracle RandomHeuristic NodeActiveLearningHeuristic get_node_active_learning_heuristic_class_by_name get_node_active_learning_heuristic_by_name _default_heuristic_name_normalizer ModelBasedHeuristic BayesianHeuristic VariationRatioHeuristic BALDAggregator NormalEntropyAggregator VariationRatioAggregator BALDHeuristic SoftmaxEntropyBayesianAggregator BayesianSoftmaxEntropyHeuristic BayesianAggregator get_node_with_max_min_ref_distance ClosenessCentralityHeuristic CentralityHeuristic ApproximateVertexCoverHeuristic BufferedHeuristic PageRankCentralityHeuristic MaximumShortestPathDistanceHeuristic HarmonicCentralityHeuristic _compute_shortest_path_distances BetweennessCentralityHeuristic DegreeCentralityHeuristic PreviousExperienceBasedHeuristic OneVsAllBinaryEntropyHeuristic SoftmaxEntropyHeuristic BaseMinMaxSimilarityHeuristic MinMaxSimilarityHeuristic MostProbableMatchingInUnexploredRegionHeuristic BatchOptimizedMaxSimilarityHeuristic _cluster_node_representations_in_joint_space SimilarityBasedHeuristic MaxSimilarityHeuristic _create_one_hot_available_per_cluster_matrix CoreSetHeuristic RemoveEdgesHeuristic EdgeModifierHeuristic add_self_loops ZipExtractor _WK3l TarExtractor Extractor dataset_name_normalization WK3l15k EntityAlignment _label_alignment_to_id_alignment download_file_from_google_drive DBP15kJAPE load_triples _DBP15k compact_knowledge_graph_alignment compact_single_alignment get_synthetic_math_graph compact_columns compact_graph sub_graph_alignment get_other_side save_response_content DWY100k apply_compaction exact_self_alignment get_erdos_renyi _load_alignment validation_split add_inverse_triples compact_knowledge_graph_alignment_dataset get_dataset_by_name OnlineKnowledgeGraphAlignmentDatasetLoader get_confirm_token available_datasets DBP15kFull KnowledgeGraphAlignmentDataset MatchSideEnum _load_file KnowledgeGraph WK3l120k metrics_long_to_wide auc_qh get_significance aggregate_auc_qh get_rank evaluate_alignment evaluate_model EdgeWeightsEnum AbstractKGMatchingModel KGMatchingModel get_matching_model_by_name GCNAlign MessagePassingBlock InverseSourceOutDegreeWeighting SymmetricWeighting SumAggregator GCNBlock LinearMessageCreator OnlyUpdate IdentityMessageCreator InverseTargetInDegreeWeighting NodeUpdater MessageCreator MessageAggregator EdgeWeighting _guess_num_nodes MissingEdgeTypesException PairwiseLoss MarginLoss FullMatchingLoss MatchingLoss sample_from_candidates sample_exclusive SampledMatchingLoss DistanceToSimilarity get_similarity L1CDist BoundInverseTransformation SimilarityNormalization NegativeTransformation Similarity transformation_normalizer SimilarityEnum generalized_k_means CosineSimilarity LpSimilarity CSLSNormalization DotProductSimilarity NodeEmbeddings get_embedding LpNormalization L2NodeEmbeddingNormalizer L1NodeEmbeddingNormalizer NoneNodeEmbeddingNormalizer NodeEmbeddingNormalizer NodeEmbeddingNormalizationMethod norm_method_normalizer SqrtIndividualNodeEmbeddingInitializer NodeEmbeddingInitializer RandomNodeEmbeddingInitializer GcnNodeEmbeddingInitializer SqrtTotalNodeEmbeddingInitializer StdOneNodeEmbeddingInitializer init_method_normalizer NodeEmbeddingInitMethod evaluate_active_learning_heuristic AlignmentModelTrainer EarlyStoppingTrainer argparse_bool generate_experiments reduce_kwargs_for_method from_dot identity get_subclass_by_name to_dot enum_values kwargs_or_empty get_all_subclasses value_to_enum _resolve_experiment_buffer get_params_from_experiments buffered_load_parameters run_experiments log_metrics_to_mlflow log_params_to_mlflow _has_non_empty_metrics get_results get_metric_history_for_runs buffered_load_metric_history _get_run_information_from_experiments csls get_optimizer_class_by_name remove_node_from_edges_while_keeping_paths get_device softmax_entropy_from_logits truncated_normal_ _guess_num_nodes filter_edges_by_nodes resolve_device_from_to_kwargs GenericTest TestTests RandomActiveLearningHeuristicTests MaximumShortestPathDistanceHeuristicTests OracleTests OneVsAllBinaryEntropyHeuristicTests MaxSimilarityHeuristicTests MostProbableMatchingsInUnexploredRegionsHeuristicTest HeuristicTestTests ClosenessCentralityHeuristicTests BetweennessCentralityHeuristicTests CoreSetHeuristicHeuristicTests RemoveEdgesHeuristicTests BatchOptimizedMaxSimilarityHeuristicTests BALDHeuristicTests DegreeCentralityHeuristicTests MinMaxSimilarityHeuristicHeuristicTests BayesianSoftmaxEntropyHeuristicTests SoftmaxEntropyHeuristicTests HarmonicCentralityHeuristicTests _BayesianHeuristicTests PreviousExperienceBasedHeuristicTests _get_dataset VariationRatioHeuristicTests ApproximateVertexCoverHeuristicTests DummyModel _EdgeModifierHeuristicTests PageRankCentralityHeuristicTests _NodeActiveLearningHeuristicTests test_add_inverse_triples test_exact_self_alignment _AlignmentGenerationTests CompactionTests _KnowledgeGraphGenerationTests SubGraphAlignmentTests ErdosRenyiTests ExactSelfAlignmentTests MathTests test_add_self_loops _random_tensor _get_cycle_edge_tensor _KGMatchingTests GCNAlignTests NodeEmbeddingNormalizerTestsTests StdOneNodeEmbeddingInitializerTests _NodeEmbeddingInitializerTests _CommonEmbeddingInitializerTests NodeEmbeddingTests L2NodeEmbeddingNormalizerTests NodeInitializerTestTests SqrtIndividualNodeEmbeddingInitializerTests NoneNodeEmbeddingNormalizerTests L1NodeEmbeddingNormalizerTests _NodeEmbeddingNormalizerTests SqrtTotalNodeEmbeddingInitializerTests RandomNodeInitializationTests _MessagePassingBlockTests SymmetricWeightingTests EdgeWeightingTestsTests _MessageCreatorTests NodeUpdaterTestsTests _MessageAggregatorTests MessagePassingBlockTestsTests GCNBlockTests LinearMessageCreatorTests InverseTargetInDegreeWeightingTests _NodeUpdaterTests OnlyUpdateTests _CommonGraphTests MessageAggregatorTestsTests _EdgeWeightingTests GCNBlockBiasTests MessageCreatorTestsTests SumAggregatorTests InverseSourceOutDegreeWeightingTests IdentityMessageCreatorTests _MatchingLossTests test_sample_exclusive _PairwiseLossTests FullMatchingLossTests MatchingLossTestTests SampledMatchingLossTests SelfAdversarialSampledMatchingLossTests PairwiseLossTestTests MarginLossTests _CommonTransformationTests L1SimilarityTests L1CDistTests CosineSimilarityTests NegativeTransformationTests test_get_similarity _CommonSimilarityTests DotProductSimilarityTests L2SimilarityTests _CommonTests BoundInverseTransformationTests Test AlignmentModelTrainerTests DummyModel DotConversionTests ExperimentGenerationTests test_filter_edges_by_nodes test_get_optimizer_class_by_name CSLSTests RemoveNodeFromEdgesWhileKeepingPathsTests format to_latex get_significance print copy apply unstack aggregate_auc_qh auc_qh merge savefig set_index relplot set evaluate_active_learning_heuristic get_node_active_learning_heuristic_by_name log_metrics_to_mlflow _common_part get EarlyStoppingTrainer evaluate_heuristic get_dataset_by_name get_similarity from_dot GCNAlign warning SampledMatchingLoss device is_available to pop deepcopy basicConfig sorted list items add_argument dataset available_datasets tracking_uri set_experiment subset dict run_experiments ArgumentParser info parse_args set_tracking_uri get_node_active_learning_heuristic_class_by_name argmax list min todense floyd_warshall edge_tensor_unique edge_weights restricted_available update sorted ones_like stack tensor max values update sorted stack len isinstance t shape stack append tensor range list apply_compaction dict info max values len KnowledgeGraph triples inverse_triples unique info compact_columns apply_compaction enumerate compact_single_alignment compact_graph compact_knowledge_graph_alignment load int with_inverse_triples isinstance compact with_self_loops get_subclass_by_name validation_split info dataset_loader_cls list info _load_file zip _label_alignment_to_id_alignment tensor map set warning info len info _label_alignment_to_id_alignment _load_file len int num_train default_generator argsort randperm manual_seed train cat int EntityAlignment clone t stack inverse_triples KnowledgeGraph self_loops num_entities int KnowledgeGraph tolist set randperm repeat append EntityAlignment as_tensor num_entities cat stack warning erdos_renyi_graph randint empty list range get get_confirm_token save_response_content Session items list startswith int groupby min metrics_long_to_wide append auc sort_values merge pivot groupby append DataFrame ttest_ind_from_stats values float unsqueeze all_to_all t device item get_rank to range lower unsqueeze device randint long range len value_to_enum isinstance all_to_all new_zeros shape new_ones unsqueeze unique device index_add_ float argmax range get_subclass_by_name init_class norm_class info AlignmentOracle dict warning fatal num_available reset_parameters trange evaluate_model difference normalizer isinstance set append sorted keys info items list name set difference add warning kwargs_or_empty signature keys update items list isinstance dict items list dict warning startswith split sorted log_params to_dot keys range len log_metrics to_dot start_run join sorted list search_runs experiment log_params_to_mlflow log_metrics_to_mlflow dict info end_run hexdigest enumerate len metrics search_runs list extend MlflowClient token reset_index to_csv info sorted MlflowClient product isinstance to_csv get_metric_history_for_runs info append read_csv Path _resolve_experiment_buffer experiment_id buffered_load_parameters tolist Path mkdir info buffered_load_metric_history set_tracking_uri get_experiment_by_name as_tensor stack cat unique warning isinstance device log_softmax softmax mean size enumerate ndimension squeeze add_ copy_ shape normal_ alignment validation_split sub_graph_alignment get_erdos_renyi left_graph exact_self_alignment right_graph add_self_loops deepcopy relation_label_to_id clone get_erdos_renyi deepcopy relation_label_to_id clone get_erdos_renyi add_inverse_triples arange cat dict frozenset frozenset in_channels out_channels list map add set randrange sample_exclusive randint as_tensor range linear_kernel DotProductSimilarity LpSimilarity cdist LpSimilarity cdist cosine_similarity CosineSimilarity get_similarity list view stack range filter_edges_by_nodes lower get_optimizer_class_by_name | # Active Learning for Entity Alignment [](https://arxiv.org/abs/2001.08943) [](https://docs.python.org/3.8/) [](https://pytorch.org/docs/stable/index.html) [](https://opensource.org/licenses/MIT) This repository contains the source code for the paper ``` Active Learning for Entity Alignment Max Berrendorf*, Evgeniy Faerman*, and Volker Tresp https://arxiv.org/abs/2001.08943 | 2,934 |
mbiesenb/TensorFlowTikTak | ['unity'] | ['Unity: A General Platform for Intelligent Agents'] | ml-agents/mlagents/envs/communicator_objects/environment_parameters_proto_pb2.py ml-agents/tests/trainers/test_trainer_controller.py ml-agents/mlagents/trainers/buffer.py ml-agents/mlagents/envs/communicator_objects/unity_rl_initialization_input_pb2.py ml-agents/mlagents/envs/communicator_objects/brain_parameters_proto_pb2.py ml-agents/tests/envs/test_envs.py ml-agents/mlagents/envs/communicator_objects/__init__.py ml-agents/mlagents/envs/rpc_communicator.py ml-agents/mlagents/trainers/ppo/__init__.py gym-unity/gym_unity/envs/__init__.py ml-agents/mlagents/envs/communicator_objects/agent_action_proto_pb2.py ml-agents/mlagents/trainers/learn.py gym-unity/gym_unity/envs/unity_env.py ml-agents/mlagents/trainers/bc/trainer.py ml-agents/mlagents/trainers/policy.py ml-agents/mlagents/envs/communicator_objects/unity_rl_initialization_output_pb2.py ml-agents/tests/trainers/test_curriculum.py ml-agents/mlagents/trainers/meta_curriculum.py ml-agents/mlagents/trainers/curriculum.py ml-agents/mlagents/trainers/ppo/models.py ml-agents/mlagents/envs/communicator_objects/space_type_proto_pb2.py ml-agents/mlagents/envs/communicator_objects/unity_output_pb2.py ml-agents/mlagents/envs/communicator_objects/unity_input_pb2.py gym-unity/gym_unity/__init__.py ml-agents/mlagents/trainers/ppo/policy.py ml-agents/mlagents/envs/communicator_objects/engine_configuration_proto_pb2.py ml-agents/mlagents/envs/communicator_objects/brain_type_proto_pb2.py ml-agents/mlagents/envs/socket_communicator.py gym-unity/setup.py ml-agents/mlagents/trainers/trainer_controller.py ml-agents/mlagents/envs/communicator_objects/agent_info_proto_pb2.py ml-agents/mlagents/envs/communicator_objects/unity_to_external_pb2_grpc.py ml-agents/tests/trainers/test_ppo.py ml-agents/mlagents/envs/brain.py ml-agents/mlagents/trainers/bc/policy.py ml-agents/tests/trainers/test_bc.py ml-agents/tests/mock_communicator.py ml-agents/mlagents/envs/communicator_objects/unity_message_pb2.py ml-agents/mlagents/trainers/models.py ml-agents/mlagents/trainers/__init__.py ml-agents/mlagents/envs/communicator_objects/resolution_proto_pb2.py ml-agents/mlagents/envs/communicator_objects/unity_to_external_pb2.py ml-agents/mlagents/envs/communicator_objects/unity_rl_input_pb2.py ml-agents/tests/trainers/test_buffer.py ml-agents/mlagents/trainers/trainer.py ml-agents/mlagents/envs/communicator.py ml-agents/setup.py ml-agents/mlagents/envs/communicator_objects/unity_rl_output_pb2.py ml-agents/mlagents/envs/__init__.py ml-agents/mlagents/trainers/bc/__init__.py gym-unity/tests/test_gym.py ml-agents/mlagents/envs/exception.py ml-agents/mlagents/envs/environment.py ml-agents/mlagents/trainers/bc/models.py ml-agents/mlagents/envs/communicator_objects/command_proto_pb2.py ml-agents/mlagents/trainers/exception.py ml-agents/tests/trainers/test_meta_curriculum.py ml-agents/mlagents/trainers/ppo/trainer.py ml-agents/mlagents/envs/communicator_objects/header_pb2.py UnityGymException UnityEnv test_gym_wrapper test_multi_agent BrainInfo BrainParameters Communicator UnityEnvironment UnityException UnityTimeOutException UnityEnvironmentException UnityActionException RpcCommunicator UnityToExternalServicerImplementation SocketCommunicator UnityToExternalServicer UnityToExternalStub add_UnityToExternalServicer_to_server BufferException Buffer Curriculum CurriculumError MetaCurriculumError TrainerError main run_training MetaCurriculum LearningModel Policy UnityPolicyException UnityTrainerException Trainer TrainerController BehavioralCloningModel BCPolicy BehavioralCloningTrainer PPOModel PPOPolicy PPOTrainer get_gae discount_rewards MockCommunicator test_initialization test_reset test_close test_step test_handles_bad_filename 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_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 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_initialization test_initialize_trainers dummy_bc_config dummy_bad_config dummy_config dummy_start test_load_config sample step MockCommunicator UnityEnv step MockCommunicator UnityEnv method_handlers_generic_handler add_generic_rpc_handlers start_learning int str TrainerController int Process getLogger print start info append randint docopt range list zeros_like size reversed range asarray tolist discount_rewards UnityEnvironment close MockCommunicator UnityEnvironment close MockCommunicator reset str local_done print agents step close reset MockCommunicator UnityEnvironment len UnityEnvironment close MockCommunicator reset_default_graph close 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 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 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 reset_default_graph assert_array_almost_equal array discount_rewards TrainerController | <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) **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) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be | 2,935 |
mbinkowski/nntimeseries | ['time series'] | ['Autoregressive Convolutional Neural Networks for Asynchronous Time Series'] | results_display.py nnts/household.py nnts/artificial.py nnts/models/LR.py nnts/config.py nnts/models/LSTM.py nnts/models/SOCNN.py nnts/utils.py nnts/user.py nnts/book.py nnts/keras_utils.py nnts/__init__.py nnts/models/__init__.py nnts/models/LSTM2.py nnts/models/CNN.py nnts/models/_imports_.py generate_artificial.py nnts/lobster.py nnts/_imports_.py nnts/models/CNN_book.py total_params get_pivot NoisySignal ArtificialGenerator BinaryNoise GaussianNoise pnl_loss def_pnl def_pnl_loss_L2 BookGenerator download_and_unzip HouseholdGenerator HouseholdAsynchronousGenerator multitask_accuracies ResetLSTM MyCallback ThresholdStopper TensorBoard PrintStates cross_entropy_loss def_R2 LrReducer Test Activation_ get_multitask_cross_entropy LOBSTERGenerator parse get_generator Generator list_of_param_dicts Model UserGenerator get_param_no ModelRunner CNNmodel CNNmodel LRmodel LSTMmodel LSTMmodel SOCNNmodel items list copy rename append pivot sigmoid int time urlretrieve join remove print extractall rand close apply mkdir rmdir to_pickle ZipFile read_csv clip append arange make_acc list prod join print repr append listdir split print repr | # nntimeseries The repository provides the code for the paper [*Autoregressive Convolutional Neural Networks for Asynchronous Time Series*](https://arxiv.org/abs/1703.04122), as well as general code for running grid serach on keras models. Files 'nnts/models/{CNN, LSTM, LSTM2, LR, SOCNN}.py' provide code for testing respective models, with the last one implementing the proposed Significance-Offset CNN and LSTM2 implementing multi-layer LSTM. **Basic Usage** Each of the model files can be run as a script, e.g. - `python ./CNN.py --dataset=artificial` # default save file - `python ./SOCNN.py --dataset=household --save_file=results/household_0.pkl` | 2,936 |
mblondel/soft-dtw | ['time series', 'dynamic time warping'] | ['Soft-DTW: a Differentiable Loss Function for Time-Series'] | examples/plot_interpolation.py sdtw/dataset.py sdtw/tests/test_path.py sdtw/barycenter.py sdtw/chainer_func.py sdtw/__init__.py setup.py sdtw/tests/test_chainer_func.py sdtw/tests/test_soft_dtw.py sdtw/distance.py sdtw/path.py sdtw/setup.py examples/plot_chainer_MLP.py sdtw/soft_dtw.py examples/plot_barycenter.py configuration split_time_series train Objective MLP sdtw_barycenter SoftDTWLoss list_ucr _parse_ucr load_ucr SquaredEuclidean gen_all_paths delannoy_num configuration SoftDTW _grad _func test_grad test_gen_all_paths _softmax test_soft_dtw _softmin test_soft_dtw_grad_X test_soft_dtw_grad _soft_dtw_bf add_subpackage Configuration int round Objective setup TupleDataset Adam StandardUpdater Trainer SerialIterator run minimize ones ravel array len int list reshape strip map open append split join _parse_ucr array zeros range zeros copy add_extension reshape Variable reshape backward RandomState randn print check_grad ravel list delannoy_num assert_equal gen_all_paths len max array compute assert_almost_equal _soft_dtw_bf compute reshape grad assert_array_almost_equal make_func SoftDTW compute jacobian_product reshape grad SquaredEuclidean assert_array_almost_equal make_func SoftDTW | mblondel/soft-dtw | 2,937 |
mbohlkeschneider/gluon-ts | ['time series', 'anomaly detection'] | ['High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes'] | test/shell/test_nested_params.py src/gluonts/model/lstnet/__init__.py src/gluonts/mx/block/sndense.py src/gluonts/mx/distribution/beta.py src/gluonts/mx/distribution/logit_normal.py src/gluonts/model/tpp/distribution/weibull.py test/distribution/test_transformed_distribution.py test/dataset/test_data_loader.py test/test_transform.py examples/run_rolling_forecast_backtest.py test/time_feature/test_seasonality.py src/gluonts/mx/util.py src/gluonts/mx/block/decoder.py src/gluonts/nursery/__init__.py src/gluonts/mx/model/predictor.py src/gluonts/shell/sagemaker/train.py src/gluonts/nursery/spliced_binned_pareto/gaussian_model.py src/gluonts/mx/distribution/box_cox_transform.py test/dataset/test_stat.py src/gluonts/nursery/glide/util.py src/gluonts/mx/representation/hybrid_representation.py src/gluonts/dataset/repository/_m3.py test/dataset/test_multivariate_grouper.py test/dataset/test_dataset_types.py src/gluonts/nursery/anomaly_detection/__init__.py src/gluonts/model/estimator.py src/gluonts/nursery/glide/_partition.py src/gluonts/mx/block/quantile_output.py src/gluonts/model/seq2seq/__init__.py src/gluonts/model/gp_forecaster/_network.py src/gluonts/model/deepvar/__init__.py src/gluonts/mx/block/encoder.py test/core/test_ty.py src/gluonts/mx/representation/discrete_pit.py test/distribution/test_distribution_sampling.py test/kernels/test_rbf_kernel.py test/distribution/test_inflated_beta.py src/gluonts/dataset/jsonl.py test/evaluation/test_metrics.py test/model/deepvar_hierarchical/test_deepvar_hierarchical.py src/gluonts/env.py src/gluonts/mx/distribution/distribution.py src/gluonts/model/simple_feedforward/_network.py conftest.py src/gluonts/mx/block/feature.py src/gluonts/dataset/rolling_dataset.py test/representation/test_bin.py src/gluonts/mx/serde.py src/gluonts/_version.py src/gluonts/nursery/gmm_tpp/gmm_base.py src/gluonts/support/__init__.py test/block/test_regularization.py test/block/test_scaler.py src/gluonts/dataset/loader.py src/gluonts/torch/distributions/piecewise_linear.py src/gluonts/model/gpvar/__init__.py src/gluonts/transform/feature.py src/gluonts/nursery/spliced_binned_pareto/tcn.py src/gluonts/model/__init__.py test/distribution/test_distribution_output_shapes.py src/gluonts/mx/__init__.py test/model/wavenet/__init__.py src/gluonts/nursery/glide/parallel.py evaluations/show_results.py src/gluonts/dataset/artificial/_base.py src/gluonts/mx/block/dropout.py test/model/npts/__init__.py test/model/deepstate/test_model.py src/gluonts/dataset/artificial/__init__.py src/gluonts/time_feature/holiday.py test/model/gpvar/test_gpvar.py src/gluonts/dataset/split/splitter.py src/gluonts/core/serde/_json.py src/gluonts/nursery/spliced_binned_pareto/training_functions.py src/gluonts/model/deepvar_hierarchical/__init__.py src/gluonts/mx/batchify.py test/distribution/test_distribution_output_serde.py src/gluonts/model/transformer/layers.py src/gluonts/model/renewal/_transform.py test/distribution/test_piecewise_linear.py src/gluonts/mx/distribution/deterministic.py src/gluonts/model/tpp/forecast.py src/gluonts/mx/model/estimator.py src/gluonts/model/trivial/_estimator.py test/model/npts/test_npts.py src/gluonts/mx/distribution/lds.py src/gluonts/model/canonical/_network.py src/gluonts/torch/component.py test/torch/model/test_simple_torch_model.py test/model/test_backtest.py examples/warm_start.py test/model/deepstate/test_issm.py src/gluonts/nursery/anomaly_detection/supervised_metrics/bounded_pr_auc.py src/gluonts/mx/representation/local_absolute_binning.py src/gluonts/torch/model/forecast_generator.py test/dataset/test_jsonl.py test/distribution/test_mx_distribution_inference.py src/gluonts/core/serde/__init__.py test/torch/model/test_torch_forecast.py src/gluonts/core/component.py test/model/tpp/test_tpp_predictor.py src/gluonts/nursery/spliced_binned_pareto/data_functions.py src/gluonts/model/trivial/identity.py src/gluonts/model/deepstate/_network.py test/model/gp_forecaster/test_inference.py src/gluonts/model/r_forecast/__init__.py test/distribution/test_flows.py src/gluonts/mx/kernels/_rbf_kernel.py src/gluonts/dataset/artificial/generate_synthetic.py src/gluonts/nursery/spliced_binned_pareto/distr_tcn.py src/gluonts/model/san/_layers.py src/gluonts/mx/trainer/callback.py src/gluonts/mx/context.py src/gluonts/mx/kernels/_kernel_output.py src/gluonts/nursery/glide/sequential.py src/gluonts/mx/distribution/dirichlet.py src/gluonts/model/deep_factor/_estimator.py src/gluonts/testutil/dummy_datasets.py test/test_itertools.py test/model/deepvar_hierarchical/generate_hierarchical_dataset.py src/gluonts/shell/train.py test/nursery/test_autogluon_tabular.py src/gluonts/evaluation/metrics.py src/gluonts/nursery/sagemaker_sdk/model.py test/time_feature/test_agg_lags.py examples/gp_synthetic_example.py src/gluonts/nursery/anomaly_detection/supervised_metrics/_buffered_precision_recall.py src/gluonts/nursery/anomaly_detection/supervised_metrics/utils.py src/gluonts/dataset/repository/__init__.py src/gluonts/mx/block/enc2dec.py src/gluonts/torch/model/predictor.py test/distribution/test_torch_piecewise_linear.py src/gluonts/mx/trainer/__init__.py test/model/rotbaum/test_rotbaum_smoke.py src/gluonts/shell/sagemaker/params.py test/representation/test_rep.py test/representation/test_lab.py src/gluonts/model/tft/_layers.py test/distribution/test_default_quantile_method.py src/gluonts/mx/distribution/multivariate_gaussian.py src/gluonts/model/transformer/trans_encoder.py test/time_feature/test_holiday.py src/gluonts/dataset/__init__.py src/gluonts/model/canonical/_estimator.py src/gluonts/mx/model/forecast.py src/gluonts/shell/serve/__init__.py test/model/transformer/test_model.py src/gluonts/model/forecast.py src/gluonts/model/forecast_generator.py src/gluonts/torch/modules/__init__.py test/dataset/test_rolling.py test/trainer/test_model_iteration_averaging.py src/gluonts/model/transformer/_network.py src/gluonts/nursery/sagemaker_sdk/entry_point_scripts/train_entry_point.py src/gluonts/core/exception.py test/time_feature/test_features.py test/model/deepar/__init__.py src/gluonts/model/rotbaum/_estimator.py src/gluonts/dataset/multivariate_grouper.py src/gluonts/evaluation/backtest.py test/model/tft/test_model.py src/gluonts/mx/distribution/transformed_distribution_output.py src/gluonts/nursery/autogluon_tabular/quantile_example.py src/gluonts/mx/representation/mean_scaling.py src/gluonts/model/prophet/_predictor.py src/gluonts/transform/__init__.py src/gluonts/model/gpvar/_network.py test/dataset/test_train_test_data_leakage.py test/model/deepstate/test_deepstate_smoke.py src/gluonts/transform/split.py test/model/transformer/__init__.py src/gluonts/dataset/util.py src/gluonts/time_feature/__init__.py src/gluonts/core/serde/pd.py test/trainer/test_callbacks.py src/gluonts/mx/kernels/_periodic_kernel.py src/gluonts/model/seq2seq/_forking_estimator.py test/model/seq2seq/test_model.py src/gluonts/time_feature/_base.py docs/conf.py test/test_json.py test/dataset/artificial/test_recipe.py src/gluonts/model/tpp/deeptpp/_estimator.py src/gluonts/nursery/glide/pipeline.py src/gluonts/mx/block/scaler.py src/gluonts/mx/distribution/student_t.py test/model/tpp/__init__.py src/gluonts/model/lstnet/_estimator.py src/gluonts/dataset/repository/_tsf_datasets.py src/gluonts/model/npts/_model.py src/gluonts/model/san/__init__.py src/gluonts/mx/trainer/learning_rate_scheduler.py test/model/naive_predictors/test_r_code_compliance_of_naive_2.py src/gluonts/torch/batchify.py src/gluonts/mx/activation.py src/gluonts/model/renewal/_estimator.py src/gluonts/model/transformer/__init__.py src/gluonts/model/trivial/__init__.py src/gluonts/shell/util.py test/distribution/test_distribution_slice.py test/model/naive_predictors/test_predictors.py src/gluonts/core/serde/_repr.py src/gluonts/model/simple_feedforward/__init__.py src/gluonts/mx/block/__init__.py src/gluonts/mx/representation/__init__.py src/gluonts/nursery/spliced_binned_pareto/spliced_binned_pareto.py test/model/tpp/test_deeptpp.py src/gluonts/torch/modules/feature.py src/gluonts/torch/model/__init__.py test/model/n_beats/__init__.py src/gluonts/model/r_forecast/_predictor.py src/gluonts/model/renewal/__init__.py src/gluonts/model/trivial/mean.py test/model/conftest.py src/gluonts/shell/env.py src/gluonts/mx/distribution/uniform.py src/gluonts/model/deepvar/_network.py src/gluonts/torch/model/deepar/__init__.py src/gluonts/nursery/anomaly_detection/supervised_metrics/__init__.py src/gluonts/model/n_beats/__init__.py src/gluonts/dataset/repository/_artificial.py src/gluonts/shell/sagemaker/__init__.py src/gluonts/model/n_beats/_network.py src/gluonts/torch/modules/scaler.py src/gluonts/core/__init__.py src/gluonts/nursery/glide/__init__.py src/gluonts/core/serde/_parse.py src/gluonts/mx/distribution/__init__.py src/gluonts/dataset/repository/_gp_copula_2019.py src/gluonts/mx/prelude.py src/gluonts/model/wavenet/__init__.py src/gluonts/dataset/repository/_m4.py src/gluonts/transform/field.py src/gluonts/mx/distribution/gaussian.py test/nursery/anomaly_detection/supervised_metrics/test_precision_recall.py src/gluonts/model/rotbaum/_predictor.py test/model/rotbaum/test_model.py src/gluonts/dataset/repository/_m5.py src/gluonts/model/lstnet/_network.py src/gluonts/nursery/sagemaker_sdk/__init__.py src/gluonts/exceptions.py test/model/deepvar_hierarchical/test_reconciliation_error.py src/gluonts/model/canonical/__init__.py src/gluonts/model/rotbaum/_model.py test/distribution/test_lds.py src/gluonts/mx/distribution/gamma.py src/gluonts/mx/block/rnn.py src/gluonts/nursery/autogluon_tabular/predictor.py src/gluonts/shell/exceptions.py test/model/wavenet/test_model.py src/gluonts/mx/component.py test/kernels/test_periodic_kernel.py src/gluonts/support/pandas.py src/gluonts/mx/block/regularization.py src/gluonts/dataset/field_names.py test/test_forecaster_entrypoints.py src/gluonts/mx/linalg_util.py src/gluonts/core/_base.py src/gluonts/model/rotbaum/_preprocess.py src/gluonts/evaluation/_base.py src/gluonts/itertools.py src/gluonts/torch/model/deepar/estimator.py test/model/tft/__init__.py src/gluonts/dataset/repository/_lstnet.py src/gluonts/model/tft/_estimator.py src/gluonts/model/n_beats/_ensemble.py src/gluonts/mx/distribution/lowrank_multivariate_gaussian.py src/gluonts/shell/sagemaker/serve.py test/torch/model/test_deepar.py src/gluonts/mx/distribution/mixture.py test/paper_examples/test_axiv_paper_examples.py src/gluonts/model/common.py src/gluonts/mx/model/__init__.py test/torch/model/test_deepar_modules.py src/gluonts/transform/sampler.py src/gluonts/nursery/sagemaker_sdk/utils.py src/gluonts/model/npts/__init__.py src/gluonts/core/ty.py src/gluonts/transform/_base.py test/model/canonical/test_model.py test/representation/test_grb.py test/model/simple_feedforward/__init__.py test/model/canonical/__init__.py src/gluonts/model/seasonal_naive/__init__.py test/model/deep_factor/test_model.py test/shell/test_shell.py src/gluonts/mx/representation/embedding.py src/gluonts/mx/representation/binning_helpers.py test/test_sanity.py setup.py src/gluonts/model/deepstate/_estimator.py src/gluonts/dataset/artificial/recipe.py test/block/test_feature.py test/model/r_forecast/test_r_predictor.py test/model/gp_forecaster/test_model.py src/gluonts/model/tpp/__init__.py src/gluonts/model/rotbaum/__init__.py src/gluonts/model/deepar/__init__.py examples/evaluate_model.py src/gluonts/evaluation/__init__.py src/gluonts/model/tpp/distribution/base.py src/gluonts/model/seq2seq/_transform.py src/gluonts/model/seq2seq/_seq2seq_network.py test/dataset/test_variable_length.py test/dataset/split/test_split.py test/support/test_jitter.py src/gluonts/mx/distribution/dirichlet_multinomial.py test/dataset/test_common.py src/gluonts/model/npts/_weighted_sampler.py src/gluonts/model/gpvar/_estimator.py src/gluonts/model/prophet/__init__.py test/distribution/test_label_smoothing.py test/core/test_serde_flat.py test/model/lstnet/test_lstnet.py src/gluonts/model/wavenet/_estimator.py src/gluonts/transform/convert.py test/evaluation/test_evaluator.py src/gluonts/model/seasonal_naive/_estimator.py src/gluonts/torch/prelude.py test/model/npts/test_model.py src/gluonts/nursery/anomaly_detection/supervised_metrics/_segment_precision_recall.py test/nursery/sagemaker_sdk/test_entry_point_scripts.py test/trainer/test_model_averaging.py test/dataset/artificial/test_complex_seasonal.py test/model/seq2seq/__init__.py src/gluonts/dataset/repository/_util.py src/gluonts/model/gp_forecaster/gaussian_process.py test/representation/test_hyb.py src/gluonts/testutil/shell.py src/gluonts/model/renewal/_network.py test/time_feature/test_lag.py 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tensor_to_numpy from_hyperparameters skip_encoding DType equals_ndarray equals_list BaseValidatedInitializerModel equals equals_dict equals_default_impl validated let _Config _ScopedSettings inject Settings Dependency checked get_param_type get_param_default fqname_for join decode get_args nest _asdict _translate clone encode _encode encode_np_inexact encode_np_dtype encode_np_integer encode_np_ndarray encode_pd_timestamp encode_from_state decode StatelessMeta encode_path Kind Stateless encode_partial Stateful encode encode_pydantic_model dump_json load_json parse_tuple parse_unary_op parse_set parse parse_num parse_attribute parse_constant parse_str parse_name parse_expr parse_dict parse_expr_call parse_keyword parse_name_constant parse_list as_repr dump_code as_repr_list as_repr_dict as_repr_str as_repr_float load_code BasicFeatureInfo ProcessDataEntry serialize_data_entry FileDataset MetaData Timestamp load_datasets TrainDatasets TimeZoneStrategy ProcessTimeSeriesField SourceContext 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DeepStatePredictionNetwork FourierDateFeatures time_features_from_frequency_str get_lags_for_frequency DeepVAREstimator DeepVARPredictionNetwork DeepVARNetwork DeepVARTrainingNetwork make_rnn_cell constraint_mat null_space_projection_mat DeepVARHierarchicalEstimator DeepVARHierarchicalNetwork reconcile_samples reconciliation_error DeepVARHierarchicalTrainingNetwork DeepVARHierarchicalPredictionNetwork RNNModel DeepFactorEstimator DeepFactorPredictionNetwork DeepFactorTrainingNetwork DeepFactorNetworkBase GPVAREstimator GPVARTrainingNetwork GPVARNetwork GPVARPredictionNetwork GaussianProcess GaussianProcessEstimator GaussianProcessPredictionNetwork GaussianProcessTrainingNetwork GaussianProcessNetworkBase LSTNetEstimator LSTNetPredict LSTNetBase LSTNetTrain naive_2 seasonality_test Naive2Predictor NPTSEstimator NPTS NPTSPredictor KernelType WeightedSampler NBEATSEnsembleEstimator NBEATSEnsemblePredictor NBEATSEstimator NBEATSGenericBlock linear_space NBEATSBlock seasonality_model NBEATSNetwork trend_model NBEATSTrainingNetwork NBEATSSeasonalBlock NBEATSTrendBlock NBEATSPredictionNetwork ProphetPredictor feat_name ProphetDataEntry DeepRenewalProcessEstimator DeepRenewalTrainingNetwork DeepRenewalNetwork DeepRenewalPredictionNetwork DeepRenewalProcessSampleOutputTransform DeepRenewalProcessPredictor AddAxisLength ThirdPartyEstimator TreeEstimator QRX QuantileReg QRF RotbaumForecast TreePredictor CardinalityLabel PreprocessGeneric PreprocessOnlyLagFeatures RForecastPredictor SelfAttentionEstimator CausalConv1D PosFFN SinusoidalPositionalEmbedding SelfAttention _torch_gather SelfAttentionPredictionNetwork SelfAttentionBlock SelfAttentionNetwork SelfAttentionTrainingNetwork SeasonalNaiveEstimator SeasonalNaivePredictor ForkingSeq2SeqEstimator ForkingSeq2SeqDistributionPredictionNetwork ForkingSeq2SeqNetworkBase ForkingSeq2SeqTrainingNetwork ForkingSeq2SeqPredictionNetwork MQRNNEstimator MQCNNEstimator Seq2SeqEstimator MLP2QRForecaster CNN2QRForecaster RNN2QRForecaster Seq2SeqTrainingNetwork Seq2SeqNetworkBase Seq2SeqPredictionNetwork ForkingSequenceSplitter SimpleFeedForwardEstimator SimpleFeedForwardNetworkBase SimpleFeedForwardSamplingNetwork SimpleFeedForwardTrainingNetwork SimpleFeedForwardDistributionNetwork TemporalFusionTransformerEstimator _default_feat_args GatedResidualNetwork VariableSelectionNetwork SelfAttention GatedLinearUnit TemporalFusionDecoder TemporalFusionEncoder FeatureProjector TemporalFusionTransformerNetwork TemporalFusionTransformerTrainingNetwork FeatureEmbedder TemporalFusionTransformerPredictionNetwork TFTInstanceSplitter BroadcastTo PointProcessSampleForecast PointProcessForecastGenerator PointProcessGluonPredictor DeepTPPEstimator DeepTPPTrainingNetwork DeepTPPPredictionNetwork DeepTPPNetworkBase TPPDistributionOutput TPPTransformedDistribution TPPDistribution LoglogisticOutput Loglogistic Weibull WeibullOutput dot_attention LayerNormalization MultiHeadAttention MultiHeadAttentionBase TransformerFeedForward TransformerProcessBlock InputLayer split_heads combine_heads MultiHeadSelfAttention TransformerDecoder TransformerEncoder TransformerEstimator TransformerPredictionNetwork TransformerTrainingNetwork TransformerNetwork ConstantValuePredictor ConstantPredictor IdentityPredictor MovingAveragePredictor MeanPredictor MeanEstimator MeanEstimator ConstantEstimator IdentityEstimator MovingAverageEstimator QuantizeScaled WaveNetEstimator CausalDilatedResidue WaveNetSampler WaveNetTraining LookupValues WaveNet conv1d LipSwish deriv_elu get_activation deriv_swish get_activation_deriv deriv_tanh deriv_softrelu deriv_lipswish _is_stackable _pad_arrays stack as_in_context batchify skip_encoding_mx_gluon_parameterdict equals_representable_block equals_parameter_dict _ num_gpus check_gpu_support get_mxnet_context MXContext jitter_cholesky lower_triangular_ones jitter_cholesky_eig batch_diagonal encode_mx_ndarray encode_mx_context _broadcast_param copy_parameters cumsum import_symb_block hybrid_block_to_symbol_block export_repr_block assert_shape import_repr_block weighted_average export_symb_block get_hybrid_forward_input_names HybridContext mx_switch make_nd_diag DilatedCausalGated ResidualSequential CausalConv1D _get_int Seq2SeqDecoder ForkingMLPDecoder OneShotDecoder RNNZoneoutCell VariationalZoneoutCell Seq2SeqEnc2Dec FutureFeatIntegratorEnc2Dec PassThroughEnc2Dec Seq2SeqEncoder HierarchicalCausalConv1DEncoder RNNEncoder RNNCovariateEncoder MLPEncoder FeatureEmbedder FeatureAssembler MLP QuantileLoss QuantileOutput ProjectParams ActivationRegularizationLoss TemporalActivationRegularizationLoss RNN MeanScaler NOPScaler MinMax Scaler SNDense jacobian_sn_mlp_block_bf SNMLPBlock BetaOutput Beta _Exp Bijection _Log ComposedBijection _Softrelu InverseBijection BijectionHybridBlock ComposedBijectionHybridBlock AffineTransformation BijectionOutput BinnedOutput BinnedArgs Binned InverseBoxCoxTransformOutput BoxCoxTransformOutput BoxCoxTransform InverseBoxCoxTransform CategoricalOutput Categorical DeterministicArgProj DeterministicOutput Deterministic DirichletOutput Dirichlet DirichletMultinomialOutput DirichletMultinomial _expand_param _sample_multiple softplus Distribution getF _index_tensor nans_like Output DistributionOutput ArgProj EmpiricalDistribution EmpiricalDistributionOutput GammaOutput Gamma Gaussian GaussianOutput GenParetoOutput GenPareto ZeroAndOneInflatedBeta OneInflatedBetaOutput ZeroInflatedBeta ZeroAndOneInflatedBetaOutput OneInflatedBeta ZeroInflatedBetaOutput iresnet InvertibleResnetHybridBlock log_abs_det LaplaceFixedVarianceOutput LaplaceOutput Laplace _safe_split kalman_filter_step LDSArgsProj ParameterBounds LDS LogitNormalOutput LogitNormal LowrankGPOutput GPArgProj LowrankMultivariateGaussianOutput LowrankMultivariateGaussian lowrank_log_likelihood inv_softplus capacitance_tril log_det mahalanobis_distance MixtureDistributionOutput MixtureDistribution MixtureArgs MultivariateGaussianOutput 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test_ctsplitter_train_short_intervals test_CanonicalInstanceSplitter test_Transformation test_AddTimeFeatures test_cdf_to_gaussian_transformation test_ctsplitter_mask_sorted test_ctsplitter_train_correct assert_serializable MockContinuousTimeSampler test_InstanceSplitter point_process_dataset assert_shape test_ctsplitter_train_samples_correct_times test_add_method test_gaussian_cdf test_gaussian_ppf test_multi_dim_transformation test_AddTimeFeatures_empty_time_features test_target_dim_indicator test_align_timestamp test_ctsplitter_no_train_last_point test_BucketInstanceSampler test_activation_deriv test_feature_assembler test_feature_embedder test_TemporalActivationRegularizationLoss test_ActivationRegularizationLoss test_scaler test_minmaxscaler test_nopscaler Complex test_dynamic_loading Baz test_to_code test Foo test_component_ctor Bar check_equality Span test_serde_fq test_ndarray_serialization test_timestamp_encode_decode test_string_escape test_serde_method MyGluonBlock test_serde_partial CategoricalFeatureInfo test_json_serialization X test_code_serialization BestEpochInfo test_nested_params test_inject MySettings test_functional Args test_let test_declarative test_partial_assignment test_get StatelessClassTyped test_ty_varargs test_checked StatefulClass test_stateless varargs test_stateful test_stateless_immutable foo test_stateless_typed test_checked_invalid StatelessClass test_dataset_instance test_process_start_field load_file_dataset_cached load_parsed_dataset test_io_speed load_json_lines_file load_list_dataset load_file_dataset_numpy test_loader_multivariate baseline load_json load_file_dataset test_validation_data_loader DefaultListDataset count_item_ids test_inference_data_loader ExactlyOneSampler WriteIsTrain test_training_data_loader default_transformation DefaultFileDataset test_dataset_fields test_jsonl test_jsonlgz test_multivariate_grouper_train test_multivariate_grouper_test create_dynamic_dataset test_step_strategy generate_dataset test_invalid_rolling_parameters test_dynamic_integration test_dynamic_features DatasetStatisticsExceptions ts make_dummy_dynamic_feat make_time_series DatasetStatisticsTest check_train_test_split test_data_leakage test_frequency_converter test_pad_arrays_axis test_inference_loader_shapes_small_batch test_inference_loader_short_intervals test_train_loader_short_intervals test_train_loader_length loader_factory test_inference_loader_shapes test_train_loader_shapes pp_dataset test_variable_length_stack test_pad_arrays_pad_left test_variable_length_stack_zerosize test_complex_seasonal test_generate test_lifted_decorator test_call_and_repr test_recipe_dataset test_functional test_two test_length test_arp make_series test_splitter test_ts_slice_to_item test_quantile test_inverse_quantile test_stdevs test_cdf test_means test_variances test_quantile test_distribution_output_serde test_distribution_output_variance test_deterministic_output test_distribution_output_shapes test_distribution_output_mean test_inflated_beta_sampling test_multivariate_sampling test_sampling test_piecewise_linear_sampling test_distribution_shapes test_slice_axis_results SliceHelper test_distr_slice_axis test_jacobian test_flow_shapes allclose jacobian_autograd test_flow_invertibility test_symbol_and_array test_issue_287 test_smooth_mask_adds_to_one test_get_smooth_mask_correct labels test_output_sets_alpha test_loss_correct assert_shape_and_finite test_lds_likelihood test_mixture_inference fit_mixture_distribution plot_samples test_mixture_output test_mixture_logprob histogram test_mixture test_inference_mixture_different_families diff test_binned_likelihood maximum_likelihood_estimate_sgd test_deterministic_l2 test_laplace test_beta_likelihood test_categorical_likelihood test_dirichlet test_loglogistic_likelihood test_dirichlet_multinomial test_inflated_beta_likelihood test_gamma_likelihood test_inflated_poisson_likelihood test_weibull_likelihood inv_softplus test_logit_normal_likelihood test_empirical_distribution test_neg_binomial test_poisson_likelihood test_studentT_likelihood test_lowrank_multivariate_gaussian test_piecewise_linear test_deterministic_l1 test_box_cox_tranform test_gaussian_likelihood test_multivariate_gaussian test_genpareto_likelihood test_inflated_neg_binomial_likelihood test_nanmixture_categorical_inference test_nanmixture_gaussian_inference test_nan_mixture test_nanmixture_output diff test_values test_fkpwl_distr_output_same_as_pl test_shapes test_simple_symmetric test_fkpwl_distr_args_correct test_robustness test_values test_shapes test_simple_symmetric test_robustness empirical_cdf test_transformed_distribution exp_quantile exp_cdf iterable test_metrics test_metrics_mp naive_multivariate_forecaster naive_forecaster rmsle test_MASE_sMAPE_M4 test_custom_eval_fn data_iterator test_metrics_multivariate fcst_iterator iterator test_evaluation_with_QuantileForecast test_metrics_multivariate_custom_eval_fn calculate_metrics test_metrics test_seasonal_error test_msis test_target_metrics test_periodic_kernel test_periodic_kernel_compute test_radial_basis_function_kernel from_hyperparameters accuracy_test dsinfo serialize_test repr_test pytest_runtest_setup AttrDict test_benchmark make_estimator test_forecast_parser test_forecast_multivariate test_DistributionForecast test_Forecast test_item_id_info test_parallelized_predictor test_localizer test_repr test_serialize Estimator hyperparameters test_accuracy test_distribution test_lagged_subsequences test_deepar_smoke hyperparameters test_repr test_serialize test_accuracy test_deepstate_smoke test_deepstate_exceptions_with_feat_static_cat test_seasonality_issm_h test_zero_feature test_composite_issm_h test_composite_issm_h_default test_level_issm_h test_level_trend_issm_h hyperparameters test_repr test_serialize test_accuracy test_deepvar load_multivariate_constant_dataset sine7 HierarchicalTrainDatasets HierarchicalMetaData test_deepvar_hierarchical test_reconciliation_error test_reconciliation_error hyperparameters test_repr test_serialize test_accuracy test_smoke test_gpvar_proj load_multivariate_constant_dataset test_gp_output load_xfull load_gp_params load_exact_std load_ytrain load_exact_mean relative_error test_inference hyperparameters test_repr test_serialize test_accuracy test_lstnet load_multivariate_constant_dataset seasonal_naive_predictor naive_2_predictor generate_random_dataset test_seriali_predictors test_predictor test_accuracy load_naive_2_data test_naive_2 test_repr test_serialize test_accuracy _inject_nans_in_target get_test_data test_weighted_sampler test_npts_forecaster _test_nans_in_target test_climatological_forecaster test_npts_custom_features test_repr test_serialize args name estimator_config test_accuracy test_prophet_serialization test_feat_dynamic_real_success test_feat_dynamic_real_bad_size test_min_obs_error hyperparameters test_repr test_accuracy_smoke_test test_serialize test_output_transform hyperparameters test_repr test_serialize test_accuracy test_rotbaum_smoke test_r_predictor_serialization test_forecasts test_causal_conv_1d compute_causalconv1d test_hierarchical_cnn_encoders make_dataset test_forking_sequence_with_features test_forking_sequence_splitter test_inference_quantile_prediction test_is_iqf test_repr test_mqcnn_covariate_smoke_test test_serialize Estimator test_feat_static_cat_smoke_test hyperparameters test_backwards_compatibility test_mqcnn_scaling_smoke_test test_accuracy test_infer_quantile_forecast test_compute_quantile_weights test_compute_quantile_loss hyperparameters test_repr test_serialize test_accuracy hyperparameters test_repr test_serialize test_accuracy point_process_dataset_2 point_process_dataset test_trainining_network_disallows_hybrid test_prediction_network_output test_prediction_network_disallows_hybrid _allclose test_log_likelihood MockTPPPredictionNet predictor_factory test_tpp_pred_dataset_2_shapes_ok hyperparameters test_repr test_serialize test_accuracy get_predictions testing hyperparameters test_repr test_serialize test_accuracy test_tabular_estimator test_get_features_dataframe test_n_k_filter_defaults_custom test_labels_filter test_forward_fill test_n_k_filter_defaults test_bounded_pr_auc labels_and_scores test_labels_to_ranges test_buffered_precision_recall test_segment_precision_recall test_aggregate_precision_recall_curve test_range_overlap test_extend_ranges test_train_script create_arguments simple_feedforward_estimator test_listing_1 test_appendix_c test_binning test_gr_binning test_hyb test_la_binning test_mean test_rep test_nested_params batch_transform test_train_shell test_server_shell dynamic_server test_listify_dataset static_server test_dynamic_shell test_dynamic_batch_shell test_as_json_dict_outputs_valid_json train_env test_jitter_synthetic_gp test_jitter_unit test_mx_switch test_weighted_average test_linear_interpolation test_cumsum test_erf test_symb_block_export_import_nested_array test_erfinv test_exponential_left_tail_approximation sym_block_import_export_test_cases test_exponential_right_tail_approximation test_symb_block_import_backward_compatible test_agg_lags test_feature_unnormalized_bounds test_feature_normalized_bounds test_special_date_feature_set_daily_squared_exponential test_special_date_feature_set_daily test_special_date_feature_set_hourly test_holidays test_lags test_get_seasonality test_torch_deepar test_torch_deepar_with_features test_lagged_lstm test_deepar_modules test_simple_model mean_abs_scaling FeedForwardNetwork LightningFeedForwardNetwork test_forecast_multivariate test_DistributionForecast test_Forecast RandomNetwork test_pytorch_predictor_serde maximum_likelihood_estimate_sgd test_neg_binomial test_gamma_likelihood test_poisson test_normal_likelihood inv_softplus test_studentT_likelihood test_beta_likelihood test_callbacks test_callbacklist test_PatientMetricAttentiveScheduler test_model_averaging test_Alpha_Suffix initialize_model test_NTA_V1 test_epochs test_learning_rate test_learning_rate_decay_factor test_patience assert_invalid_param assert_valid_param seed int format print getenv warning randint max when setattr get_result seed get_closest_marker int get_state format debug getenv set_state info randint max gluon exec add_javascript CardDirective add_transform add_config_value add_directive parent Path makedirs glob print make_evaluation_predictions estimator test get_dataset pprint train __name__ ones_like kernel_matrix plot ones float64 reshape RBFKernel squeeze GaussianProcess exact_inference sqrt tile sin zeros data list set_data zip collect_params values assert_gluonts update copy warn parent exists split GitRepo rsplit describe dist_root lstrip append getattr __init__ getattr zip list keys parameters any create_model get join list items enumerate Trie items list pop _translate map append count pop valmap get_args isinstance _translate keymap nest _encode base_encode encode update hasattr isinstance __getnewargs_ex__ cast fqname_for args get update locate cast __new__ operand parse_expr list keys map values Name value isinstance list map dict keywords args isinstance join map isfinite get join list map chain parse_file Path FileDataset warning dump BytesIO map set_worker_info put PseudoShuffled apply Cyclic MultiProcessLoader iter truncate_features concatenate get_windows freq copy to_pandas append to_numpy len ScaleHistogram assert_data_error worker_id int num_workers append fn walk _list_files info _split freqstr date_range list keys asarray float64 astype zeros range len writer int Timestamp writerow len append date range enumerate dirname makedirs freq test generate_sf2 dirname train makedirs val_or_callable callable isinstance seed evaluate isinstance recipe count items list isinstance dict cast resolve callable append enumerate any array isinstance isinstance ARp exp max min RecipeDataset dataset_info test generate train ListDataset MetaData DatasetInfo rmtree mkdir dataset_recipe info exists materialize_dataset generate_sf2 mkdir generate parent makedirs save_metadata clean_up_dataset download_dataset save_dataset urlretrieve url rmtree list FileDataset save_to_file rmtree remove parent iterrows set_index Series transpose append sum head values frequency_add load_from_pandas prediction_length url index metadata date_range save_to_file append to_dict read_csv enumerate makedirs str list load_datasets test tqdm match zip train check_dataset rollforward warn read_excel save_to_file max values list iterrows Timestamp apply sheet_name append asarray truncate_trailing_nan unique makedirs to_dict len read_csv save_to_file makedirs T concatenate copy apply drop unique save_to_file read_csv values len mkdir frequency_converter int hasattr forecast_horizon save_datasets default_prediction_length_from_frequency save_metadata mkdir frequency len name split isnumeric print dirname makedirs lead_time freq prediction_length apply info evaluator setFormatter list items calculate_dataset_statistics getLogger addHandler serialize_message make_evaluation_predictions Formatter removeHandler FileHandler get_seasonality freq mean masked_invalid apply add info get list put deserialize predict range _make_2_block_diagonal getF len swapaxes concat broadcast_add zeros_like to_offset to_offset name norm_freq_str to_offset list sorted set HybridSequentialRNNCell RnnCell range add shape hstack list reshape shape moveaxis range len abs asnumpy where cumsum sqrt acf len ones mean seasonal seasonality_test len linear_space stack concat linear_space stack Block broadcast_like arange_like stack append expand_dims range cast reshape transpose broadcast_add batch_dot softmax reshape transpose sqrt Uniform Swish _act_type isinstance ELU Activation exp sigmoid ndarray isinstance set reduce max NDArray ndarray isinstance _pad_arrays array list keys asnumpy zip num_gpus info num_gpus info eye range zeros_like ones_like batch_diagonal broadcast_mul expand_dims syevd broadcast_add batch_diagonal zeros_like broadcast_mul eye expand_dims potrf shape NDArray isinstance zip parameters export imports exists str save_parameters str load_parameters expand_dims linalg_gemm2 ones_like linalg_trmm sum maximum where broadcast_axes zip where isinstance _broadcast_param ndarray isinstance gemm2 weight broadcast_axes enumerate Symbol NDArray isinstance isinstance squeeze slice_axis append enumerate len sample_func gemm2 getF potrf linalg_gemm2 linalg_trsm squeeze linalg_potrf eye expand_dims broadcast_sub log_prob make_nd_diag broadcast_div ones_like linalg_gemm2 broadcast_mul eye expand_dims sum sumlogdiag broadcast_div linalg_gemm2 squeeze expand_dims sum getF pi capacitance_tril mahalanobis_distance log_det sum max log sum range Block SymbolBlock HybridBlock range len inf concatenate accumulate array arange where append fill_forward append len range fill_forward list argmax auc zip append enumerate array labels_to_ranges_numba list min start stop append max range enumerate extend_ranges any sum labels_to_ranges singleton_precision_recall partial_filter precision_recall_fn zeros array enumerate len list concatenate unique zip sum precision_recall_fn labels_to_ranges labels_to_ranges max range_overlap array serialize show list plot deserialize islice get_dataset to_pandas Path figure zip TabularEstimator train predict makedirs mean max print list tqdm L2Loss set_postfix step range num_time_series int subplot arange subplots plot xlabel min ylabel legend range asnumpy seed rand fill_diagonal list into_batches map put fn divmod range append divide_into len reshape ravel asnumpy polyfit time lamb append max thinning_sampler strftime info evaluator items output_data_dir serialize load_datasets make_evaluation_predictions Evaluator estimator get_dataset model_dir Path s3_dataset info dataset rvs int ones_like arange choice len add_spikes pi linspace sin standard_t normal add_spikes rvs int arange choice append len minimum add_spikes_asymmetric maximum pi linspace sin standard_t backward step mean distr_tcn float numpy arange train_step_from_batch setdiff1d backward tensor float transpose Unfold choice append icdf distr_tcn step range minimum str list arange plot abs mean title array figure legend fill_between ravel keys len format info __version__ fqname_for info log_version join serialize isinstance model hyperparameters from_inputs datasets info run_train run_test evaluator items list make_evaluation_predictions Evaluator apply warning info maybe_len len load guard iter_entry_points signature __contains__ arguments keyfilter make_gunicorn_app run info forecaster_type_by_name run_train_and_test TrainEnv Path _push info split_key list items compile split_by_prefix strip detrim_sagemaker_parameters map_dct_values join list items batcher enumerate pop copy count info zip sum enumerate len Flask ThrougputIter log_throughput freq timings ListDataset predict fn put start Process Queue get time as_json_dict append predict gluonts_batch_suppress_errors predictor_factory batch_inference_invocations inference_invocations get_base_app isnan isposinf isneginf isinstance from_hyperparameters fqname_for model Application __version__ make_app info deserialize type ndim ones_like exp zeros_like square abs sqrt zip zeros_like where append randint range copy make_gunicorn_app Process time join get_context start ServerFacade terminate cast sleep mkdtemp range len max cumsum reshape min linspace histogram append prod range len _make_lags_for_minute name _make_lags_for_month norm_freq_str _make_lags_for_hour _make_lags_for_day _make_lags_for_week n to_offset get name divmod norm_freq_str warning n items list isinstance tensor load_state_dict state_dict parameters clamp zeros_like tensor_to_numpy standard_gaussian_cdf range _empirical_cdf_inverse_transform deepcopy ndarray isinstance zip transform load iter_entry_points islice list Cyclic PseudoShuffled list iter list dump seek StringIO bdump BytesIO seek BytesIO StringIO range AddTimeFeatures AddAgeFeature AddTimeFeatures map_transform date_range assert_serializable len AddTimeFeatures map_transform assert_serializable assert_serializable map_transform AddAgeFeature len list arange flatmap_transform InstanceSplitter assert_serializable len list arange assert_serializable flatmap_transform CanonicalInstanceSplitter len print t ListDataset iter assert_serializable Chain assert_padded_array assert_shape t ListDataset array nan iter assert_serializable Chain add make_dataset t ScaleHistogram iter assert_serializable Chain range calculate_dataset_statistics bin_counts add make_dataset t ScaleHistogram iter assert_serializable Chain range make_test_data make_fake_output cdf_to_gaussian_forward_transform t array iter expand_dims Chain cdf tolist array standard_gaussian_cdf ppf standard_gaussian_ppf linspace tolist t ListDataset Chain array ListDataset array cumsum ContinuousTimeInstanceSplitter iter next array _mask_sorted iter splitter next ContinuousTimeInstanceSplitter list splitter ContinuousTimeInstanceSplitter list splitter ContinuousTimeInstanceSplitter shuffle_iterator splitter ContinuousTimeInstanceSplitter splitter ContinuousTimeInstanceSplitter transform AddObservedValuesIndicator enumerate ones ListDataset range dump_json dump_code __class__ load_json fqname_for load_code int sum array roll CountTrailingZeros ListDataset transform next array ListDataset transform next array drop_empty initialize ndarray randn get_deriv_autograd act_deriv get_activation get_activation_deriv initialize test_forward_pass FeatureEmbedder test_parameter_names hybridize test_parameters_length One initialize test_forward_pass FeatureAssembler from_iterable test_parameter_names hybridize test_parameters_length One ar ActivationRegularizationLoss tar TemporalActivationRegularizationLoss broadcast_div expand_dims keepdims s NOPScaler s s seed dump_json dump_code x_list Baz compare_tpes x_dict input_fields compare_vals Bar load_json load_code len load_code dedent Complex isinstance inexact issubdtype integer type serialize_fn now encode decode encode decode partial encode decode m X clone DeepAREstimator Settings _declare encode decode StatefulClass encode decode StatelessClass StatelessClass StatelessClassTyped varargs process find_files is_valid open find_files is_valid open find_files is_valid array FileDataset Timestamp print MultivariateGrouper ListDataset MultivariateGrouper ListDataset grouper zip ListDataset raises constant_dataset raises generate_rolling_dataset zip generate_rolling_dataset generate_rolling_dataset create_dynamic_dataset make_evaluation_predictions train DeepAREstimator generate_rolling_dataset prediction_length freq test train len check_train_test_split get_dataset next pp_dataset loader_factory iter list pp_dataset loader_factory iter list loader_factory iter list loader_factory iter list loader_factory iter list loader_factory iter stack stack _pad_arrays _pad_arrays seed dump_code evaluate print dict func func_reconstructed load_code assert_allclose list RecipeDataset test generate train array take_as_list Timestamp Two evaluate SmoothSeasonality list evaluate RandomGaussian dict keys evaluate uniform Length something something_else RandomGaussian Constant array Length normalized_ar1 uniform evaluate date_range to_data_entry TimeSeriesSlice rolling_split DateSplitter prediction_length OffsetSplitter get_dataset train range split max broadcast_like MixtureDistribution Gaussian quantile expand_dims abs array asnumpy cdf MixtureDistribution array quantile mean __name__ serialize_fn distr_class stddev __name__ serialize_fn distr_class __name__ variance serialize_fn distr_class __name__ serialize_fn distr_class cdf __name__ serialize_fn distr_class encode decode loss initialize product distribution sample args_proj get_args_proj initialize product distribution args_proj get_args_proj initialize product distribution args_proj get_args_proj initialize ones distribution DeterministicOutput sample args_proj get_args_proj percentile float64 serialize_fn astype distr_class linspace sample empirical_cdf std asnumpy sample distr serialize_fn asnumpy sample distr serialize_fn ZeroAndOneInflatedBeta choice beta sample sample array loss quantile slice_axis uniform asnumpy isinstance initialize bijection_func f_inv randn f initialize bijection_func f_inv f zeros range initialize bijection_func f_inv randn log_abs_det_jac train ListDataset DeepAREstimator Trainer initialize ones distribution log_prob distr_out_class array args_proj get_args_proj arange ones log_softmax _get_mask Binned _smooth_mask nd expand_dims _get_mask Binned _smooth_mask nd expand_dims Binned initialize BinnedOutput hybridize get_args_proj assert_shape_and_finite asnumpy log_prob assert_almost_equal sample LDS sum array emission_coeff sample_marginals len show hist asnumpy broadcast_like MixtureDistribution serialize_fn where mean uniform stack sample empirical_cdf std asnumpy initialize MixtureDistributionOutput ones distribution serialize_fn log_prob sample args_proj get_args_proj asnumpy initialize list backward MixtureDistributionOutput ones distribution step Trainer tqdm hybridize set_postfix histogram collect_params range array args_proj get_args_proj asnumpy initialize list args_proj backward ones distribution Trainer tqdm hybridize set_postfix collect_params step range get_args_proj components fit_mixture_distribution sample enumerate args components fit_mixture_distribution args MixtureDistributionOutput MixtureDistribution Gaussian log_prob enumerate Trainer DataLoader Constant initialize ones collect_params range ArrayDataset proj hybridize as_in_context zip enumerate backward print cpu step array get_args_proj maximum_likelihood_estimate_sgd print Beta BetaOutput sample zeros maximum_likelihood_estimate_sgd ZeroAndOneInflatedBeta ZeroAndOneInflatedBetaOutput OneInflatedBetaOutput ZeroInflatedBeta sample zeros OneInflatedBeta ZeroInflatedBetaOutput StudentTOutput maximum_likelihood_estimate_sgd StudentT sample zeros GammaOutput Gamma maximum_likelihood_estimate_sgd sample zeros maximum_likelihood_estimate_sgd Gaussian GaussianOutput sample zeros maximum_likelihood_estimate_sgd MultivariateGaussianOutput asnumpy arange ones transpose MultivariateGaussian tri dot sample float diag maximum_likelihood_estimate_sgd Dirichlet DirichletOutput sample array asnumpy maximum_likelihood_estimate_sgd DirichletMultinomial sample DirichletMultinomialOutput array asnumpy maximum_likelihood_estimate_sgd LowrankMultivariateGaussianOutput arange LowrankMultivariateGaussian ones transpose dot sqrt eye float array asnumpy maximum_likelihood_estimate_sgd arange LowrankMultivariateGaussian print eye EmpiricalDistributionOutput float asnumpy maximum_likelihood_estimate_sgd Gaussian GaussianFixedVarianceOutput sample zeros maximum_likelihood_estimate_sgd LaplaceFixedVarianceOutput sample zeros Laplace NegativeBinomialOutput maximum_likelihood_estimate_sgd NegativeBinomial sample zeros maximum_likelihood_estimate_sgd LaplaceOutput sample zeros Laplace maximum_likelihood_estimate_sgd PiecewiseLinear arange PiecewiseLinearOutput squeeze zip sample zeros expand_dims array asnumpy enumerate len maximum_likelihood_estimate_sgd TransformedDistributionOutput InverseBoxCoxTransform Gaussian GaussianOutput sample zeros TransformedDistribution InverseBoxCoxTransformOutput maximum_likelihood_estimate_sgd exp Binned BinnedOutput logspace random_uniform sample zeros sum array log maximum_likelihood_estimate_sgd exp CategoricalOutput flatten Categorical random_uniform sample zeros sum array log maximum_likelihood_estimate_sgd PoissonOutput Poisson sample zeros maximum_likelihood_estimate_sgd LogitNormal LogitNormalOutput sample zeros maximum_likelihood_estimate_sgd Loglogistic print LoglogisticOutput sample zeros maximum_likelihood_estimate_sgd Weibull print sample zeros WeibullOutput maximum_likelihood_estimate_sgd GenParetoOutput print sample zeros GenPareto distribution maximum_likelihood_estimate_sgd squeeze ZeroInflatedPoissonOutput distribution ZeroInflatedNegativeBinomialOutput squeeze maximum_likelihood_estimate_sgd attach_grad broadcast_like zeros_like backward prob NanMixture serialize_fn logical_and where log uniform logical_or distr_class sample log_prob asnumpy initialize list NanMixtureOutput backward ones Trainer tqdm GaussianOutput hybridize set_postfix collect_params step array range get_args_proj asnumpy initialize list NanMixtureOutput backward ones args_proj distribution Trainer CategoricalOutput tqdm hybridize set_postfix collect_params step array range get_args_proj initialize NanMixtureOutput ones distribution serialize_fn log_prob sample args_proj get_args_proj empirical_cdf reshape serialize_fn asnumpy ones PiecewiseLinear serialize_fn sample PiecewiseLinear array normal initialize distribution PiecewiseLinearOutput uniform cdf sum args_proj crps get_args_proj distribution PiecewiseLinearOutput array fkpl_proj initialize array get_args_proj numpy Tensor astype float32 from_numpy exp_quantile ones serialize_fn linspace zeros TransformedDistribution Uniform range len range len evaluator Timestamp data_iterator forecaster fcst_iterator date_range nan append input_type ts_datastructure range Series Evaluator calculate_metrics items list Series Evaluator calculate_metrics items list Series Evaluator calculate_metrics items list MultivariateEvaluator DataFrame calculate_metrics Series Evaluator ev date_range iter items list Series Evaluator calculate_metrics items list MultivariateEvaluator DataFrame calculate_metrics assert_almost_equal metric assert_almost_equal metric assert_almost_equal msis assert_almost_equal calculate_seasonal_error ones_like exp kernel_matrix PeriodicKernel reshape pi sqrt tile sin range kernel_matrix PeriodicKernel reshape pi sin zeros range exp kernel_matrix RBFKernel reshape tile range skip constant_dataset default_synthetic backtest_metrics list items prediction_length calculate_dataset_statistics freq Evaluator make_estimator train constant_dataset print text make_from_log_contents shape map quantile DistributionForecast array map quantile train predict zip list IdentityPredictor ParallelizedPredictor ListDataset zip predict backtest_metrics Localizer ListDataset MeanEstimator update accuracy_test repr_test serialize_test train_model prediction_length trained_net distribution islice apply create_training_data_loader get_hybrid_forward_input_names type DeepAREstimator normal list product get_lagged_subsequences range len list train estimator predict list train estimator predict ZeroFeature zf date_range LevelISSM time_features get_issm_coeff stack array time_features slice get_issm_coeff LevelTrendISSM stack array range one_hot time_features slice get_issm_coeff stack SeasonalityISSM array range CompositeISSM one_hot time_features ones slice concat get_issm_coeff stack array range one_hot time_features ones slice concat get_issm_coeff stack eye range array get_from_freq metadata MultivariateGrouper prediction_length constant_dataset backtest_metrics train Estimator seed arange HierarchicalMetaData pi uniform ListDataset date_range sin zeros array enumerate backtest_metrics train DeepVARHierarchicalEstimator reconcile_samples LowrankGPOutput initialize ones proj get_args_proj initialize LowrankMultivariateGaussian ones gp_proj GPArgProj backtest_metrics train GPVAREstimator RBFKernel expand_dims GaussianProcess exact_inference evaluator list make_evaluation_predictions MultivariateEvaluator LSTNetEstimator iter train randint uniform range Timestamp list generate_random_dataset zip predictor_cls predict predictor_cls backtest_metrics predictor_cls values load_naive_2_data append naive_2 array range len date_range sum ones_like inf append get_test_data _test_nans_in_target ListDataset histogram NPTSPredictor assert_almost_equal next range predict len sum list exp inf append arange get_test_data _test_nans_in_target set ListDataset histogram NPTSPredictor assert_almost_equal next range predict len sum list exp inf append arange get_test_data min _test_nans_in_target ListDataset date_range histogram NPTSPredictor assert_almost_equal next range predict len ListDataset predict sort nan copy arange random histogram sample randint assert_almost_equal sum dict skip arange ProphetPredictor dict ListDataset next predict dict ListDataset dict str ListDataset value ProphetPredictor update accuracy_test update update DeepRenewalProcessSampleOutputTransform array tf list from_inputs train predict backtest_metrics RForecastPredictor list prediction_length xfail freq Evaluator get_dataset dict predict RForecastPredictor list zeros_like reversed range enumerate len normal initialize CausalConv1D compute_causalconv1d One asnumpy initialize randn reshape HierarchicalCausalConv1DEncoder hybridize len reshape arange make_dataset array log10 iter next Chain trans make_dataset iter next Chain trans from_hyperparameters list make_dummy_datasets_with_features train predict list make_dummy_datasets_with_features from_inputs train predict list make_dummy_datasets_with_features from_inputs train predict list sorted make_dummy_datasets_with_features from_inputs quantile train predict list make_dummy_datasets_with_features from_inputs train predict list make_dummy_datasets_with_features from_inputs train range predict len ones zeros QuantileLoss enumerate compute_quantile_weights items list QuantileForecast append array ListDataset seed initialize model stack DeepTPPTrainingNetwork seed seed seed initialize DeepTPPPredictionNetwork model stack array asnumpy predictor_factory mean ListDataset iter MovingAveragePredictor next get_predictions assert_equal get_features_dataframe assert_frame_equal TabularEstimator labels_filter array n_k_filter array n_k_filter array segment_precision_recall labels_to_ranges enumerate array list aggregate_precision_recall_curve zip assert_almost_equal auc assert_almost_equal bounded_pr_auc aggregate_precision_recall_curve parse_known_args add_argument ArgumentParser backtest_metrics Evaluator metadata constant_dataset train DeepAREstimator backtest_metrics Evaluator metadata train constant_dataset MyEstimator cpu initialize_from_array array r initialize_from_array r repeat cpu expand_dims asnumpy len initialize_from_array r print cpu range array asnumpy len r asnumpy broadcast_div reshape expand_dims s Representation s pop decode_nested_parameters serialize from_hyperparameters model ServeEnv Settings base ServeEnv Settings base setenv dumps list values record_tuples run_train_and_test mean array ones execution_parameters mean array ones execution_parameters mean array ones execution_parameters dumps jsonify_floats cumsum flip asnumpy insert scipy_erf tolist array erf scipy_erfinv erfinv linspace initialize hybrid_block_to_symbol_block block_type hybridize array my_block initialize block_type hybridize array my_block array array print linear_interpolation LinearInterpolation ExponentialTailApproximation array log ExponentialTailApproximation array log Timestamp add_agg_lags ListDataset iter AddAggregateLags next array feature feature to_datetime sfs SpecialDateFeatureSet sfs date_range SpecialDateFeatureSet array sfs SpecialDateFeatureSet date_range squared_exponential_kernel assert_almost_equal array get_lags_for_frequency islice test get_dataset mean train DeepAREstimator predict islice mean ListDataset train DeepAREstimator predict script cat model model ones _past_length DeepARLightningModule dict unroll_lagged_rnn DeepARModel zeros output_distribution evaluator prediction_length get_predictor make_evaluation_predictions freq Evaluator default_synthetic LightningFeedForwardNetwork Trainer TrainDataLoader InstanceSplitter AddObservedValuesIndicator fit RandomNetwork InstanceSplitter PyTorchPredictor distribution arg_proj zero_grad clip_grad_norm_ SGD bias parameters TensorDataset constant_ maximum_likelihood_estimate_sgd NormalOutput Normal sample zeros maximum_likelihood_estimate_sgd PoissonOutput Poisson sample zeros TrainingHistory TerminateOnNaN callbacks CallbackList prediction_length WarmStart ModelIterationAveraging freq get_dataset TrainingHistory train SimpleFeedForwardEstimator step Adam range MetricAttentiveScheduler select_checkpoints average_arrays isinstance strategy Timestamp ListDataset zeros train SimpleFeedForwardEstimator initialize_model list items load_cached_model update_average_trigger list_data apply NTA collect_params load_averaged_model enumerate initialize_model list items int load_cached_model update_average_trigger list_data len apply ceil collect_params Alpha_Suffix max load_averaged_model enumerate assert_invalid_param assert_valid_param assert_invalid_param assert_valid_param assert_invalid_param assert_valid_param assert_invalid_param assert_valid_param Trainer | # GluonTS - Probabilistic Time Series Modeling in Python [](https://pypi.org/project/gluonts/) [](./LICENSE) [][stable docs url] [][latest docs url] GluonTS is a Python toolkit for probabilistic time series modeling, built around [Apache MXNet (incubating)](https://mxnet.incubator.apache.org/). GluonTS provides utilities for loading and iterating over time series datasets, state of the art models ready to be trained, and building blocks to define your own models and quickly experiment with different solutions. | 2,938 |
mbrossar/denoise-imu-gyro | ['denoising'] | ['Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation'] | src/networks.py src/losses.py src/dataset.py main_TUMVI.py src/lie_algebra.py src/utils.py src/learning.py main_EUROC.py BaseDataset TUMVIDataset EUROCDataset LearningBasedProcessing GyroLearningBasedProcessing CPUSO3 SO3 GyroLoss BaseLoss BaseNet GyroNet bmmt pload ydump bmtm bbmv bmv pdump mkdir yload bmtv float double eye join join join join join | # Denoising IMU Gyroscope with Deep Learning for Open-Loop Attitude Estimation ## Overview [[IEEE paper](https://ieeexplore.ieee.org/document/9119813), [preprint paper](https://hal.archives-ouvertes.fr/hal-02488923v4/document)] This repo contains a learning method for denoising gyroscopes of Inertial Measurement Units (IMUs) using ground truth data. In terms of attitude dead-reckoning estimation, the obtained algorithm is able to beat top-ranked visual-inertial odometry systems [3-5] in terms of attitude estimation although it only uses signals from a low-cost IMU. The obtained performances are achieved thanks to a well chosen model, and a proper loss function for orientation increments. Our approach builds upon a neural network based on dilated convolutions, without requiring any recurrent neural network. | 2,939 |
mcbuehler/Seg2Eye | ['semantic segmentation'] | ['Content-Consistent Generation of Realistic Eyes with Style'] | refinenet/deeplab/modeling/backbone/drn.py refinenet/losses/angular_error.py data/preprocessor.py models/networks/normalization.py refinenet/deeplab/modeling/decoder.py trainers/__init__.py refinenet/deeplab/modeling/backbone/mobilenet.py refinenet/deeplab/dataloaders/datasets/cityscapes.py refinenet/core/gaze.py refinenet/core/__init__.py test.py refinenet/deeplab/modeling/backbone/__init__.py trainers/pix2pix_trainer.py util/visualizer.py refinenet/core/config_default.py models/networks/discriminator.py refinenet/deeplab/utils/summaries.py refinenet/deeplab/dataloaders/__init__.py refinenet/core/checkpoint_manager.py data/__init__.py refinenet/core/gsheet_logger.py models/__init__.py refinenet/deeplab/doc/deeplab_resnet.py refinenet/deeplab/utils/loss.py refinenet/losses/__init__.py refinenet/dataset.py options/base_options.py util/tester.py models/networks/generator.py data/postprocessor.py refinenet/losses/gaze_mse_error.py refinenet/deeplab/modeling/sync_batchnorm/replicate.py models/networks/architecture.py refinenet/deeplab/dataloaders/datasets/pascal.py refinenet/deeplab/utils/saver.py models/networks/loss.py refinenet/deeplab/utils/lr_scheduler.py models/networks/base_network.py refinenet/deeplab/modeling/backbone/resnet.py refinenet/deeplab/modeling/deeplab.py refinenet/core/tensorboard.py refinenet/deeplab/modeling/sync_batchnorm/batchnorm.py data/base_dataset.py refinenet/train_segnet.py refinenet/core/training.py util/iter_counter.py options/__init__.py options/test_options.py refinenet/deeplab/modeling/sync_batchnorm/__init__.py models/pix2pix_model.py refinenet/evaluate_segnet.py options/train_options.py refinenet/model.py refinenet/deeplab/dataloaders/datasets/coco.py refinenet/evaluate_refinenet.py train.py models/networks/__init__.py refinenet/deeplab/dataloaders/utils.py refinenet/deeplab/modeling/sync_batchnorm/unittest.py refinenet/deeplab/modeling/backbone/xception.py refinenet/deeplab/train.py util/__init__.py refinenet/deeplab/utils/metrics.py data/openeds_dataset.py refinenet/deeplab/modeling/sync_batchnorm/comm.py data/prepare_openeds.py refinenet/losses/experts_gaze_mse_error.py refinenet/deeplab/dataloaders/datasets/combine_dbs.py util/util.py util/files.py refinenet/deeplab/modeling/aspp.py refinenet/deeplab/doc/deeplab_xception.py refinenet/losses/experts_angular_error.py models/networks/encoder.py refinenet/deeplab/utils/calculate_weights.py refinenet/deeplab/dataloaders/datasets/sbd.py refinenet/train_refinenet.py refinenet/deeplab/dataloaders/custom_transforms.py util/image_annotate.py refinenet/deeplab/mypath.py BaseDataset __get_shape_from_size_or_shape get_transform __crop __make_power_2 __scale_width __resize __scale_shortside normalize get_params flip OpenEDSDataset ImageProcessor OpenEDSPreparator ImagePreprocessor Preprocessor get_option_setter find_dataset_using_name create_dataloader create_inference_dataloader Pix2PixModel get_option_setter create_model find_model_using_name SPADE_STYLE_ResnetBlock BaseNetwork NLayerDiscriminator MultiscaleDiscriminator ConvEncoder SPADESTYLEGenerator GANLoss StyleLoss gram_matrix MSECalculator openEDSaccuracy ApplyStyle SPADE SPADE_STYLE_Block get_nonspade_norm_layer FC define_D create_network modify_commandline_options define_E define_G find_network_using_name BaseOptions TestOptions TrainOptions OpenEDSDataset OpenEDSDataset_Eval RefineNet convert_to_uint_rgb OpenEDSDataset RefineNet MyDeepLab OpenEDSDataset CheckpointManager DefaultConfig draw_gaze vector_to_pitchyaw pitchyaw_to_vector mean_angular_error angular_error GoogleSheetLogger Tensorboard do_final_full_test get_training_batches setup_common script_init_common log_images_with_uncertainties test_model_on_all main_loop_iterator salvage_memory init_datasets cleanup_and_quit step_modulo learning_rate_schedule Path main Trainer FixedResize RandomRotate ToTensor RandomGaussianBlur Normalize RandomHorizontalFlip FixScaleCrop RandomScaleCrop decode_seg_map_sequence encode_segmap decode_segmap get_pascal_labels get_cityscapes_labels make_data_loader CityscapesSegmentation COCOSegmentation CombineDBs VOCSegmentation SBDSegmentation ResNet DeepLabv3_plus get_1x_lr_params Bottleneck get_10x_lr_params ASPP_module ResNet101 Block DeepLabv3_plus get_1x_lr_params get_10x_lr_params SeparableConv2d SeparableConv2d_same ASPP_module fixed_padding Xception build_aspp _ASPPModule ASPP Decoder build_decoder DeepLab drn_d_54 drn_c_58 drn_d_40 drn_d_38 drn_c_26 Bottleneck drn_d_105 DRN_A drn_d_22 conv3x3 DRN drn_a_50 drn_d_24 drn_c_42 BasicBlock fixed_padding InvertedResidual conv_bn MobileNetV2 ResNet ResNet101 Bottleneck fixed_padding Block AlignedXception SeparableConv2d build_backbone _sum_ft SynchronizedBatchNorm2d _unsqueeze_ft _SynchronizedBatchNorm SynchronizedBatchNorm1d SynchronizedBatchNorm3d SyncMaster FutureResult SlavePipe execute_replication_callbacks CallbackContext DataParallelWithCallback patch_replication_callback TorchTestCase as_numpy calculate_weigths_labels SegmentationLosses LR_Scheduler Evaluator Saver TensorboardSummary AngularError ExpertsAngularError ExpertsGazeMSEError GazeMSEError Pix2PixTrainer create_folder_if_not_exists listdir copy_src copy get_text_image IterationCounter Tester natural_keys uint82bin print_tensor_stats copyconf find_class_in_module save_image mkdirs load_network tile_images tensor2im save_obj atoi tensor2label save_network mkdir str2bool load_obj natural_sort labelcolormap print_h5_tree annotate_pupil visualize_sidebyside Visualizer int load_size maximum no_flip crop_size randint Lambda aspect_ratio crop_size Resize append round ndarray isinstance size hasattr int ndarray __get_shape_from_size_or_shape isinstance round int __get_shape_from_size_or_shape int __get_shape_from_size_or_shape size shape ndarray isinstance len Normalize items list import_module replace find_dataset_using_name initialize find_dataset_using_name batchSize print dataset_mode DataLoader N dataset deepcopy items list replace print exit import_module find_model_using_name print __name__ find_model_using_name model sqrt float mul t size mm view find_class_in_module parse_known_args netG netD find_network_using_name cls init_variance init_weights DataParallel print_network init_type cuda numpy clip multiply empty cos sin reshape empty divide arctan2 norm multiply divide sum clip LINE_AA arrowedLine tuple COLOR_GRAY2BGR astype int32 sin cvtColor config_json ArgumentParser device fully_reproducible seed list import_json dir import_dict getattr parse_args replace manual_seed type items add_argument dict install callable original_full_dataset list sorted tuple Subset OrderedDict DataLoader test_num_samples info dataset_class enumerate len getLogger batch_size use_apex CheckpointManager initialize formatter addHandler strftime Tensorboard sum target_lr setFormatter load_last_checkpoint resume_from removeHandler info __name__ FileHandler GoogleSheetLogger write_file_contents makedirs empty_cache collect items list isinstance iter Tensor to next items list eval ready salvage_memory update_current_step info add_scalar list items eval original_full_dataset list items last_step update_or_append_row test_model_on_all DataLoader salvage_memory info target_lr int lr_decay_factor tensorboard_learning_rate_every_n_steps lr_decay_epoch_interval num_warmup_epochs tensorboard_log_func power float base_lr step_modulo batch_size model log_every_n_steps clip_grad_norm_ zero_grad use_apex numpy step_modulo list get_training_batches gradient_norm_clip test_every_n_steps update_current_step salvage_memory tensorboard_scalars_every_n_steps range last_step update_or_append_row tensorboard_images_every_n_steps info num_epochs enumerate add_image int join items save_at_step __del__ backward dict parameters test_model_on_all train step amax add_scalar items list __del__ exit batch_size Trainer ArgumentParser cuda backbone seed str training parse_args range close start_epoch manual_seed gpu_ids validation print add_argument epochs len append transpose decode_segmap from_numpy show copy imshow get_pascal_labels get_cityscapes_labels zeros range zeros astype get_pascal_labels enumerate NUM_CLASSES VOCSegmentation COCOSegmentation SBDSegmentation DataLoader CombineDBs CityscapesSegmentation use_sbd ResNet parameters requires_grad range len parameters requires_grad range len pad load_url load_state_dict DRN_A load_url DRN load_state_dict load_url DRN load_state_dict load_url DRN load_state_dict load_url DRN load_state_dict load_url DRN load_state_dict load_url DRN load_state_dict load_url DRN load_state_dict load_url DRN load_state_dict load_url DRN load_state_dict list hasattr __data_parallel_replicate__ modules enumerate len replicate data isinstance sum uint8 join print astype close tqdm db_root_dir save bincount zeros numpy array log append pi pi pi pi set len mkdir rmtree remove copytree isdir join print copy rmtree make_archive append listdir create_folder_if_not_exists uint8 normalize putText FONT_HERSHEY_SIMPLEX zeros double print setattr Namespace append range concatenate clip isinstance concatenate reshape size transpose unsqueeze append tile_images numpy range len concatenate reshape size float transpose astype append tile_images numpy range fromarray replace repeat dirname save expand_dims makedirs mkdir makedirs sort items list print exit lower import_module join checkpoints_dir name save cuda state_dict load join list checkpoints_dir isinstance name print load_state_dict cuda keys uint8 list id2label uint82bin zeros array range print list hasattr print keys enumerate list append get_error_map OrderedDict unsqueeze to_1resized_imagebatch range cat len int list enumerate append range cat len | [](https://raw.githubusercontent.com/nvlabs/SPADE/master/LICENSE.md)  # Content-Consistent Generation of Realistic Eyes with Style [Project page](https://ait.ethz.ch/projects/2019/seg2eye/) | [Paper](https://arxiv.org/abs/1911.03346) This README describes the Seg2Eye model. For the Refiner model, please have a look at [this folder](refinenet/). ## Abstract Accurately labeled real-world training data can be scarce, and hence recent works adapt, modify or generate images to boost target datasets. However, retaining relevant details from input data in the generated images is challenging and failure could be critical to the performance on the final | 2,940 |
mcordts/cityscapesScripts | ['autonomous driving'] | ['Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection'] | cityscapesscripts/helpers/box3dImageTransform.py cityscapesscripts/evaluation/evalObjectDetection3d.py cityscapesscripts/evaluation/evalPanopticSemanticLabeling.py cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py cityscapesscripts/annotation/cityscapesLabelTool.py cityscapesscripts/evaluation/objectDetectionHelpers.py cityscapesscripts/preparation/createTrainIdInstanceImgs.py cityscapesscripts/helpers/labels.py cityscapesscripts/helpers/csHelpers.py cityscapesscripts/helpers/labels_cityPersons.py cityscapesscripts/preparation/createPanopticImgs.py cityscapesscripts/evaluation/instances2dict.py cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py cityscapesscripts/evaluation/plot3dResults.py cityscapesscripts/preparation/json2labelImg.py cityscapesscripts/evaluation/instance.py cityscapesscripts/preparation/json2instanceImg.py cityscapesscripts/preparation/createTrainIdLabelImgs.py cityscapesscripts/helpers/annotation.py cityscapesscripts/viewer/cityscapesViewer.py setup.py cityscapesscripts/helpers/version.py cityscapesscripts/download/downloader.py CorrectionBox configuration CityscapesLabelTool main enum login download_packages parse_arguments list_available_packages main get_available_packages evaluateMatches readPredInfo CArgs computeAverages prepareJSONDataForResults assignGt2Preds getGtInstances setInstanceLabels filterGtInstances readGTImage getPrediction evaluateImgLists matchGtWithPreds main printResults evaluate3dObjectDetection main Box3dEvaluator PQStat get_traceback pq_compute_single_core pq_compute_multi_core PQStatCat average_pq evaluatePanoptic print_results rgb2id main Instance main instances2dict getFiles calcOverlapMatrix EvaluationParameters calcIouMatrix create_AP_plot set_up_xaxis create_all_axes get_available_items_scaling get_x_y_vals create_result_table_and_legend_plot prepare_data create_table_row csToMplColor set_up_PR_plot_axis main fill_standard_subplot create_PR_plot fill_and_finalize_subplot create_spider_chart_plot plot_data CsBbox2d CsPoly CsObjectType CsObject Annotation CsIgnore2d CsBbox3d get_projection_matrix apply_transformation_points Box3dImageTransform Camera get_K_multiplier getCoreImageFileName printError getCsFileInfo getColorEntry getDirectory ensurePath writeDict2JSON colors assureSingleInstanceName main convert2panoptic main main printHelp createInstanceImage printError main json2instanceImg printHelp printError json2labelImg main createLabelImage main CityscapesViewer CsLabelType enum exec_ argv exit CityscapesLabelTool QApplication user_data_dir join chmod format get remove S_IREAD S_IWRITE getpass post eval lower raise_for_status isfile input Session makedirs get raise_for_status print format get_available_packages get join format exists print md5 raise_for_status get_available_packages add_argument ArgumentParser list_available login download_packages parse_arguments list_available_packages join predictionWalk format printError getCsFileInfo predictionPath realpath filter city sequenceNb dirname abspath append frameNb walk append name format printError gtInstancesFile print instances2dict isfile writeDict2JSON readPredInfo format print assignGt2Preds filterGtInstances readGTImage abspath zip flush len count_nonzero deepcopy int instLabels name reshape convert logical_and id copy shape ignoreInEval append array enumerate open instLabels cumsum max printError ones append minRegionSizes copy unique zip float empty enumerate distanceConfs convolve distanceThs min overlaps argsort dot zeros len instLabels distanceAvailable distanceThs overlaps where nanmean average argmax isclose enumerate format instLabels distanceAvailable print enumerate tolist instLabels evaluateMatches computeAverages prepareJSONDataForResults exportFile getGtInstances JSONOutput setInstanceLabels matchGtWithPreds dirname ensurePath writeDict2JSON printResults format groundTruthSearch printError gtInstancesFile glob print getPrediction evaluateImgLists append join format step_size max_depth evaluate saveResults results checkCw min_iou_to_match loadGT labels_to_evaluate loadPredictions info cw Box3dEvaluator plot_data get join evaluate3dObjectDetection evalLabels error add_argument realpath resultsFolder dirname ArgumentParser info parse_args EvaluationParameters gtFolder predictionFolder makedirs astype int32 PQStat join remove format uint64 get list items print rgb2id astype set add unique zip open array len PQStat format array_split print cpu_count apply_async close append Pool enumerate len pq_average print sorted format keys time format printError pq_compute_multi_core print average_pq print_results append resultsFile gtJsonFile predictionJsonFile evaluatePanoptic format toDict print Instance len unique abspath append array flush open instances2dict minimum transpose maximum split minimum transpose maximum split append join walk sort color text list format items legend text axis create_table_row set_theta_direction arange set_rlabel_position plot set_xticklabels set_yticklabels set_yticks pi set_theta_offset set_xticks legend fill tick_params enumerate set_ylim len fill_standard_subplot set_ylabel set_title set_ylim set_xticklabels set_xlim linspace set_xticks format arange set_title set_xticklabels set_xlabel set_xlim set_ylabel set_xticks set_ylim set_up_xaxis set_xlabel subplot2grid list sorted plot map maximum zip set_up_PR_plot_axis range len create_AP_plot get_x_y_vals replace set_title get_available_items_scaling set_ylabel fill_standard_subplot create_PR_plot set_ylim list sorted plot map scatter zip list max values list values show create_all_axes create_result_table_and_legend_plot tight_layout figure get_legend_handles_labels fill_and_finalize_subplot create_spider_chart_plot enumerate prepare_data path plot_data zeros u0 fy fx zeros v0 T concatenate print str exit format basename printError CsFile split len getCsFileInfo dirname makedirs save open basename printError dirname ignoreInEval append sum format replace glob realpath mkdir unique flush enumerate join print sort zeros array len setNames outputFolder cityscapesPath convert2panoptic useTrainId replace sort json2instanceImg flush len json2labelImg print basename format format printHelp hasInstances format trainId Draw deleted printError print new id objects polygon label createInstanceImage Annotation fromJsonFile save printHelp getopt format trainId Draw deleted printError print new id color objects polygon label createLabelImage Annotation fromJsonFile save CityscapesViewer resize | # The Cityscapes Dataset This repository contains scripts for inspection, preparation, and evaluation of the Cityscapes dataset. This large-scale dataset contains a diverse set of stereo video sequences recorded in street scenes from 50 different cities, with high quality pixel-level annotations of 5 000 frames in addition to a larger set of 20 000 weakly annotated frames. Details and download are available at: www.cityscapes-dataset.com ## Dataset Structure The folder structure of the Cityscapes dataset is as follows: ``` {root}/{type}{video}/{split}/{city}/{city}_{seq:0>6}_{frame:0>6}_{type}{ext} ``` The meaning of the individual elements is: - `root` the root folder of the Cityscapes dataset. Many of our scripts check if an environment variable `CITYSCAPES_DATASET` pointing to this folder exists and use this as the default choice. | 2,941 |
mdabashar/TAnoGAN | ['time series', 'anomaly detection'] | ['TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks'] | nab_dataset.py models/recurrent_models_pyramid.py main NabDataset DataSettings LSTMGenerator LSTMDiscriminator print NabDataset DataSettings | # TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks ## Published at: https://ieeexplore.ieee.org/abstract/document/9308512/keywords#keywords ## arXiv: https://arxiv.org/abs/2008.09567 ## Video Presentation: https://www.youtube.com/watch?v=m3wazsde_Vs ## Cite: @inproceedings{bashar2020tanogan, title={TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks}, author={Bashar, Md Abul and Nayak, Richi}, booktitle={2020 IEEE Symposium Series on Computational Intelligence (SSCI)}, pages={1778--1785}, | 2,942 |
mdbloice/Patch-Augmentation | ['adversarial attack'] | ['Patch augmentation: Towards efficient decision boundaries for neural networks'] | PatchAugmentation.py PatchAugmentation | # Patch Augmentation *Patch Augmentation* is an novel image augmentation technique designed to improve model generalisation and mitigate against adversarial attacks. For details, see the following pre-print: **Patch augmentation: Towards efficient decision boundaries for neural networks**, *arXiv:1911.07922*, Nov. 2019, <https://arxiv.org/abs/1911.07922> ## How it works *Patch Augmentation* is a data-independent approach that creates new image data based on image/label pairs, where a patch from one of the two images in the pair is superimposed on to the other image, creating a new augmented sample. Below is a visual example of the technique:  The augmented image label is a combination of the image pair's original labels. The labels for the dog and cat classes are `[1.0, 0.0]` and `[0.0, 1.0]` respectively. Clockwise from the upper left, the augmented image's labels are `[0.72220625, 0.27779375]`, `[0.2832, 0.7168]`, `[0.0, 1.0]`, and `[0.918925, 0.081075]` respectively. A notebook containing a reproducible experiment (training ResNet20v1 using the CIFAR-100 data set) can be found in the following notebook: [Patch-Augmentation-CIFAR-100.ipynb](Patch-Augmentation-CIFAR-100.ipynb) | 2,943 |
mdv3101/CDeCNet | ['table detection'] | ['CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images'] | mmdet/datasets/pipelines/instaboost.py tools/regnet2mmdet.py mmdet/ops/masked_conv/__init__.py mmdet/core/bbox/coder/delta_xywh_bbox_coder.py mmdet/core/mask/utils.py configs/_base_/datasets/coco_instance.py mmdet/core/bbox/samplers/pseudo_sampler.py mmdet/models/backbones/regnet.py mmdet/models/backbones/res2net.py mmdet/models/backbones/ssd_vgg.py mmdet/models/necks/nasfcos_fpn.py configs/cascade_rcnn/db_cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py mmdet/models/dense_heads/rpn_head.py tests/test_heads.py tests/test_pipelines/test_transform.py mmdet/models/roi_heads/grid_roi_head.py mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py mmdet/core/utils/misc.py mmdet/core/post_processing/bbox_nms.py mmdet/models/dense_heads/ssd_head.py mmdet/core/bbox/coder/__init__.py mmdet/models/detectors/__init__.py mmdet/models/roi_heads/mask_heads/__init__.py mmdet/ops/context_block.py tools/browse_dataset.py mmdet/core/bbox/assigners/__init__.py mmdet/models/roi_heads/mask_heads/htc_mask_head.py mmdet/datasets/samplers/group_sampler.py mmdet/core/bbox/assigners/base_assigner.py mmdet/models/dense_heads/anchor_head.py mmdet/datasets/builder.py mmdet/core/evaluation/eval_hooks.py mmdet/models/losses/mse_loss.py mmdet/models/necks/fpn_carafe.py mmdet/core/bbox/iou_calculators/builder.py mmdet/ops/wrappers.py tools/pytorch2onnx.py mmdet/core/bbox/assigners/assign_result.py mmdet/models/detectors/fcos.py mmdet/models/dense_heads/fcos_head.py mmdet/models/necks/hrfpn.py mmdet/utils/collect_env.py mmdet/core/utils/dist_utils.py tools/print_config.py tests/test_pisa_heads.py mmdet/models/roi_heads/mask_scoring_roi_head.py postprocessing/1.multiscale_full_json_process.py tools/convert_datasets/cityscapes.py postprocessing/1.full_json_process.py mmdet/core/bbox/coder/tblr_bbox_coder.py mmdet/models/detectors/nasfcos.py mmdet/models/losses/accuracy.py mmdet/ops/nms/__init__.py mmdet/models/detectors/single_stage.py setup.py mmdet/ops/merge_cells.py mmdet/models/builder.py mmdet/core/bbox/iou_calculators/iou2d_calculator.py mmdet/utils/__init__.py mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py mmdet/models/dense_heads/nasfcos_head.py mmdet/ops/roi_align/roi_align.py mmdet/datasets/__init__.py mmdet/datasets/pipelines/loading.py mmdet/models/losses/balanced_l1_loss.py postprocessing/2.multiscale_post_map_bbox.py tools/get_flops.py mmdet/models/detectors/mask_rcnn.py mmdet/models/losses/focal_loss.py tools/convert_datasets/pascal_voc.py mmdet/core/evaluation/class_names.py mmdet/core/bbox/demodata.py mmdet/ops/carafe/__init__.py tools/test.py mmdet/models/dense_heads/fovea_head.py mmdet/models/dense_heads/pisa_retinanet_head.py mmdet/models/roi_heads/shared_heads/res_layer.py tests/test_fp16.py docs/conf.py mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py mmdet/core/bbox/samplers/sampling_result.py configs/_base_/models/db_cascade_mask_rcnn_r50_fpn.py mmdet/datasets/cityscapes.py mmdet/core/evaluation/recall.py mmdet/core/optimizer/copy_of_sgd.py mmdet/models/roi_heads/roi_extractors/__init__.py mmdet/models/dense_heads/ga_retina_head.py mmdet/core/post_processing/merge_augs.py tools/robustness_eval.py configs/_base_/datasets/coco_detection.py mmdet/models/detectors/grid_rcnn.py mmdet/core/anchor/builder.py mmdet/core/fp16/__init__.py mmdet/models/detectors/cascade_rcnn.py mmdet/models/utils/__init__.py mmdet/models/detectors/htc.py mmdet/core/__init__.py mmdet/ops/plugin.py configs/_base_/schedules/schedule_1x.py mmdet/models/backbones/db_resnet.py mmdet/utils/logger.py tests/test_async.py tests/test_wrappers.py mmdet/models/dense_heads/reppoints_head.py mmdet/models/dense_heads/free_anchor_retina_head.py mmdet/models/backbones/hrnet.py mmdet/models/backbones/resnext.py mmdet/models/detectors/rpn.py mmdet/models/utils/res_layer.py tests/test_pipelines/test_loading.py mmdet/models/dense_heads/retina_sepbn_head.py mmdet/ops/__init__.py tools/benchmark.py tests/test_anchor.py mmdet/utils/contextmanagers.py mmdet/models/necks/bfp.py mmdet/apis/inference.py tests/test_dataset.py mmdet/core/bbox/assigners/atss_assigner.py tests/test_forward.py tests/test_masks.py mmdet/core/fp16/hooks.py mmdet/core/bbox/samplers/base_sampler.py mmdet/models/roi_heads/test_mixins.py tools/fuse_conv_bn.py convert_dual_backbone.py mmdet/core/bbox/iou_calculators/__init__.py tests/test_backbone.py mmdet/ops/roi_align/__init__.py tests/test_sampler.py mmdet/models/detectors/atss.py mmdet/models/losses/utils.py tools/test_robustness.py mmdet/datasets/pipelines/__init__.py mmdet/models/detectors/fast_rcnn.py mmdet/core/bbox/transforms.py mmdet/ops/roi_align/gradcheck.py mmdet/models/backbones/__init__.py mmdet/models/roi_heads/bbox_heads/bbox_head.py tests/test_losses.py tools/publish_model.py mmdet/core/bbox/assigners/max_iou_assigner.py mmdet/ops/carafe/grad_check.py mmdet/core/bbox/assigners/point_assigner.py mmdet/ops/roi_pool/__init__.py tests/test_soft_nms.py mmdet/models/detectors/faster_rcnn.py mmdet/core/utils/__init__.py mmdet/ops/nms/nms_wrapper.py mmdet/models/losses/iou_loss.py mmdet/apis/test.py mmdet/models/roi_heads/cascade_roi_head.py mmdet/core/bbox/coder/pseudo_bbox_coder.py configs/_base_/default_runtime.py mmdet/core/optimizer/builder.py mmdet/models/dense_heads/atss_head.py mmdet/models/detectors/fsaf.py mmdet/ops/dcn/deform_conv.py tests/test_assigner.py mmdet/apis/__init__.py mmdet/version.py tests/async_benchmark.py tools/train.py mmdet/core/optimizer/default_constructor.py mmdet/core/evaluation/mean_ap.py mmdet/ops/generalized_attention.py mmdet/core/anchor/__init__.py mmdet/models/detectors/retinanet.py mmdet/datasets/dataset_wrappers.py mmdet/models/necks/pafpn.py mmdet/ops/non_local.py mmdet/ops/sigmoid_focal_loss/sigmoid_focal_loss.py mmdet/models/losses/cross_entropy_loss.py configs/dcn/db_cascade_mask_rcnn_x101_fpn_dconv_c3-c5_1x_coco.py mmdet/models/roi_heads/bbox_heads/double_bbox_head.py mmdet/ops/sigmoid_focal_loss/__init__.py mmdet/models/roi_heads/htc_roi_head.py mmdet/datasets/pipelines/formating.py mmdet/models/roi_heads/shared_heads/__init__.py mmdet/models/roi_heads/__init__.py mmdet/models/losses/pisa_loss.py mmdet/models/losses/smooth_l1_loss.py mmdet/core/bbox/assigners/approx_max_iou_assigner.py mmdet/models/roi_heads/base_roi_head.py mmdet/datasets/voc.py mmdet/models/necks/__init__.py mmdet/models/losses/ghm_loss.py mmdet/ops/carafe/carafe.py mmdet/models/roi_heads/mask_heads/fcn_mask_head.py mmdet/core/anchor/anchor_generator.py tests/test_nms.py mmdet/models/backbones/db_resnext.py mmdet/models/roi_heads/bbox_heads/__init__.py mmdet/core/mask/mask_target.py mmdet/utils/profiling.py mmdet/models/losses/__init__.py mmdet/core/bbox/assigners/center_region_assigner.py mmdet/ops/masked_conv/masked_conv.py mmdet/core/bbox/coder/base_bbox_coder.py mmdet/core/post_processing/__init__.py mmdet/datasets/xml_style.py mmdet/models/necks/fpn.py mmdet/datasets/coco.py mmdet/models/__init__.py mmdet/datasets/pipelines/compose.py mmdet/models/dense_heads/ga_rpn_head.py mmdet/core/bbox/samplers/__init__.py mmdet/models/detectors/mask_scoring_rcnn.py configs/cascade_rcnn/db_cascade_mask_rcnn_r50_fpn_1x_coco.py mmdet/datasets/samplers/__init__.py mmdet/core/bbox/samplers/combined_sampler.py mmdet/models/roi_heads/roi_extractors/single_level.py mmdet/ops/dcn/deform_pool.py tools/upgrade_model_version.py mmdet/core/anchor/utils.py mmdet/models/detectors/two_stage.py mmdet/models/detectors/base.py mmdet/core/bbox/samplers/ohem_sampler.py mmdet/models/roi_heads/pisa_roi_head.py mmdet/models/roi_heads/mask_heads/fused_semantic_head.py mmdet/ops/roi_pool/gradcheck.py mmdet/core/fp16/decorators.py mmdet/datasets/custom.py demo/image_demo.py mmdet/datasets/wider_face.py postprocessing/3.multiscale_post_nms.py mmdet/models/necks/nas_fpn.py tools/coco_error_analysis.py mmdet/core/evaluation/bbox_overlaps.py mmdet/models/dense_heads/fsaf_head.py mmdet/ops/utils/__init__.py mmdet/utils/flops_counter.py mmdet/core/mask/__init__.py tests/test_merge_cells.py tests/test_necks.py mmdet/models/roi_heads/mask_heads/grid_head.py mmdet/ops/roi_pool/roi_pool.py mmdet/models/dense_heads/__init__.py tests/test_utils.py mmdet/core/bbox/builder.py mmdet/core/fp16/utils.py mmdet/models/backbones/resnet.py mmdet/models/roi_heads/double_roi_head.py mmdet/core/bbox/samplers/random_sampler.py mmdet/utils/util_mixins.py mmdet/models/roi_heads/mask_heads/maskiou_head.py mmdet/ops/dcn/__init__.py mmdet/datasets/pipelines/test_aug.py mmdet/models/dense_heads/retina_head.py mmdet/models/dense_heads/pisa_ssd_head.py mmdet/ops/carafe/setup.py mmdet/core/bbox/__init__.py mmdet/core/optimizer/__init__.py mmdet/core/bbox/samplers/score_hlr_sampler.py mmdet/models/detectors/fovea.py mmdet/ops/conv_ws.py mmdet/datasets/samplers/distributed_sampler.py tools/detectron2pytorch.py mmdet/core/mask/structures.py mmdet/models/dense_heads/guided_anchor_head.py mmdet/models/detectors/test_mixins.py tools/analyze_logs.py mmdet/models/detectors/reppoints_detector.py tests/test_config.py mmdet/core/anchor/point_generator.py mmdet/models/roi_heads/standard_roi_head.py mmdet/apis/train.py mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py tests/test_optimizer.py mmdet/core/evaluation/__init__.py mmdet/__init__.py mmdet/datasets/pipelines/transforms.py make_cuda_ext write_version_py readme get_version parse_requirements get_git_hash get_hash main show_result_pyplot LoadImage inference_detector init_detector multi_gpu_test collect_results_cpu collect_results_gpu single_gpu_test batch_processor parse_losses train_detector set_random_seed LegacyAnchorGenerator AnchorGenerator LegacySSDAnchorGenerator SSDAnchorGenerator build_anchor_generator PointGenerator calc_region images_to_levels anchor_inside_flags build_assigner build_bbox_coder build_sampler ensure_rng random_boxes roi2bbox bbox_flip distance2bbox bbox_mapping bbox2result bbox_mapping_back bbox2roi ApproxMaxIoUAssigner AssignResult ATSSAssigner BaseAssigner is_located_in scale_boxes bboxes_area CenterRegionAssigner MaxIoUAssigner PointAssigner BaseBBoxCoder bbox2delta delta2bbox DeltaXYWHBBoxCoder legacy_delta2bbox LegacyDeltaXYWHBBoxCoder legacy_bbox2delta PseudoBBoxCoder bboxes2tblr TBLRBBoxCoder tblr2bboxes build_iou_calculator bbox_overlaps BboxOverlaps2D BaseSampler CombinedSampler InstanceBalancedPosSampler IoUBalancedNegSampler OHEMSampler PseudoSampler RandomSampler SamplingResult ScoreHLRSampler bbox_overlaps get_classes imagenet_vid_classes voc_classes imagenet_det_classes coco_classes cityscapes_classes wider_face_classes DistEvalHook EvalHook eval_map tpfp_imagenet print_map_summary average_precision get_cls_results tpfp_default plot_iou_recall set_recall_param print_recall_summary _recalls eval_recalls plot_num_recall force_fp32 auto_fp16 Fp16OptimizerHook wrap_fp16_model patch_forward_method patch_norm_fp32 cast_tensor_type mask_target mask_target_single BitmapMasks polygon_to_bitmap BaseInstanceMasks PolygonMasks split_combined_polys encode_mask_results build_optimizer build_optimizer_constructor register_torch_optimizers CopyOfSGD DefaultOptimizerConstructor multiclass_nms merge_aug_scores merge_aug_masks merge_aug_bboxes merge_aug_proposals DistOptimizerHook allreduce_grads _allreduce_coalesced unmap tensor2imgs multi_apply build_dataloader build_dataset worker_init_fn _concat_dataset CityscapesDataset CocoDataset CustomDataset ClassBalancedDataset RepeatDataset ConcatDataset VOCDataset WIDERFaceDataset XMLDataset Compose DefaultFormatBundle Transpose ToTensor Collect WrapFieldsToLists to_tensor ImageToTensor ToDataContainer InstaBoost LoadImageFromFile LoadMultiChannelImageFromFiles LoadProposals LoadAnnotations MultiScaleFlipAug RandomFlip Pad Corrupt PhotoMetricDistortion MinIoURandomCrop SegRescale Resize RandomCrop Albu Normalize Expand DistributedSampler GroupSampler DistributedGroupSampler build_shared_head build_detector build_loss build build_backbone build_roi_extractor build_head build_neck DB_ResNet BasicBlock Bottleneck DB_ResNetV1d DB_ResNeXt Bottleneck HRModule HRNet RegNet Res2Net Res2Layer Bottle2neck ResNet BasicBlock Bottleneck ResNetV1d ResNeXt Bottleneck SSDVGG L2Norm AnchorHead reduce_mean ATSSHead FCOSHead FeatureAlign FoveaHead FreeAnchorRetinaHead FSAFHead GARetinaHead GARPNHead FeatureAdaption GuidedAnchorHead NASFCOSHead PISARetinaHead PISASSDHead RepPointsHead RetinaHead RetinaSepBNHead RPNHead SSDHead ATSS BaseDetector CascadeRCNN FasterRCNN FastRCNN FCOS FOVEA FSAF GridRCNN HybridTaskCascade MaskRCNN MaskScoringRCNN NASFCOS RepPointsDetector RetinaNet RPN SingleStageDetector RPNTestMixin TwoStageDetector Accuracy accuracy BalancedL1Loss balanced_l1_loss binary_cross_entropy mask_cross_entropy _expand_binary_labels CrossEntropyLoss cross_entropy sigmoid_focal_loss py_sigmoid_focal_loss FocalLoss GHMR _expand_onehot_labels GHMC bounded_iou_loss iou_loss IoULoss BoundedIoULoss GIoULoss giou_loss mse_loss MSELoss isr_p carl_loss L1Loss smooth_l1_loss SmoothL1Loss l1_loss weight_reduce_loss weighted_loss reduce_loss BFP FPN FPN_CARAFE HRFPN NASFCOS_FPN NASFPN PAFPN BaseRoIHead CascadeRoIHead DoubleHeadRoIHead GridRoIHead HybridTaskCascadeRoIHead MaskScoringRoIHead PISARoIHead StandardRoIHead MaskTestMixin BBoxTestMixin BBoxHead Shared4Conv1FCBBoxHead Shared2FCBBoxHead ConvFCBBoxHead DoubleConvFCBBoxHead BasicResBlock _do_paste_mask FCNMaskHead FusedSemanticHead GridHead HTCMaskHead MaskIoUHead SingleRoIExtractor ResLayer ResLayer last_zero_init ContextBlock conv_ws_2d ConvWS2d GeneralizedAttention SumCell ConcatCell BaseMergeCell GlobalPoolingCell NonLocal2D build_plugin_layer MaxPool2d Conv2d ConvTranspose2d NewEmptyTensorOp Linear CARAFEPack CARAFENaive CARAFENaiveFunction CARAFE CARAFEFunction DeformConvFunction ModulatedDeformConv DeformConvPack ModulatedDeformConvPack DeformConv ModulatedDeformConvFunction DeformRoIPoolingPack DeformRoIPoolingFunction ModulatedDeformRoIPoolingPack DeformRoIPooling MaskedConv2dFunction MaskedConv2d nms batched_nms soft_nms nms_match RoIAlign RoIAlignFunction RoIPool RoIPoolFunction SigmoidFocalLoss SigmoidFocalLossFunction collect_env add_flops_counting_methods add_flops_counter_hook_function bn_flops_counter_hook reset_flops_count gn_flops_counter_hook relu_flops_counter_hook deconv_flops_counter_hook get_model_parameters_number add_flops_mask flops_to_string params_to_string remove_flops_mask remove_batch_counter_hook_function start_flops_count add_batch_counter_variables_or_reset pool_flops_counter_hook empty_flops_counter_hook add_flops_mask_variable_or_reset add_batch_counter_hook_function get_model_complexity_info conv_flops_counter_hook remove_flops_counter_hook_function batch_counter_hook add_flops_counter_variable_or_reset is_supported_instance stop_flops_count upsample_flops_counter_hook linear_flops_counter_hook compute_average_flops_cost print_model_with_flops get_root_logger profile_time NiceRepr read_file_return_bbox_dict write_file Overlap_percentage check_overlap read_file_return_bbox_dict write_file Overlap_percentage check_overlap test_anchor_generator_with_tuples test_retina_anchor test_strides test_standard_anchor_generator test_guided_anchor test_ssd_anchor_generator test_approx_iou_assigner_with_empty_boxes test_point_assigner test_max_iou_assigner test_approx_iou_assigner_with_empty_gt test_point_assigner_with_empty_gt test_random_assign_result test_center_region_assigner_with_ignore test_center_region_assigner_with_empty_bboxes test_approx_iou_assigner test_point_assigner_with_empty_boxes_and_gt test_max_iou_assigner_with_empty_boxes_and_gt test_max_iou_assigner_with_empty_boxes test_center_region_assigner_with_empty_gts test_max_iou_assigner_with_empty_gt test_max_iou_assigner_with_ignore test_center_region_assigner test_approx_iou_assigner_with_empty_boxes_and_gt test_max_iou_assigner_with_empty_boxes_and_ignore MaskRCNNDetector AsyncTestCase AsyncInferenceTestCase test_resnext_backbone check_norm_state test_resnet_basic_block all_zeros test_res2net_backbone test_resnet_bottleneck is_block test_res2net_bottle2neck test_renext_bottleneck is_norm test_regnet_backbone test_resnet_backbone test_resnet_res_layer _check_mask_head test_config_build_detector _get_config_directory _check_anchorhead _check_roi_head _check_roi_extractor _check_bbox_head test_config_data_pipeline test_custom_classes_override_default test_dataset_wrapper _get_config_directory _get_config_module test_single_stage_forward_gpu _get_detector_cfg test_faster_rcnn_ohem_forward _demo_mm_inputs test_single_stage_forward_cpu test_rpn_forward test_two_stage_forward test_auto_fp16 test_force_fp32 test_cast_tensor_type test_ga_anchor_head_loss _dummy_bbox_sampling test_anchor_head_loss test_mask_head_loss _demodata_refine_boxes test_fsaf_head_loss test_bbox_head_loss test_refine_boxes test_ce_loss test_polygon_mask_index test_bitmap_mask_flip test_bitmap_mask_index test_bitmap_mask_pad test_polygon_mask_crop_and_resize test_bitmap_mask_to_ndarray test_bitmap_mask_crop dummy_bboxes dummy_raw_polygon_masks test_polygon_mask_crop test_polygon_mask_iter test_bitmap_mask_crop_and_resize test_bitmap_mask_iter test_bitmap_mask_resize test_polygon_mask_area test_polygon_mask_resize test_bitmap_mask_expand test_polygon_mask_to_ndarray test_bitmap_mask_to_tensor test_bitmap_mask_area test_polygon_to_tensor test_polygon_mask_expand test_polygon_mask_pad test_polygon_mask_rescale dummy_raw_bitmap_masks test_bitmap_mask_rescale test_polygon_mask_to_bitmap test_bitmap_mask_init test_polygon_mask_flip test_polygon_mask_init test_resize_methods test_sum_cell test_concat_cell test_global_pool_cell test_fpn test_nms_device_and_dtypes_cpu test_nms_match test_nms_device_and_dtypes_gpu check_optimizer test_build_optimizer_constructor SubModel ExampleDuplicateModel test_torch_optimizers PseudoDataParallel ExampleModel test_build_optimizer check_default_optimizer test_default_optimizer_constructor test_pisa_retinanet_head_loss test_pisa_ssd_head_loss test_pisa_roi_head_loss test_random_sampler_empty_pred test_ohem_sampler _context_for_ohem test_ohem_sampler_empty_gt test_random_sampler_empty_gt test_random_sampler test_random_sample_result test_ohem_sampler_empty_pred test_score_hlr_sampler_empty_pred test_soft_nms_device_and_dtypes_cpu test_params_to_string test_linear test_conv_transposed_2d test_nn_op_forward_called test_conv2d test_max_pool_2d TestLoading test_resize test_pad test_normalize test_min_iou_random_crop test_random_crop test_albu_transform test_flip cal_train_time plot_curve load_json_logs main parse_args add_plot_parser add_time_parser main parse_args main parse_args retrieve_data_cfg main analyze_results analyze_individual_category makeplot main convert convert_bn convert_conv_fc main fuse_module fuse_conv_bn parse_args main parse_args main parse_args main parse_args process_checkpoint main parse_args export_onnx_model convert_reslayer convert convert_head main convert_stem get_distortions_from_results print_coco_results get_distortions_from_file get_coco_style_results get_voc_style_results get_results main main parse_args voc_eval_with_return single_gpu_test collect_results coco_eval_with_return main multi_gpu_test parse_args main parse_args convert parse_config truncate_reg_channel truncate_cls_channel is_head reorder_cls_channel main collect_annotations cvt_annotations load_img_info main collect_files parse_args main cvt_annotations parse_xml parse_args decode _minimal_ext_cmd exists join format asctime get_hash split print list gen_packages_items config img add_argument inference_detector show_result_pyplot ArgumentParser init_detector parse_args checkpoint get_classes isinstance model load_checkpoint warn eval build_detector fromfile simplefilter to data isinstance Compose warn cfg dict modules test_pipeline device is_cuda collate show_result module hasattr imsave update join encode_mask_results isinstance imresize show_result size ProgressBar eval zip append dataset tensor2imgs range enumerate len update get_dist_info ProgressBar eval collect_results_cpu collect_results_gpu sleep append dataset range enumerate len rstrip tensor broadcast list get_dist_info mkdtemp encode append range dump bytearray zip load join barrier extend rmtree mkdir_or_exist full list get_dist_info bytearray dumps extend tobytes shape loads all_gather zip append tensor max zeros seed manual_seed_all manual_seed items list isinstance clone get_world_size OrderedDict mean all_reduce item div_ Tensor sum dict parse_losses model workflow log_level MMDistributedDataParallel warning DistSamplerSeedHook cuda run total_epochs build_optimizer checkpoint_config get_root_logger work_dir build_dataset optimizer_config get val load_from build_dataloader imgs_per_gpu resume_from register_training_hooks resume optimizer DistOptimizerHook eval_hook lr_config load_checkpoint register_hook dict Runner log_config Fp16OptimizerHook MMDataParallel append stack clamp long _rand RandomState isinstance minimum astype float32 maximum from_numpy ensure_rng Tensor ndarray isinstance clone new_tensor bbox_flip new_tensor view new_full new_zeros append cat enumerate cpu append unique numpy clamp zeros_like stack unsqueeze div_ float log exp clamp size repeat expand_as view_as abs log stack unsqueeze div_ float log exp clamp size repeat expand_as view_as abs log tensor unsqueeze cat split clamp_ unsqueeze tensor cat split clamp size min max minimum T astype maximum float32 zeros range items list eval is_str arange ones hstack maximum zeros sum range minimum zeros_like concatenate argsort vstack zeros bbox_overlaps range enumerate len max zeros_like concatenate argsort vstack zeros argmax bbox_overlaps enumerate len append empty starmap cumsum tuple vstack Pool get_cls_results list print_map_summary append range eps close mean item zip enumerate maximum argsort any average_precision zeros len get_classes ndarray isinstance table len is_str print_log AsciiTable append zeros range enumerate sum sort hstack copy zeros float argmax fliplr range enumerate array isinstance min set_recall_param print_recall_summary _recalls array append zeros bbox_overlaps range len arange table insert size tolist print_log AsciiTable append array enumerate show ndarray plot isinstance xlabel tolist axis ylabel figure show ndarray plot isinstance xlabel tolist axis ylabel figure hasattr patch_norm_fp32 modules half children isinstance half patch_forward_method float forward ndarray isinstance Iterable Tensor Mapping list map cat mask_size size to_ndarray new_zeros device to numpy clip _pair frPyObjects bool astype merge tolist append slice_list range len append range isinstance len dir getattr startswith append optim pop deepcopy build_optimizer_constructor dict optim_constructor view size new_zeros expand batched_nms nms nms_thr sort min clone max_num zip append bbox_mapping_back cat append mean bbox_mapping_back zip Tensor isinstance average mean array list _take_tensors _flatten_dense_tensors zip _unflatten_dense_tensors OrderedDict all_reduce copy_ div_ append type values all_reduce _allreduce_coalesced get_world_size div_ uint8 transpose size astype ascontiguousarray append array range list map new_full get deepcopy isinstance append build_dataset range len get isinstance ConcatDataset _concat_dataset build_from_cfg ClassBalancedDataset RepeatDataset DistributedSampler get_dist_info DistributedGroupSampler DataLoader seed Tensor ndarray isinstance isinstance all_reduce clone get_world_size div_ topk isinstance size t eq mul_ expand_as append sum max log abs e where float weight_reduce_loss new_full size squeeze expand size weight_reduce_loss binary_cross_entropy_with_logits _expand_binary_labels float squeeze arange type_as sigmoid pow weight_reduce_loss binary_cross_entropy_with_logits _sigmoid_focal_loss size weight_reduce_loss view new_full size squeeze expand clamp view zeros_like size min where abs max clamp min max decode pos_assigned_gt_inds loss_cls max list view append sum range cat detach size unique float reshape sort pow bbox_overlaps len view reshape size pow loss_bbox float sum abs where abs get_enum sum reduce_loss arange grid_sample isinf size where expand stack any device to split Sequential isinstance constant_init size view pop str plugin_layer copy ndarray isinstance new_zeros Tensor to numpy is_cuda ndarray isinstance from_numpy cpu Tensor pop copy nms_op eval to max cat isinstance from_numpy cpu Tensor show join str defaultdict list replace items check_output strip get_compiler_version device_count __version__ get_compiling_cuda_version platform is_available range append flops_model get_model_parameters_number input_constructor stop_flops_count add_flops_counting_methods start_flops_count compute_average_flops_cost new_empty print_model_with_flops print compute_average_flops_cost apply sum __get__ reset_flops_count apply __batch_counter__ is_supported_instance modules add_batch_counter_hook_function apply remove_batch_counter_hook_function apply add_batch_counter_variables_or_reset apply apply apply type issubclass numel shape affine prod shape affine prod groups kernel_size out_channels in_channels list kernel_size out_channels groups in_channels expand sum prod print len register_forward_hook hasattr remove hasattr is_supported_instance items issubclass hasattr register_forward_hook type is_supported_instance remove is_supported_instance hasattr is_supported_instance get_logger record_event monotonic Event str close write open float min max items Overlap_percentage list set append keys len print sqrt dict build_anchor_generator AnchorGenerator tensor grid_anchors base_anchors build_anchor_generator grid_anchors dict valid_flags is_available enumerate build_anchor_generator dict zip is_available grid_anchors base_anchors grid_anchors dict valid_flags build_head is_available enumerate base_anchors grid_anchors dict valid_flags build_head is_available enumerate FloatTensor assign LongTensor MaxIoUAssigner LongTensor FloatTensor assign MaxIoUAssigner Tensor FloatTensor assign LongTensor MaxIoUAssigner LongTensor FloatTensor assign MaxIoUAssigner empty LongTensor FloatTensor assign MaxIoUAssigner Tensor empty assign empty MaxIoUAssigner LongTensor assign PointAssigner FloatTensor LongTensor assign PointAssigner FloatTensor assign PointAssigner FloatTensor FloatTensor assign LongTensor ApproxMaxIoUAssigner FloatTensor assign LongTensor ApproxMaxIoUAssigner FloatTensor assign empty ApproxMaxIoUAssigner assign empty ApproxMaxIoUAssigner random LongTensor FloatTensor get_extra_property assign CenterRegionAssigner assign CenterRegionAssigner LongTensor FloatTensor LongTensor FloatTensor assign float CenterRegionAssigner LongTensor FloatTensor assign CenterRegionAssigner float long int getenv isinstance isinstance data hasattr zeros_like allclose isinstance block BasicBlock randn dict block Bottleneck randn randn ResLayer range layer len isinstance check_norm_state randn ResNet stem model is_block init_weights parameters getattr modules is_norm train range ResNetV1d dict BottleneckX block randn randn model ResNeXt is_block init_weights modules train model RegNet randn init_weights train dict block randn Bottle2neck randn model Res2Net is_block init_weights modules train join dirname join list _get_config_directory model print glob build_detector build_optimizer train_cfg _check_roi_head roi_head test_cfg fromfile optimizer pop join get _get_config_directory print Compose astype float32 dict train_pipeline test_pipeline fromfile randint _check_mask_head mask_iou_head grid_points mask_head bbox_head bbox_roi_extractor mask_roi_extractor grid_roi_extractor _check_roi_extractor _check_bbox_head with_mask ModuleList isinstance get isinstance ModuleList zip get isinstance ModuleList zip get MagicMock CLASSES close NamedTemporaryFile dataset_class items list defaultdict MagicMock ConcatDataset values cumsum CustomDataset ceil set mean ClassBalancedDataset append randint max RepeatDataset len fromfile join _get_config_directory Config deepcopy model _get_config_module train_cfg test_cfg pop _get_detector_cfg _demo_mm_inputs build_detector forward pop skip _get_detector_cfg _demo_mm_inputs build_detector forward cuda pop _get_detector_cfg _demo_mm_inputs item build_detector float forward pop backward _get_detector_cfg requires_grad_ _demo_mm_inputs build_detector forward pop _get_detector_cfg _demo_mm_inputs build_detector forward T RandomState LongTensor FloatTensor rand BitmapMasks append randint range clip ndarray FloatTensor float32 dict cast_tensor_type int32 array model ones ExampleModule is_available cuda model ones ExampleModule is_available cuda Config sum AnchorHead dict forward loss Config sum FSAFHead dict is_available forward cuda loss Config sum dict cuda is_available forward GuidedAnchorHead loss Config loss _dummy_bbox_sampling forward rand get_targets dict BBoxHead sum bbox2roi print refine_bboxes _demodata_refine_boxes BBoxHead int random_boxes group_items astype from_numpy ensure_rng numpy randint empty long cat Config FCNMaskHead mask_iou_head _dummy_bbox_sampling forward rand get_targets dict MaskIoUHead randint sum loss cat build_sampler rand dict build_assigner assign sample range append dict Tensor build_loss long append randint range randint min BitmapMasks dummy_raw_bitmap_masks empty dummy_raw_bitmap_masks BitmapMasks array rescale dummy_raw_bitmap_masks BitmapMasks array resize dummy_raw_bitmap_masks BitmapMasks flip dummy_raw_bitmap_masks BitmapMasks pad dummy_raw_bitmap_masks BitmapMasks crop array dummy_raw_bitmap_masks BitmapMasks dummy_bboxes crop_and_resize randint dummy_raw_bitmap_masks BitmapMasks expand dummy_raw_bitmap_masks BitmapMasks areas dummy_raw_bitmap_masks BitmapMasks to_ndarray dummy_raw_bitmap_masks BitmapMasks to_tensor dummy_raw_bitmap_masks BitmapMasks dummy_raw_bitmap_masks BitmapMasks enumerate BitmapMasks PolygonMasks dummy_raw_polygon_masks uint8 rescale array PolygonMasks dummy_raw_polygon_masks uint8 PolygonMasks stack resize array dummy_raw_polygon_masks flip PolygonMasks dummy_raw_polygon_masks PolygonMasks crop array dummy_raw_polygon_masks pad PolygonMasks dummy_raw_polygon_masks dummy_bboxes crop_and_resize randint PolygonMasks dummy_raw_polygon_masks areas PolygonMasks dummy_raw_polygon_masks to_bitmap PolygonMasks dummy_raw_polygon_masks to_ndarray PolygonMasks dummy_raw_polygon_masks to_tensor PolygonMasks dummy_raw_polygon_masks PolygonMasks dummy_raw_polygon_masks PolygonMasks dummy_raw_polygon_masks enumerate sum_cell SumCell randn ConcatCell concat_cell randn GlobalPoolingCell gp_cell randn randn interpolate _resize max_pool2d BaseMergeCell FPN isinstance num_outs modules fpn_model range nms FloatTensor float64 astype float32 DoubleTensor array nms print astype float32 skip device_count to array range from_numpy nms_match zeros array range len dict len range named_parameters param_groups parameters list enumerate check_optimizer list param_groups ExampleDuplicateModel PseudoDataParallel ExampleModel dict DataParallel parameters enumerate named_parameters is_available optim_constructor check_default_optimizer DefaultOptimizerConstructor check_optimizer build_optimizer_constructor param_groups ExampleModel dict parameters optim_constructor enumerate check_optimizer build_optimizer ExampleModel dict check_default_optimizer Config sum PISARetinaHead dict forward loss Config sum PISASSDHead dict forward loss Config forward_train dict PISARoIHead sum LongTensor FloatTensor RandomSampler assign MaxIoUAssigner sample Tensor FloatTensor RandomSampler assign MaxIoUAssigner sample empty long LongTensor FloatTensor RandomSampler assign MaxIoUAssigner sample empty roi_head _get_detector_cfg LongTensor FloatTensor OHEMSampler _context_for_ohem assign MaxIoUAssigner sample Tensor LongTensor FloatTensor OHEMSampler _context_for_ohem assign MaxIoUAssigner sample Tensor empty LongTensor FloatTensor OHEMSampler _context_for_ohem assign MaxIoUAssigner sample Tensor empty random range MaxIoUAssigner LongTensor FloatTensor _context_for_ohem assign ScoreHLRSampler sample Tensor empty FloatTensor float64 astype float32 DoubleTensor soft_nms array params_to_string assert_equal wrapper ref product randn backward Conv2d OrderedDict requires_grad_ eval manual_seed wrapper ref product randn backward min OrderedDict eval manual_seed ConvTranspose2d wrapper ref product randn MaxPool2d OrderedDict wrapper ref product randn backward OrderedDict eval manual_seed Linear pop join deepcopy dict shape dirname resize_module imread build_from_cfg join deepcopy flip_module dict shape dirname imread build_from_cfg join crop_module dict shape dirname create_random_bboxes imread build_from_cfg join deepcopy crop_module reshape dict shape array dirname create_random_bboxes imread build_from_cfg mode join deepcopy dict shape dirname transform resize_module imread build_from_cfg join deepcopy dict shape array dirname transform imread build_from_cfg load dict normalize albu_transform build_from_cfg list std print argmin mean array append argmax keys include_outliers enumerate arange backend max out show list title savefig legend gca append plot concatenate cla keys enumerate json_logs switch_backend print style xlabel set_xticks set_style array len add_argument add_parser add_argument add_parser ArgumentParser add_plot_parser add_subparsers add_time_parser zip json_logs load_json_logs add_argument model fuse_module build_detector fromfile build_dataset get build_dataloader wrap_fp16_model synchronize perf_counter test eval enumerate print load_checkpoint fuse_conv_bn MMDataParallel fromfile train update imshow_det_bboxes train ProgressBar retrieve_data_cfg skip_type len subplot plot insert xlabel close ylabel xlim shape ylim title vstack figure legend savefig zeros fill_between range len deepcopy evaluate COCOeval print createIndex accumulate getImgIds append getCatIds enumerate deepcopy evaluate COCOeval print makeplot COCO accumulate getImgIds recThrs loadRes dirname vstack getCatIds enumerate makedirs analyze_results ann result types ones size add from_numpy zeros from_numpy add load convert_conv_fc print len set OrderedDict dict save range convert_bn enumerate src convert dst depth Parameter eps reshape running_mean bias sqrt weight running_var named_children isinstance fuse_conv_bn Conv2d Identity save_checkpoint out forward_dummy hasattr tuple get_model_complexity_info shape cuda options merge_from_dict load decode rstrip save Popen in_file process_checkpoint out_file get_available_passes optimize apply passes modules save export_onnx_model printable_graph empty isinstance graph print replace add print replace add print int add split items list convert_reslayer convert_head startswith convert_stem zeros _print load list print_coco_results isinstance print mean zeros keys enumerate len load list isinstance print mean zeros keys enumerate len print get_coco_style_results get_voc_style_results load append replace enumerate task get_results filename str local_rank tmpdir launcher MMDistributedDataParallel format_only show get_dist_info format_results gpu_collect dump CLASSES init_dist single_gpu_test evaluate multi_gpu_test show_dir show_score_thr list evaluate COCOeval summarize is_str COCO accumulate getImgIds loadRes stats load eval_map CLASSES img_norm_cfg size collect_results rstrip tensor broadcast list get_dist_info mkdtemp encode append range dump bytearray zip load join barrier extend rmtree mkdir_or_exist full set_random_seed coco_eval_with_return final_prints seed corruptions voc_eval_with_return final_prints_aggregate obj_from_dict workers_per_gpu insert iou_thr results2json deepcopy coco severities dict add_mutually_exclusive_group localtime autoscale_lr abspath train_detector basename strftime get_root_logger work_dir append val resume_from info gpu_ids join collect_env mkdir_or_exist pipeline isinstance close NamedTemporaryFile reg_class_agnostic fromfile reshape size cat reshape reshape pop format replace search parse_config truncate_reg_channel reorder_cls_channel truncate_cls_channel num_classes append join print glob print track_progress track_parallel_progress int decode asarray basename area id dict dirname unique append imread toBbox pop dump labels dict append gt_dir list items img_dir cityscapes_path int parse findall text getroot append zeros array find join list print track_progress extend zip list_from_file isdir devkit_path cvt_annotations | # CDeC-Net [](https://paperswithcode.com/sota/table-detection-on-icdar2013-1?p=cdec-net-composite-deformable-cascade-network) [](https://github.com/open-mmlab/mmdetection) CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images Paper Link: [ieeexplore](https://ieeexplore.ieee.org/document/9411922) | [arXiv](https://arxiv.org/abs/2008.10831) | [Research Gate](https://www.researchgate.net/publication/343877463_CDeC-Net_Composite_Deformable_Cascade_Network_for_Table_Detection_in_Document_Images) | [CVIT, IIIT-H](http://cvit.iiit.ac.in/usodi/cdec-net.php) ## Introduction CDeC-Net is an end-to-end network for detecting tables in document images. The network consists of a multistage extension of Mask R-CNN with a dual backbone having deformable convolution for detecting tables varying in scale with high detection accuracy at higher IoU threshold. CDeC-Net achieves state-of-the-art results on various publicly available benchmark datasets. The code is implemented in PyTorch using <a href="https://github.com/open-mmlab/mmdetection">MMdetection</a> framework (Version 2.0.0). ## Release Notes: | 2,944 |
meder411/MappedConvolutions | ['depth estimation'] | ['Mapped Convolutions'] | package/layers/util/icosphere_functions.py package/tests/test_mapped_pooling.py package/layers/util/read_write.py package/tests/test_standard_convolution.py package/layers/util/util.py package/layers/loss/berhu_loss_layer.py package/layers/util/__init__.py package/tests/parameters.py examples/resample_rect_to_sphere.py package/layers/nn/convolution_layer.py package/layers/util/grids.py package/tests/test_mapped_transposed_convolution.py package/layers/metrics/__init__.py package/tests/test_mapped_convolution.py package/layers/loss/__init__.py package/layers/nn/mapped_max_pooling_layer.py package/layers/util/conversions.py profile/plot_profile.py package/layers/nn/mapped_transposed_convolution_layer.py package/layers/nn/__init__.py package/layers/nn/resample_layer.py examples/resample_cube_to_sphere_with_depth.py package/layers/nn/mapped_convolution_layer.py package/layers/nn/transposed_convolution_layer.py package/tests/utils.py package/setup.py package/layers/util/spherical_projections.py package/layers/util/network_training.py package/layers/nn/mapped_avg_pooling_layer.py package/layers/__init__.py package/layers/nn/layer_utils.py package/layers/util/point_functions.py package/tests/test_resample.py package/layers/util/mesh_functions.py package/layers/metrics/depth_metrics.py profile/profile_mapped_convolution.py extension BerHuLoss sq_rel_error lin_rms_sq_error log_rms_sq_error delta_inlier_ratio abs_rel_error Convolution ConvolutionFunction check_input_map_shape check_interp_weights_dim check_interp_weights_shape check_type check_args check_input_dim check_sample_map_dim check_sample_map_shape check_input_shape _ntuple MappedAvgUnpoolFunction MappedAvgPoolFunction MappedAvgPool MappedAvgUnpool MappedConvolution MappedConvolutionFunction MappedMaxPool MappedMaxPoolFunction MappedMaxUnpoolFunction MappedMaxUnpool MappedTransposedConvolution MappedTransposedConvolutionFunction ResampleFunction Resample UnresampleFunction Unresample TransposedConvolution TransposedConvolutionFunction convert_spherical_to_cube convert_spherical_to_3d convert_cube_to_3d convert_3d_to_cube convert_cubemap_tuple_to_image convert_3d_to_spherical convert_spherical_to_image get_equirectangular_grid_resolution equirectangular_meshgrid cube_meshgrid_resolution cartesian_meshgrid cube_meshgrid vertices_to_equirectangular_resample_map sphere_to_samples_resample_map image_to_sphere_resample_map vertex_to_vertex_kernel_map sphere_to_cube_resample_map faces_to_equirectangular_resample_map compute_num_vertices resample_rgb_to_vertex sphere_to_image_resample_map resample_vertex_to_rect gnomonic_kernel_from_sphere gnomonic_kernel resample_cube_to_vertex generate_icosphere get_conv_operator_samples vertices_to_faces faces_to_vertices get_downsample_map AverageMeter xavier_init NetworkManager knn write_ply load_optimizer writeHeatmap load_rgb_pts mkdirs save_checkpoint load_partial_model read_exr writeImage reverse_gnomonic_projection_map reverse_cubemap_projection_map equirectangular_meshgrid cube_to_3d cube_to_rect get_reverse_cube_projection_map grid_projection_map cubemap_idx_to_xy reverse_equirectangular_projection_map get_perspective_projection_map perspective_projection_map get_projection_map perspective_resampling batched_index_select time_cuda batched_scatter sample_map3 gradients transposed_input_2x3 sample_map4 sample_map6 sample_map25 input_4x7 sample_map7 sample_map2 transposed_ones weights_0_25 transposed_weights_unit interp_weights0 sample_map5 sample_map0 input_4x5 input_ones input_3x4 sample_map8 transposed_input_2x2 weights_unit sample_map1 bias transposed_weights_0_25 test_layer_weights_cuda test_bilinear_interpolation_cpu test_out_of_bounds_sampling_cuda test_downsampling_with_integer_sampling_cuda test_downsampling_with_bilinear_interpolation_cuda test_downsampling_with_bilinear_interpolation_cpu test_integer_sampling_cuda test_downsampling_with_integer_sampling_cpu test_out_of_bounds_sampling_cpu test_bilinear_interpolation_cuda test_layer_weights_cpu test_integer_sampling_cpu test_max_pool_integer_downsampling_cpu test_avg_pool_integer_sampling_cuda test_max_pool_integer_sampling_cpu test_max_pool_bilinear_interpolation_sampling_cpu test_max_pool_bilinear_downsampling_cuda test_avg_pool_integer_sampling_cpu test_max_pool_integer_sampling_cuda test_max_pool_bilinear_interpolation_sampling_cuda test_max_pool_integer_downsampling_cuda test_max_pool_bilinear_downsampling_cpu test_integer_sampling_non_square_cpu test_bilinear_interpolation_sampling_cuda test_integer_sampling_cuda test_integer_sampling_non_square_cuda test_bilinear_interpolation_sampling_cpu test_integer_sampling_cpu test_unresample_nearest_integer_sampling_cpu test_unresample_bilinear_real_sampling_cuda test_unresample_bilinear_integer_sampling_cuda test_unresample_nearest_out_of_bounds_sampling_cpu test_unresample_bispherical_real_sampling_cuda test_unresample_bispherical_real_sampling_cpu test_unresample_bilinear_out_of_bounds_sampling_cuda test_unresample_bispherical_integer_sampling_cuda test_unresample_weighted_sampling_cpu test_unresample_weighted_sampling_cuda test_unresample_bispherical_out_of_bounds_sampling_cpu test_unresample_nearest_real_sampling_cpu test_unresample_bilinear_real_sampling_cpu test_unresample_nearest_real_sampling_cuda test_unresample_bispherical_out_of_bounds_sampling_cuda test_unresample_bispherical_integer_sampling_cpu test_unresample_bilinear_integer_sampling_cpu test_unresample_nearest_out_of_bounds_sampling_cuda test_unresample_nearest_integer_sampling_cuda test_unresample_bilinear_out_of_bounds_sampling_cpu test_standard_transposed_conv_cuda test_standard_conv_cuda test_standard_conv_cpu test_standard_transposed_conv_cpu mapped_resample_test print_test_header time_cuda save_grad print_group_results mapped_pool_test standard_conv_test standard_transposed_conv_test print_device_header print_report print_group_header print_device_result mapped_conv_test mapped_transposed_conv_test pixel_shuffle_resampling profile is_available check_interp_weights_dim check_interp_weights_shape check_type check_input_dim check_sample_map_dim check_sample_map_shape check_input_shape clone pi sqrt atan2 cos sin float abs long float float pi get_equirectangular_grid_resolution expand zeros repeat range expand float range layer Unresample sphere_to_image_resample_map get_device to is_cuda layer view Resample sphere_to_image_resample_map index_add_ get_device zeros to cuda is_cuda layer view sphere_to_cube_resample_map Resample index_add_ get_device zeros to cuda is_cuda view asin cos pi atan2 sqrt sin zeros atan range num_vertices format print exit num_faces get_face_barycenters convert_3d_to_spherical get_vertices get_vertex_resolution stack get_planar_conv_operator_from_samples gnomonic_kernel_from_sphere stack get_planar_conv_operator_from_samples equirectangular_meshgrid convert_cube_to_3d stack get_planar_conv_operator_from_samples cube_meshgrid convert_3d_to_spherical stack get_planar_conv_operator_from_samples format view print exit get_face_barycenters convert_3d_to_spherical get_vertices convert_3d_to_spherical get_face_barycenters get_conv_operator_deg2 get_conv_operator_deg1 get_face_barycenters convert_3d_to_spherical view Unresample float get_device to is_cuda Unresample float get_device to is_cuda data fill_ bias xavier_normal_ weight __name__ constant_ makedirs join basename copyfile save find update load_state_dict state_dict update items list load_state_dict to is_tensor values state_dict read vstack view describe astype write fromarrays vstack int32 append FLOAT reshape channels InputFile PixelType empty array enumerate imsave cmap get_cmap max imsave reverse_gnomonic_projection_map convert_spherical_to_image grid_projection_map reverse_equirectangular_projection_map reverse_cubemap_projection_map convert_3d_to_cube convert_spherical_to_3d cubemap_idx_to_xy perspective_projection_map zeros range equirectangular_meshgrid view arange view meshgrid zeros float range pi equirectangular_meshgrid view cos pi zeros range equirectangular_meshgrid view asin cos pi atan2 sqrt sin zeros atan range arange to get_device meshgrid normalize float range view cube_meshgrid_resolution convert_cube_to_3d cube_meshgrid zeros float convert_3d_to_spherical range float radians tan cos dstack from_numpy sqrt linspace sin meshgrid convert_3d_to_spherical synchronize time func shape list expand shape list zeros_like expand double zeros tensor sample_map0 zeros tensor zeros tensor sample_map2 zeros tensor double tensor tensor tensor assert_allclose double mapped_conv_test in_channels double assert_allclose mapped_conv_test in_channels double assert_allclose mapped_conv_test in_channels double assert_allclose mapped_conv_test in_channels double assert_allclose mapped_conv_test in_channels double assert_allclose mapped_conv_test assert_allclose cuda mapped_conv_test in_channels cuda assert_allclose mapped_conv_test in_channels cuda assert_allclose mapped_conv_test in_channels cuda assert_allclose mapped_conv_test in_channels cuda assert_allclose mapped_conv_test in_channels cuda assert_allclose mapped_conv_test mapped_pool_test double assert_allclose mapped_pool_test double assert_allclose mapped_pool_test double assert_allclose mapped_pool_test double assert_allclose mapped_pool_test double assert_allclose mapped_pool_test cuda assert_allclose mapped_pool_test cuda assert_allclose mapped_pool_test cuda assert_allclose mapped_pool_test cuda assert_allclose mapped_pool_test cuda assert_allclose in_channels mapped_transposed_conv_test assert_allclose double in_channels mapped_transposed_conv_test assert_allclose double in_channels mapped_transposed_conv_test in_channels mapped_transposed_conv_test assert_allclose cuda in_channels mapped_transposed_conv_test assert_allclose cuda in_channels mapped_transposed_conv_test mapped_resample_test double Unresample assert_allclose mapped_resample_test double Unresample assert_allclose mapped_resample_test double Unresample assert_allclose mapped_resample_test double Unresample assert_allclose mapped_resample_test double Unresample assert_allclose mapped_resample_test double Unresample assert_allclose mapped_resample_test double Unresample assert_allclose mapped_resample_test double Unresample assert_allclose mapped_resample_test double Unresample assert_allclose mapped_resample_test double Unresample assert_allclose mapped_resample_test Unresample cuda assert_allclose mapped_resample_test Unresample cuda assert_allclose mapped_resample_test Unresample cuda assert_allclose mapped_resample_test Unresample cuda assert_allclose mapped_resample_test Unresample cuda assert_allclose mapped_resample_test Unresample cuda assert_allclose mapped_resample_test Unresample cuda assert_allclose mapped_resample_test Unresample cuda assert_allclose mapped_resample_test Unresample cuda assert_allclose mapped_resample_test Unresample cuda assert_allclose pytorch_layer fill_ gradcheck my_layer double assert_allclose pytorch_layer fill_ gradcheck my_layer double assert_allclose pytorch_layer fill_ gradcheck my_layer cuda assert_allclose pytorch_layer fill_ gradcheck my_layer cuda assert_allclose is_available all print exit mean cuda time_cuda backward bias gradcheck cuda time_cuda backward bias gradcheck cuda gradcheck time_cuda cuda backward gradcheck time_cuda cuda backward time_cuda fill_ backward gradcheck double cuda time_cuda fill_ backward gradcheck double cuda print print print print print append pad range view | ## UPDATE 5/25/2020 New version of backend and more extensive examples to match paper's results coming soon # Mapped Convolutions **Official PyTorch implementation of the mapped convolution operation** This repository contains the "Mapped Convolutions" library. It is written to be a Python extension to PyTorch and can run on either GPU (needs CUDA) or CPU. ## Set Up I highly recommend using some kind of virtual environment, like [Conda](https://www.anaconda.com/), [virtualenv](https://virtualenv.pypa.io/en/latest/), or [Docker](https://www.docker.com/). ### Dependencies To install the Python dependencies, you can either use the provided Conda YML file (for a Conda environment) or use the `requirements.txt` file for a `pip` installation. For Conda, first [install Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) and then call the command: | 2,945 |
medtray/SeqGAN-vs-MLE-vs-PG-BLEU-vs-ScheduleSampling | ['text generation'] | ['SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient'] | neuroir/eval/__init__.py neuroir/rankers/mtensor.py neuroir/inputters/recommender/__init__.py neuroir/rankers/duet.py plot_real_data_results.py neuroir/rankers/arcii.py neuroir/utils/misc.py bleu_rollout.py neuroir/inputters/multitask/__init__.py loss.py neuroir/utils/copy_utils.py lstm_discriminator.py neuroir/rankers/__init.py plot_disc_results.py neuroir/eval/bleu/bleu_scorer.py neuroir/inputters/ranker/vector.py neuroir/inputters/multitask/data.py mle_real_data.py generator.py neuroir/eval/rouge/__init__.py neuroir/models/ranker.py neuroir/recommender/seq2seq.py neuroir/recommender/hredqs.py neuroir/utils/timer.py neuroir/recommender/layers.py neuroir/rankers/esm.py neuroir/multitask/layers.py plot_results.py neuroir/eval/ltorank.py neuroir/multitask/mnsrf.py neuroir/encoders/encoder.py neuroir/encoders/__init__.py neuroir/config.py neuroir/hyparam.py neuroir/decoders/state.py neuroir/decoders/decoder.py neuroir/modules/maxout.py seq_gan_real_data.py main.py neuroir/modules/global_attention.py neuroir/__init__.py neuroir/utils/logging.py neuroir/eval/squad_eval.py neuroir/multitask/cars.py neuroir/inputters/recommender/vector.py neuroir/objects/session.py neuroir/inputters/constants.py neuroir/eval/rouge/rouge.py neuroir/inputters/recommender/utils.py neuroir/inputters/ranker/utils.py neuroir/inputters/multitask/vector.py neuroir/modules/__init__.py process_real_data.py neuroir/inputters/__init__.py neuroir/utils/__init__.py neuroir/eval/bleu/bleu.py neuroir/models/recommender.py neuroir/rankers/arci.py neuroir/modules/copy_generator.py neuroir/multitask/mmtensor.py neuroir/inputters/multitask/utils.py seq_gan_different_pretrain.py plot_pretrain_effect.py neuroir/inputters/recommender/data.py seq_gan_different_disc.py neuroir/rankers/drmm.py neuroir/rankers/dssm.py discriminator.py neuroir/objects/document.py neuroir/modules/embeddings.py neuroir/recommender/__init.py neuroir/objects/query.py neuroir/rankers/cdssm.py neuroir/decoders/rnn_decoder.py neuroir/modules/util_class.py rollout.py neuroir/models/multitask.py neuroir/inputters/ranker/data.py neuroir/decoders/__init__.py neuroir/inputters/vocabulary.py target_lstm.py neuroir/multitask/__init.py neuroir/inputters/ranker/__init__.py neuroir/models/__init__.py neuroir/eval/bleu/__init__.py data_iter.py neuroir/encoders/rnn_encoder.py neuroir/objects/__init__.py bleu_Rollout DisDataIter GenDataIter Discriminator Generator NLLLoss LSTM_Discriminator Encoder generate_samples GANLoss SS train_epoch MLE WordIndexer AmazonReviewGloveDataset train_ss main PG_BLEU eval_epoch generate_samples show_some_generated_sequences train_epoch test_predict get_word read_file demo main eval_epoch fetch_vocab save_vocab load_from_big_file generate_sentence_from_id load_vocab pad_sentences generate_real_data generate_file_from_sentence get_ids Rollout generate_samples GANLoss train_epoch main eval_epoch generate_samples GANLoss train_epoch main eval_epoch generate_samples show_some_generated_sequences GANLoss train_epoch test_predict get_word read_file demo main eval_epoch TargetLSTM add_model_args update_model_args get_model_args override_model_args str2bool get_model_specific_params DecoderBase RNNDecoderBase DecoderState RNNDecoderState EncoderBase NDCG_at_k recall_at_k MAP precision_at_k MRR normalize_answer metric_max_over_ground_truths regex_match_score f1_score exact_match_score Bleu my_lcs Rouge SortedBatchSampler RankerRecommenderDataset load_words build_word_dict build_word_and_char_dict index_embedding_words load_data vectorize batchify RankerDataset SortedBatchSampler load_words build_word_dict build_word_and_char_dict index_embedding_words load_data vectorize batchify SortedBatchSampler RecommenderDataset load_words build_word_dict build_word_and_char_dict index_embedding_words load_data vectorize batchify Multitask Ranker Recommender CopyGenerator CopyGeneratorCriterion CharEmbedding PositionalEncoding Embeddings GlobalAttention Maxout LayerNorm Elementwise CARS Embedder Decoder Encoder M_MATCH_TENSOR ExactMatchChannel MNSRF Document Query Session ARCI ARCII CDSSM GatingNetwork DRMM DSSM DistributedModel LocalModel DUET ESM ExactMatchChannel MatchTensor Encoder HredQS Embedder Decoder Encoder Seq2seq collapse_copy_scores align make_src_map replace_unknown init_logger AverageMeter Timer int tolist extend range criterion view Variable backward step zero_grad reset forward cuda zero_grad unsqueeze argmax cuda view lstm lin range cat multinomial softmax emb criterion backward Variable reset hidden_dim step init_hidden len NLLLoss deepcopy generate_samples print Adam train_epoch parameters GenDataIter append cuda range eval_epoch NLLLoss deepcopy generate_samples print Adam train_epoch parameters train_ss GenDataIter append cuda range eval_epoch gen_gan_loss zero_grad GenDataIter save forward cuda is_cuda DisDataIter view GANLoss Adam Discriminator append range LongTensor get_reward update_params sample type Rollout eval_epoch NLLLoss int deepcopy generate_samples backward print Variable contiguous train_epoch parameters Tensor step gen_gan_loss zero_grad GenDataIter save forward cuda is_cuda DisDataIter view GANLoss Adam Discriminator append range LongTensor get_reward AmazonReviewGloveDataset update_params sample type eval_epoch NLLLoss int deepcopy generate_samples backward print Variable contiguous train_epoch parameters sentence_bleu read_file Tensor step bleu_Rollout load generate_samples show_some_generated_sequences Generator load_vocab load_state_dict GenDataIter cuda reshape criterion view Variable reset forward cuda int join list view Variable reshape generate_sentence_from_id print reset forward cuda flush append split join arange print generate_sentence_from_id shuffle read_file flush len seed fetch_vocab show load_from_big_file Generator state_dict plot flush join save_vocab get_moses_multi_bleu generate_real_data read_file array len word split append range len append lower word index train_test_split update list sorted set chain append close write open str arange len write choice split open generate_file_from_sentence load_from_big_file save load str add_argument_group register add_argument upper items list upper vars list items list keys info range count_nonzero range count_nonzero range count_nonzero range range Counter split sum values len compile append metric_fn max range len Query join Document tqdm upper append Session split update set tokens Counter index_embedding_words _insert tqdm queries set info most_common documents embedding_file len add load_words Vocabulary max_characters_per_token load_words UnicodeCharsVocabulary src_dict deepcopy tokens tgt_dict LongTensor documents num_candidates shuffle copy_ zero_ label max range len len copy_ zero_ max enumerate use_char_ngram range len size str add_query add_one_query src_vocab LongTensor append range append range len zeros max enumerate len long max enumerate len max range split setFormatter getLogger addHandler StreamHandler Formatter setLevel INFO FileHandler | # SeqGAN-vs-MLE-vs-PG-BLEU-vs-ScheduledSampling-PyTorch A implementation of SeqGAN, MLE, PG-BLEU and Scheduled Sampling in PyTorch. We compare the methods on synthetic dataset, and real dataset consisting of Barack Obama speeches. ## Tested with: * **PyTorch v1 Stable** * Python 3.6 * CUDA at least 8.0 (For GPU) ## Origin The idea is from paper [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient] (https://arxiv.org/pdf/1609.05473.pdf) The code is written in PyTorch with SeqGAN is based on the implementation from https://github.com/ZiJianZhao/SeqGAN-PyTorch ## Runing | 2,946 |
meet-minimalist/A-Neural-Algorithm-of-Artistic-Style-Paper-Implementation | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | tensorflow_vgg/vgg16.py helper.py tensorflow_vgg/utils.py tf_helper.py tensorflow_vgg/vgg16_avg_pool.py post_process_and_display resize_and_rescale_img_style resize_and_rescale_img_content compute_tf_output print_prob load_image load_image2 test Vgg16 Vgg16 print makedirs save isfile expand_dims array open isfile print makedirs save resize expand_dims array open show fromarray squeeze astype imshow save clip makedirs dict reset_default_graph Vgg16 build int imread min resize print imread imread imsave resize | # A-Neural-Algorithm-of-Artistic-Style-Paper-Implementation Tensorflow implementation of paper "A Neural Algorithm of Artistic Style" (https://arxiv.org/abs/1508.06576) #### In this notebook, we'll implement the paper and reconstruct the results of the said paper. The steps of the process is as follows. Also, the notebook is created to facilitate _self-learning_ approach. _Step 1: Preprocessing the input image_ _Step 2: Computing the output for selected layers for the content image and all the layers for style image._ _Step 3: What are loss functions in this problem and computing the loss functions._ _Step 3A: Content Loss for reconstruction of the content image._ _Step 3B: Style Loss for reconstruction of the style from a style image irrespective of content placement of the image._ | 2,947 |
megagonlabs/opiniondigest | ['sentiment analysis', 'aspect based sentiment analysis'] | ['OpinionDigest: A Simple Framework for Opinion Summarization'] | src/prepare.py src/generate.py src/train.py src/models.py src/utils.py src/aggregate.py src/evaluate.py src/beam_search.py Input Entity Extraction NgramBlocking BeamSearchNMT BeamSearch Search DiverseBeamSearch denumericalize TransformerModel LabelSmoothingLoss PositionalEncoding SumEvaluator Prepare Config append join tolist | # OpinionDigest This repository contains the codebase of our paper [OpinionDigest A Simple Framework for Opinion Summarization](https://arxiv.org/abs/2005.01901) accepted at [ACL 2020](https://acl2020.org/). OpinionDigest is an **unsupervised opinion summarization framework** that generates a summary from multiple review documents without requiring any gold-standard summary. OpinionDigest relies on an aspect-based sentiment classification model, which extracts opinions from input reviews, to train a seq2seq model that generates a summary from a set of opinions. This framework enables the user to easily control the output by filtering input opinions using aspects and/or sentiment polarity.  Please see [our paper](https://arxiv.org/abs/2005.01901) for details. Please also try our [online demo](http://extremereader.megagon.info/). This project is a collaboration with the Natural Language Processing Group at the University of Edinburgh ([EdinburghNLP](https://edinburghnlp.inf.ed.ac.uk/)). ## Installation | 2,948 |
meghanathmacha/xPACS | ['anomaly detection'] | ['Explaining Anomalies in Groups with Characterizing Subspace Rules'] | src/KDE/kde.py kderun str list bandwidth exp GridSearchCV to_csv set KernelDensity drop read_csv append array range score_samples fit | # xPACS Explaining Anomalies in Groups with Characterizing Subspace Rules. The work is accepted for publication and will appear in the proceedings of Data Mining and Knowledge Discovery Journal as part of the special issue: Journal Track of ECMLPKDD 2018. ## Website http://cmuxpacs.github.io/ ## Requirements * Requires Matlab and R. * Requires the following matlab packages: ``` cvx ``` | 2,949 |
megvii-model/FunnelAct | ['scene generation', 'semantic segmentation'] | ['Funnel Activation for Visual Recognition'] | resnet/inference.py shufflenetv2/shufflenet_v2.py resnet/test.py resnet/frelu.py hubconf.py shufflenetv2/test.py shufflenetv2/frelu_light.py resnet/train.py shufflenetv2/inference.py shufflenetv2/train.py resnet/resnet.py FReLU main resnext50_32x4d ResNet resnet50 resnext101_32x8d Bottleneck resnet152 resnet34 resnet50_frelu Bottleneck_FReLU_Partial resnet18 BasicBlock Bottleneck_FReLU resnet101 main worker AverageMeter infer worker get_parameters AverageMeter infer main FReLU main shufflenet_v2_x0_5 ShuffleNetV2 ShuffleV2Block main worker AverageMeter infer worker get_parameters AverageMeter infer main load format infer_func model print reshape add_argument Compose IMREAD_COLOR image top_k load_state_dict ArgumentParser zip parse_args imread enumerate Process worker join set_start_method start append range load data format init_process_group model ImageNet infer DataLoader load_state_dict info SequentialSampler update time model AverageMeter astype info enumerate set_log_file save arch makedirs append named_parameters zero_grad SGD save arch set_log_file next range state_dict update train_func param_groups astype Infinite float join int time learning_rate get_parameters AverageMeter RandomSampler reset step steps | # [Funnel Activation for Visual Recognition]() This repository provides MegEngine implementation for "[Funnel Activation for Visual Recognition](https://arxiv.org/pdf/2007.11824.pdf)". <!--  --> <img width="869" height="444" src="figures/frelu.png"/> ## Requirement - MegEngine 0.5.1 (https://github.com/MegEngine/MegEngine) ## Citation If you use these models in your research, please cite: @inproceedings{ma2020funnel, title={Funnel activation for visual recognition}, | 2,950 |
meijieru/crnn.pytorch | ['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 demo.py test/test_utils.py dataset.py tool/convert_t7.py utils.py models/crnn.py lmdbDataset alignCollate randomSequentialSampler resizeNormalize val trainBatch weights_init averager loadData prettyPrint assureRatio strLabelConverter oneHot BidirectionalLSTM CRNN _suite equal utilsTestCase trans_pos torch_to_pytorch torch_layer_serial load_params py_layer_serial normal_ __name__ fill_ data decode batchSize DataLoader IntTensor max crnn view squeeze add iter encode next range averager loadData size eval zip float criterion print Variable min parameters len criterion backward Variable loadData size step zero_grad IntTensor encode next crnn max fill_ size scatter_ long range copy_ str format print size type main size UpsamplingBilinear2d isinstance zip Tensor Iterable addTest TestSuite utilsTestCase append extend append children extend append len split data isinstance concatenate num_layers extend copy_ getattr zip range len load children list state_dict torch_layer_serial save load_params py_layer_serial __name__ enumerate | Convolutional Recurrent Neural Network ====================================== This software implements the Convolutional Recurrent Neural Network (CRNN) in pytorch. Origin software could be found in [crnn](https://github.com/bgshih/crnn) Run demo -------- A demo program can be found in ``demo.py``. Before running the demo, download a pretrained model from [Baidu Netdisk](https://pan.baidu.com/s/1pLbeCND) or [Dropbox](https://www.dropbox.com/s/dboqjk20qjkpta3/crnn.pth?dl=0). This pretrained model is converted from auther offered one by ``tool``. Put the downloaded model file ``crnn.pth`` into directory ``data/``. Then launch the demo by: | 2,951 |
melhousni/DSMNet | ['denoising'] | ['Aerial Height Prediction and Refinement Neural Networks with Semantic and Geometric Guidance'] | nets.py train_ec.py test_dsm.py train_mtl.py utils.py MTL Autoencoder sliding_window correctTile rgb_to_onehot gaussian_kernel genNormals collect_tilenames append str zeros reshape enumerate len CV_64F norm gradient dstack Sobel min max exp pi sqrt linspace meshgrid range | ## DSMNet ### About This repo contains the code and files necessary to reproduce the results published in our [paper](https://arxiv.org/pdf/2011.10697.pdf) 'Height Prediction and Refinement from Aerial Images with Semantic and Geometric Guidance'. Our method relies on a two stage pipeline : First, a multi-task network is used to predict the height, semantic labels and surface normals of an input RGB aerial image. Next, we use a denoising autoencoder to refine our height prediction in order to produce higher quality height maps. Training and testing is conducted on two publicly available datasets : The ISPRS Vaihingen and the IEEE DFC2018.  ### Citation If you find our work useful in your research, please consider citing our [paper](https://arxiv.org/pdf/2011.10697.pdf): ``` @misc{mahdi2020height, title={Height Prediction and Refinement from Aerial Images with Semantic and Geometric Guidance}, author={Elhousni Mahdi and Huang Xinming and Zhang Ziming}, | 2,952 |
melvinwevers/historical_concepts | ['word embeddings'] | ['Using Word Embeddings to Examine Gender Bias in Dutch Newspapers, 1950-1990'] | code/relation.py code/make_dutch_dictionary.py code/parse_emotion_lexicon.py code/aligning_embeddings.py code/extract_names.py code/making_embeddings_newspapers_new.py notebook/helper.py code/preprocessing.py intersection_align_gensim smart_procrustes_align_gensim Sentences iter_file TimestampedSentences iter_load_sentences save_embeddings train_embeddings train_models load_file process_corpus make_df calculate_analogies Relation cossim embedding_bias align_models calculate_vector calc_distance_between_words load_models calculate_vectors quantile_function calc_distance_between_vectors dot intersection_align_gensim syn0norm svd vocab list sort syn0norm set array Vocab keys enumerate join range str train Word2Vec build_vocab save_word2vec_format format replace join str format print save_embeddings train_embeddings range makedirs frozenset format process_corpus print glob to_datetime save_as_line_sentence apply dropna read_csv values list print DataFrame zip append sum range len test_model Relation dot norm int format load_word2vec_format append range range init_sims len cossim calculate_vector mean append enumerate bootstrap | melvinwevers/historical_concepts | 2,953 |
mertozer/mts-clustering | ['time series', 'time series clustering'] | ['Discovering patterns of online popularity from time series'] | python/centroids.py python/kShape.py python/distances.py python/mkShape.py python/mksc.py python/util.py python/mvkShape.py python/ksc.py kShape_centroid ksc_centroid vkShape_centroid dhat_shift_dep sanity_check scale_d NCCc sbd_dep_multi kSC kShape multidim_kSC multidim_kShape multidim_vkShape toy_dataset rand_index T norm dhat_shift_dep print exit matmul shape eigh eye zeros range len T print ones exit zscore matmul sbd_dep_multi shape nan_to_num eigh sqrt eye zeros sum range len T print ones exit zscore matmul sbd_dep_multi shape nan_to_num eigh sqrt eye zeros sum range len ones sanity_check append zeros range norm eps T matmul ones sanity_check append zeros argmax max range ifft int fft conj concatenate log2 ceil abs len print exit norm dhat_shift_dep print reshape argmin rand ksc_centroid shape ceil zeros range len norm print reshape argmin rand sbd_dep_multi shape ceil zeros kShape_centroid range len seed norm dhat_shift_dep print argmin rand ksc_centroid shape ceil zeros range len norm print argmin rand sbd_dep_multi shape ceil zeros kShape_centroid range len time norm print argmin rand sbd_dep_multi shape ceil zeros range len comb sum len print array | # mts-clustering Multivariate extensions to existing partitional time series clustering algorithms presented and implemented in; http://snap.stanford.edu/data/ksc.html http://www.cs.columbia.edu/~jopa/kshape.html Python and Matlab implementations of the algorithms presented in the paper titled "Discovering Patterns of Online Popularity from Time Series" https://arxiv.org/pdf/1904.04994 https://www.public.asu.edu/~mozer/dipm-SC/supp_material.pdf | 2,954 |
mertyg/learning-prototypes | ['multiple instance learning'] | ['Learning Maximally Predictive Prototypes in Multiple Instance Learning'] | run.py experiments.py mil.py utils.py model.py experiment_fn get_data gram_matrix ShapeletGenerator pairwise_dist run_for_dataset convert_to_bags plot_prototypes load_data Softmax model ShapeletGenerator get_data Sigmoid numpy BCEWithLogitsLoss cuda max str list view Adam int64 title savefig plot_prototypes legend append sum CrossEntropyLoss range format plot param_groups close choice output_fn type prototypes pairwise_dist backward print float32 parameters Tensor step array loss len convert_to_bags array load_data t squeeze mm bmm contiguous cat seed plot_convergence run_optimization print x_opt BayesianOptimization astype mean array append std range genfromtxt join loadtxt astype isin show subplots suptitle plot subplots_adjust savefig ceil ravel range | # Learning Maximally Predictive Prototypes in Multiple Instance Learning Mert Yuksekgonul, Ozgur Emre Sivrikaya, Mustafa Gokce Baydogan <br> <br> To appear in Turkish Journal of Electrical Engineering & Computer Sciences 2021. <br> <br> Previously accepted as a poster presentation at the NeurIPS 2019 Sets & Parts Workshop. You can get in touch with me ([email protected]) for any questions. | 2,955 |
mesnico/TERAN | ['information retrieval', 'image retrieval', 'cross modal retrieval'] | ['Fine-grained Visual Textual Alignment for Cross-Modal Retrieval using Transformer Encoders'] | train.py data.py evaluation.py models/loss.py models/teran.py models/visual.py test.py evaluate_utils/ptbtokenizer.py models/utils.py evaluate_utils/dcg.py evaluate_utils/rouge.py utils.py evaluate_utils/spice.py models/text.py evaluate_utils/compute_relevance.py features.py get_loaders CocoDataset get_transform BottomUpFeaturesDataset Collate FlickrDataset get_loader_single get_test_loader get_paths i2t encode_data AverageMeter LogCollector t2i evalrank get_features_extractor HuggingFaceTransformerExtractor FeatureExtractor TextWordIndexesExtractor VGGFeatureExtractor main TextCollator ResnetFeatureExtractor main validate accuracy test save_checkpoint main train cosine_sim get_model dot_sim parallel_worker_init get_dataset compute_relevances_wrt_query my_collate main dcg_score average_precision_score DCG dcg_from_ranking ranking_precision_score ndcg_score ndcg_from_ranking PTBTokenizer my_lcs Rouge Spice cosine_sim PermInvMatchingLoss AlignmentContrastiveLoss Contrastive order_sim dot_sim ContrastiveLoss JointTextImageTransformerEncoder TERAN EncoderTextGRU EncoderTextBERT EncoderText GatedAggregation Aggregator generate_square_subsequent_mask l2norm PositionalEncodingText PositionalEncodingImageGrid PositionalEncodingImageBoxes find_nhead EncoderImageFull EncoderImagePrecomp EncoderImage GCNVisualReasoning TransformerPostProcessing load join FlickrDataset DataLoader BottomUpFeaturesDataset CocoDataset Compose Normalize get_paths Collate get_transform get_loader_single get_paths Collate get_transform get_loader_single update time format logging AverageMeter LogCollector word_embeddings eval enumerate len i2t encode_data print tuple dataset DCG flatten get_test_loader t2i load_state_dict save empty_cache get_model range len flatten floor cuda values list sim_function mm order_sim append range concatenate mean trange zeros reshape min t median numpy len numpy floor cuda values list order_sim shape range concatenate mean stack trange zeros T min t median mm len HuggingFaceTransformerExtractor extract get_db_file format get_features_extractor print finetuned eval isfile input method split load evalrank checkpoint workers validate LinearWarmup save_checkpoint ArgumentParser dataset cuda max basicConfig StepLR load_model Adam DCG load_state_dict parse_args range SummaryWriter get_loaders resume is_available train num_epochs get_parameters add_argument get_model len validate model clip_grad_norm_ zero_grad LogCollector len sum update val format dampen info enumerate time backward AverageMeter parameters tb_log Eiters step add_scalar i2t encode_data Eiters t2i info add_scalars add_scalar i2t encode_data print tuple flatten Eiters t2i info add_scalars range add_scalar copyfile save topk size t eq mul_ expand_as append sum max TERAN expand_dims norm score DataLoader any compute_score Rouge append dataset array Spice enumerate FlickrDataset get_paths CocoDataset list zip join makedirs get_dataset DataLoader memmap quit sum take unique sum range unique len log2 take arange len dcg_score log2 asarray arange len dcg_from_ranking len max range len l2norm size expand print EncoderTextBERT EncoderTextGRU sqrt div float transpose masked_fill GCNVisualReasoning EncoderImagePrecomp EncoderImageFull TransformerPostProcessing list range reversed | # Transformer Encoder Reasoning and Alignment Network (TERAN) Code for the cross-modal visual-linguistic retrieval method from "Fine-grained Visual Textual Alignment for Cross-modal Retrieval using Transformer Encoders", accepted for publication in ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) [[Pre-print PDF](https://arxiv.org/abs/2008.05231)]. This work is an extension to our previous approach TERN accepted at ICPR 2020. This repo is built on top of [VSE++](https://github.com/fartashf/vsepp) and [TERN](https://github.com/mesnico/TERN). <p align="center"> <b>Fine-grained Alignment for Precise Matching</b> <br> <br> <img src="figures/alignment.jpg" width="80%"> </p> <p align="center"> <b>Retrieval</b> <br> <br> | 2,956 |
meta-toolkit/meta | ['document classification', 'information retrieval'] | ['MeTA: A Unified Toolkit for Text Retrieval and Analysis'] | contrib/YouCompleteMe/ycm_extra_conf.py GetCompilationInfoForFile GetDatabase MakeRelativePathsInFlagsAbsolute IsHeader FlagsForFile DirectoryOfThisScript join DirectoryOfThisScript exists append join startswith set join replace compiler_flags_ GetDatabase IsHeader append DirectoryOfThisScript MakeRelativePathsInFlagsAbsolute compiler_flags_ GetCompilationInfoForFile compiler_working_dir_ | # MeTA: ModErn Text Analysis Please visit our [web page][meta-website] for information and tutorials about MeTA! ### Build Status (by branch) - master: [](https://travis-ci.org/meta-toolkit/meta) [](https://ci.appveyor.com/project/skystrife/meta) - develop: [](https://travis-ci.org/meta-toolkit/meta) | 2,957 |
metachenyiyan/BreezeForest | ['density estimation', 'normalising flows'] | ['Graphical Normalizing Flows', 'Block Neural Autoregressive Flow'] | model/distribution2d.py multi_dataset_demo.py model/TreeLayer.py one_dataset_demo.py demo_functions.py model/BreezeForest.py model/tools.py generate_sample view_init_dis_sample view_init_dis_sample2 demo BreezeForest BUTTERFLY sample2d ButterFlyDis GAUSSIANS BLOBS SPIRALS MOONS CIRCLE make_butterfly CHECKERBOARD actinorm_init_bias logit CdfGaussian get_epsilons Relu sigmoid actinorm_init_scale Sigmoid ActiId bisection Logit TreeLayer BreezeForest add_ zero_grad train_forward DataLoader ReduceLROnPlateau show ones generate_sample Adam view_init_dis_sample iter append next range format plot mean backward print dict mul_ step std show DataLoader numpy plot set_title plot DataLoader set_visible gca numpy eval RandomState randn rand pi sqrt floor vstack sin append randint array range ButterFlyDis exp abs sign ones_like zeros_like sign inc_func Normal clamp_ icdf append pow range shape list view list clamp shape sqrt tensor | <p align="center"> <img width="1000" height="600" src="https://github.com/metachenyiyan/BreezeForest/blob/master/results/ppt_cover.png" title="cover image showing neuralnet and BreezeForest" > </p> <br/> # BreezeForest > An efficient autoregressive flow based generative model, proven to be universal density estimator. ## Setup: ```shell $ pip install requirements ``` | 2,958 |
meteorshowers/hed-pytorch | ['boundary detection', 'edge detection'] | ['Holistically-Nested Edge Detection'] | train_hed.py functions.py models.py utils.py data_loader.py prepare_image BSDSLoader prepare_image_cv2 cross_entropy_loss sigmoid_cross_entropy_loss l2_regression_loss weighted_cross_entropy_loss weighted_nll_loss upsample_filt make_bilinear_weights upsample interp_surgery HED crop weights_init main train test load_vgg16pretrain Averagvalue load_pretrained save_checkpoint Logger weights_init load_fsds_caffe transpose zeros_like transpose binary_cross_entropy_with_logits float long numel float long numel binary_cross_entropy mul squeeze nll_loss numel float long mul criterion squeeze numel float long CrossEntropyLoss float squeeze mul mse_loss int round zeros upsample_filt from_numpy zeros abs range cuda SGD DataLoader save_checkpoint Logger cuda load_vgg16pretrain StepLR apply load_state_dict append range maxepoch format test start_epoch HED resume flush load BSDSLoader join print named_parameters isfile train step model zero_grad save_checkpoint save_image cuda shape append maxepoch range update itersize format size cross_entropy_loss item enumerate join time backward print Averagvalue makedirs zeros step len fromarray join uint8 model print len astype shape eval save zeros save_image numpy cuda range enumerate makedirs isinstance Conv2d normal_ zero_ weight constant_ save load format print load_state_dict isfile list state_dict squeeze from_numpy load_state_dict split loadmat keys len list state_dict print reshape squeeze from_numpy load_state_dict split loadmat keys len | ### pytorch code for Holistically-Nested Edge Detection Thanks to <a href="https://github.com/zeakey">zeakey's</a> help. Created by XuanyiLi, if you have any problem in using it, please contact:[email protected]. The best result of my pytorch model is 0.772 ODS F-score now. #### my model result the following are the side outputs and the prediction example SGD no tunelr 1e-8:  Adam no tunelr 1e-4:  | 2,959 |
mfelice/imeasure | ['grammatical error detection', 'grammatical error correction'] | ['Towards a standard evaluation method for grammatical error detection and correction'] | ieval.py candgen.py align.py m2_to_ixml.py | # I-measure This repository contains a Python implementation of the **I-measure**, a metric used for evaluating grammatical error correction systems. A full description of the method can be found in the following paper, which should be cited whenever you use the script in your work: > Mariano Felice and Ted Briscoe. 2015. [**Towards a standard evaluation method for grammatical error detection and correction**](http://www.cl.cam.ac.uk/~mf501/pub/docs/2015-naacl.pdf). In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2015), Denver, CO. Association for Computational Linguistics. (To appear) # Requirements These scripts were coded in Python 2.7 and require the `xml.etree.cElementTree` library (usually shipped with Python by default). # Usage ## Simple usage You can evaluate a system's output by issuing the following command: `python ieval.py -hyp:system_output_file -ref:gold_standard_file` where *system_output_file* is the plain-text tokenised output generated by an error correction system and *gold_standard_file* is an XML-formatted gold-standard file containing each source sentence with its errors and corrections. You can test the script using the example files provided: | 2,960 |
mflames0327/DF-MOUTH | ['face swapping'] | ['DeepFaceLab: Integrated, flexible and extensible face-swapping framework'] | models/Model_XSeg/Model.py mainscripts/VideoEd.py XSegEditor/QImageDB.py merger/MergerScreen/MergerScreen.py mainscripts/dev_misc.py merger/InteractiveMergerSubprocessor.py samplelib/SampleGeneratorImageTemporal.py core/leras/models/__init__.py samplelib/Sample.py core/joblib/__init__.py core/imagelib/estimate_sharpness.py core/joblib/MPFunc.py core/leras/layers/DenseNorm.py core/leras/nn.py samplelib/SampleProcessor.py core/leras/archis/__init__.py core/leras/layers/Conv2D.py core/stdex.py core/imagelib/text.py core/leras/models/PatchDiscriminator.py core/leras/__init__.py models/Model_XSeg/__init__.py core/leras/layers/DepthwiseConv2D.py core/imagelib/equalize_and_stack_square.py samplelib/__init__.py core/imagelib/sd/draw.py core/leras/optimizers/AdaBelief.py core/randomex.py core/joblib/MPClassFuncOnDemand.py core/leras/optimizers/__init__.py core/leras/initializers/__init__.py mainscripts/Extractor.py mainscripts/Sorter.py core/mathlib/__init__.py core/leras/optimizers/OptimizerBase.py core/imagelib/draw.py samplelib/SampleGeneratorBase.py core/leras/layers/BatchNorm2D.py core/leras/layers/ScaleAdd.py XSegEditor/QCursorDB.py models/__init__.py core/joblib/SubprocessGenerator.py DFLIMG/DFLIMG.py core/leras/layers/__init__.py models/ModelBase.py facelib/FaceEnhancer.py core/joblib/ThisThreadGenerator.py XSegEditor/QIconDB.py core/imagelib/blursharpen.py merger/MergeMasked.py core/cv2ex.py models/Model_Quick96/__init__.py main.py core/leras/archis/ArchiBase.py localization/localization.py facelib/LandmarksProcessor.py core/leras/layers/Dense.py models/Model_SAEHD/__init__.py mainscripts/Merger.py mainscripts/XSegUtil.py core/imagelib/filters.py XSegEditor/QStringDB.py core/leras/initializers/CA.py core/imagelib/__init__.py core/leras/models/ModelBase.py samplelib/SampleGeneratorFaceTemporal.py core/leras/layers/InstanceNorm2D.py facelib/S3FDExtractor.py samplelib/SampleLoader.py samplelib/SampleGeneratorFaceXSeg.py core/qtex/qtex.py DFLIMG/__init__.py core/leras/optimizers/RMSprop.py core/structex.py core/leras/layers/BlurPool.py facelib/__init__.py core/leras/archis/DeepFakeArchi.py core/imagelib/SegIEPolys.py mainscripts/FacesetEnhancer.py merger/FrameInfo.py models/Model_SAEHD/Model.py core/mplib/__init__.py samplelib/SampleGeneratorFacePerson.py merger/MergerScreen/__init__.py core/imagelib/common.py core/imagelib/sd/__init__.py core/leras/layers/AdaIN.py core/leras/layers/LayerBase.py facelib/FANExtractor.py core/leras/models/XSeg.py samplelib/SampleGeneratorFace.py merger/MergeAvatar.py core/pathex.py core/leras/layers/Conv2DTranspose.py core/qtex/QXMainWindow.py merger/__init__.py core/imagelib/morph.py core/joblib/SubprocessorBase.py mainscripts/Trainer.py samplelib/PackedFaceset.py core/mathlib/umeyama.py mainscripts/Util.py core/interact/__init__.py core/leras/layers/FRNorm2D.py core/leras/layers/Saveable.py core/leras/layers/TLU.py facelib/XSegNet.py core/mplib/MPSharedList.py core/leras/models/CodeDiscriminator.py facelib/FaceType.py XSegEditor/XSegEditor.py merger/MergerConfig.py core/leras/ops/__init__.py core/interact/interact.py core/leras/device.py core/imagelib/sd/calc.py core/imagelib/reduce_colors.py samplelib/SampleGeneratorImage.py localization/__init__.py DFLIMG/DFLJPG.py core/qtex/__init__.py core/qtex/QSubprocessor.py core/qtex/QXIconButton.py samplelib/SampleGeneratorFaceCelebAMaskHQ.py core/imagelib/warp.py core/osex.py core/imagelib/color_transfer.py models/Model_Quick96/Model.py process_dev_test process_merge process_videoed_cut_video process_train process_faceset_enhancer process_xsegeditor process_xsegapply process_xsegremove process_xsegremovelabels process_videoed_video_from_sequence process_xsegfetch process_util process_extract fixPathAction process_videoed_extract_video process_sort bad_args process_videoed_denoise_image_sequence cv2_imwrite cv2_resize cv2_imread set_process_dpi_aware get_screen_size set_process_lowest_prio get_image_paths move_all_files write_bytes_safe get_first_file_by_stem get_image_unique_filestem_paths get_all_dir_names get_file_paths delete_all_files scantree get_paths get_all_dir_names_startswith random_normal suppress_stdout_stderr struct_unpack LinearMotionBlur blursharpen _scale_array color_transfer color_transfer_idt color_transfer_mkl reinhard_color_transfer lab_image_stats linear_color_transfer channel_hist_match color_transfer_mix color_transfer_sot color_hist_match overlay_alpha_image cut_odd_image normalize_channels draw_polygon draw_rect equalize_and_stack_square compute _calculate_sharpness_metric marziliano_method get_block_contrast _simple_thinning estimate_sharpness is_edge_block sobel apply_random_motion_blur apply_random_rgb_levels apply_random_hsv_shift apply_random_bilinear_resize apply_random_gaussian_blur morphTriangle morph_by_points applyAffineTransform reduce_colors SegIEPolys SegIEPolyType SegIEPoly get_text_image draw_text_lines draw_text _get_pil_font get_draw_text_lines warp_by_params gen_warp_params dist_to_edges random_circle_faded circle_faded InteractBase InteractColab InteractDesktop MPClassFuncOnDemand MPFunc SubprocessGenerator Subprocessor ThisThreadGenerator Devices Device nn ArchiBase DeepFakeArchi CAInitializerSubprocessor initializers AdaIN BatchNorm2D BlurPool Conv2D Conv2DTranspose Dense DenseNorm DepthwiseConv2D FRNorm2D InstanceNorm2D LayerBase Saveable ScaleAdd TLU CodeDiscriminator ModelBase UNetPatchDiscriminator PatchDiscriminator XSeg dssim concat average_gv_list resize2d_bilinear flatten rgb_to_lab resize2d_nearest space_to_depth tf_gradients random_binomial style_loss gelu init_weights tf_get_value upsample2d reshape_4D batch_set_value max_pool average_tensor_list gaussian_blur depth_to_space AdaBelief OptimizerBase RMSprop umeyama get_power_of_two rotationMatrixToEulerAngles polygon_area MPSharedList IndexHost Index2DHost ListHost DictHostCli DictHost QSubprocessor QDarkPalette QActionEx QSize_to_np QImage_from_np QImage_to_np QPixmap_from_np QPoint_to_np QPoint_from_np QXIconButton QXMainWindow DFLIMG DFLJPG FaceEnhancer FaceType FANExtractor blur_image_hull_mask mirror_landmarks get_face_struct_mask estimate_pitch_yaw_roll convert_98_to_68 expand_eyebrows get_rect_from_landmarks get_transform_mat draw_rect_landmarks get_cmask transform_points estimate_averaged_yaw calc_face_pitch alpha_to_color get_image_eye_mask draw_landmarks get_image_hull_mask get_image_mouth_mask S3FDExtractor XSegNet dev_test_68 dev_test dev_resave_pngs extract_vggface2_dataset extract_umd_csv dev_segmented_trash process_folder FacesetEnhancerSubprocessor extract_video video_from_sequence denoise_image_sequence cut_video remove_xseg remove_xseg_labels apply_xseg fetch_xseg FrameInfo InteractiveMergerSubprocessor MergeFaceAvatar process_frame_info MergeMasked MergeMaskedFace MergerConfigMasked MergerConfigFaceAvatar MergerConfig ScreenManager ScreenAssets Screen ModelBase PreviewHistoryWriter import_model QModel SAEHDModel XSegModel PackedFaceset Sample SampleType SampleGeneratorBase SampleGeneratorFace SampleGeneratorFaceCelebAMaskHQ MaskType SampleGeneratorFacePerson Index2DHost SampleGeneratorFaceTemporal SampleGeneratorFaceXSeg SegmentedSampleFilterSubprocessor SampleGeneratorImage SampleGeneratorImageTemporal FaceSamplesLoaderSubprocessor SampleLoader SampleProcessor QCursorDB QIconDB QImageDB QStringDB ImagePreviewSequenceBar QUIConfig QCanvasOperator LoaderQSubprocessor CanvasConfig OpMode QCanvas DragType ViewLock ColorScheme QCanvasControlsLeftBar start QCanvasControlsRightBar MainWindow PTEditMode main set_process_lowest_prio main set_process_lowest_prio unpack_faceset pack save_faceset_metadata log_info restore_faceset_metadata_folder pack_faceset save_faceset_metadata_folder restore_faceset_metadata Path input_dir unpack recover_original_aligned_filename set_process_lowest_prio add_landmarks_debug_images main set_process_lowest_prio main set_process_lowest_prio output_ext fps extract_video output_dir input_file set_process_lowest_prio audio_track_id from_time bitrate to_time cut_video input_file set_process_lowest_prio factor denoise_image_sequence set_process_lowest_prio input_dir video_from_sequence set_process_lowest_prio Path set_process_lowest_prio input_dir process_folder dev_test set_process_lowest_prio input_dir start Path set_process_lowest_prio input_dir model_dir apply_xseg Path input_dir set_process_lowest_prio Path remove_xseg set_process_lowest_prio input_dir remove_xseg_labels Path set_process_lowest_prio input_dir Path fetch_xseg set_process_lowest_prio input_dir print_help exit loader_func asarray bytearray imencode suffix shape normalize_channels resize nice SetPriorityClass HANDLE GetCurrentProcess SetProcessDPIAware user32 write_bytes parent name unlink rename exists is_dir scandir str list scandir any Path scantree exists append remove get_image_paths name stem set add verbose_print_func Path exists Path exists Path exists str list lower scandir Path startswith append exists str sorted list path lower scandir Path exists name Path rename get_file_paths unlink Path get_file_paths normal empty prod range calcsize warpAffine ones getRotationMatrix2D zeros sum medianBlur addWeighted ones zeros GaussianBlur max dtype reshape astype copy argsort shape bilateralFilter fill empty range eps T clip reshape eig dot shape sqrt cov mean diag T reshape min astype float32 empty_like solve dot shape histogram interp max range _scale_array uint8 astype float32 merge lab_image_stats COLOR_LAB2BGR cvtColor split T reshape transpose mean dot eigh eye cholesky split min max float64 astype shape unique interp ravel dtype astype shape channel_hist_match range uint8 astype float32 COLOR_BGR2LAB color_transfer_sot COLOR_LAB2BGR cvtColor uint8 color_transfer_idt color_transfer_mkl astype float32 reinhard_color_transfer linear_color_transfer color_transfer_sot clip shape repeat len shape shape range tuple line range len draw_polygon concatenate shape resize expand_dims max enumerate T convolve square mean sqrt array shape zeros float64 marziliano_method astype canny sobel gradient atan2 shape any zeros round range int exp slice get_block_contrast shape flipud round zeros is_edge_block rot90 range cvtColor COLOR_BGR2GRAY rand random clip array COLOR_HSV2BGR random merge COLOR_BGR2HSV randint clip cvtColor split LinearMotionBlur randint random randint GaussianBlur random int rand random shape resize float32 getAffineTransform float32 fillConvexPoly shape boundingRect int32 applyAffineTransform zeros expand_dims array shape morphTriangle zeros simplices fromarray uint8 convert astype COLOR_RGB2BGR array cvtColor truetype asarray Draw get_default_ttf_font_name concatenate text new _get_pil_font shape clip draw_text range len draw_text_lines zeros shape T random astype copy float32 getRotationMatrix2D dict uniform linspace random_normal warpAffine remap resize norm clip einsum concatenate norm reshape empty abs clip max random randint initializer inputs append batch_set_value run gradients expand_dims __enter__ __exit__ enumerate reduce_mean __enter__ __exit__ concat pow tanh sqrt pi as_list reshape tile transpose value resize transpose value resize transpose reshape transpose randint float32 pad make_kernel tile depthwise_conv2d gaussian_blur dtype constant arange reshape float32 square reduce_mean reducer cast softmax tile max as_list reshape transpose as_list reshape transpose constant reshape multiply matmul cast svd T ones matrix_rank mean dot eye sum diag sqrt atan2 shape Format_Grayscale8 Format_BGR888 Format_ARGB32 height reshape convertToFormat width constBits setsize range squeeze invertAffineTransform shape transform expand_dims get norm getAffineTransform polygon_area astype float32 transform_points sqrt estimate_averaged_yaw array transform_points FULL_NO_ALIGN get_transform_mat float32 array copy concatenate expand_eyebrows fillConvexPoly convexHull zeros int getStructuringElement astype fillConvexPoly MORPH_ELLIPSE convexHull dilate zeros GaussianBlur int getStructuringElement astype fillConvexPoly MORPH_ELLIPSE convexHull dilate zeros GaussianBlur shape zeros concatenate process copy blend alpha_to_color zeros get_image_hull_mask gdf max clip int blur getStructuringElement min erode argwhere MORPH_ELLIPSE expand_dims copy draw_landmarks zeros expand_eyebrows concatenate polylines tuple shape get_image_hull_mask array circle get_transform_mat draw_rect transform_points draw_polygon draw_landmarks array array rotationMatrixToEulerAngles concatenate astype float32 pi solvePnP zeros array clip get pop get_image_paths parent log_info name stem progress_bar_generator get_all_dir_names Path mkdir run fromString split cv2_imread Path normalize_channels exists input_bool str log_info name stem append get_image_paths get_rect_from_landmarks unlink mkdir parent cv2_imwrite progress_bar_generator read_text split get str get_image_paths parent log_info name len unlink Path mkdir split log_err run range exists fromString input_bool blur interact cv2_imread Path save BestGPU INTER_LINEAR max exists run log_info stem input append HEAD range get_source_filename warpAffine get_image_paths unlink get_image_to_face_mat mkdir load set_xseg_mask min read_text array split get_image_paths cv2_imwrite progress_bar_generator cv2_imread Path get_image_paths parent name stem rename Path mkdir append input_bool join get_image_paths log_info parent name copy unlink rmtree mkdir run update str get_image_paths parent input_str stem output get_first_file_by_stem unlink input_int mkdir Path log_err input run str suffix parent input_str stem overwrite_output input_int log_err Path input max run update str suffix parent progress_bar_generator output input_int rename log_err Path run clip enumerate suffix input_str wait input_int Path max input_bool str stem input update run_async get_image_paths close mkdir parent overwrite_output get_first_file_by_stem log_err probe load extract initialize get_image_paths log_info set_xseg_mask progress_bar_generator astype float32 get_resolution ask_choose_device shape XSegNet resize save load str get_image_paths log_info parent name has_polys progress_bar_generator copy get_seg_ie_polys mkdir load get_image_paths log_info set_xseg_mask input_str progress_bar_generator has_xseg_mask save load get_image_paths log_info input_str has_seg_ie_polys progress_bar_generator save set_seg_ie_polys warpAffine get_transform_mat astype float32 cv2_imread normalize_channels filename clip sharpen_func sharpen_mode concatenate predictor_func add_source_image process_frame_info temporal_face_count append range sharpen_amount predictor_func color_transfer_mkl motion_power bicubic_degrade_power motion_blur_power linear_color_transfer color_transfer_mix boundingRect resize reduce_colors max clip face_enhancer_func hist_match_threshold medianBlur super_resolution_power WARP_INVERSE_MAP ones LinearMotionBlur shape pad blur_mask_modifier image_denoise_power masked_hist_match blursharpen range color_hist_match BORDER_TRANSPARENT warpAffine sharpen_mode xseg_256_extract_func seamlessClone color_transfer_idt astype copy reinhard_color_transfer empty_like motion_deg INTER_CUBIC MORPH_ELLIPSE color_transfer_sot dilate GaussianBlur get_image_hull_mask NORMAL_CLONE uint8 int erode_mask_modifier getStructuringElement get_transform_mat float32 erode argwhere blursharpen_amount color_degrade_power landmarks_list concatenate astype float32 cv2_imread shape normalize_channels MergeMaskedFace filepath clip enumerate str parent cv2_imread locals __import__ globals dict setApplicationName setPalette QDarkPalette Path show str initialize log_info setWindowIcon addApplicationFont AA_EnableHighDpiScaling setStyle setFont gettempdir setAttribute QApplication path_contains app_icon MainWindow exec_ parent QFont raise_ AA_UseHighDpiPixmaps | <table align="center" border="0"> <tr><td colspan=2 align="center"> # DeepFaceLab <a href="https://arxiv.org/abs/2005.05535"> <img src="https://static.arxiv.org/static/browse/0.3.0/images/icons/favicon.ico" width=14></img> https://arxiv.org/abs/2005.05535</a> ### the leading software for creating deepfakes <img src="doc/DFL_welcome.png" align="center"> </td></tr> <tr><td colspan=2 align="center"> | 2,961 |
mgeezzyy/Facial-Expression-Recognition-2018 | ['l2 regularization', 'facial expression recognition'] | ['Deep Facial Expression Recognition: A Survey', 'Convolutional Neural Networks for Facial Expression Recognition'] | src/demo.py src/frontalize.py src/facial_feature_detector.py src/check_resources.py src/models.py src/camera_calibration.py get_opengl_matrices estimate_camera point_in_frustum calib_camera calc_inside extract_frustum check_dlib_landmark_weights download_file extract_bz2 fbeta myfrontalize demo get_landmarks display_landmarks _shape_to_np ThreeD_Model frontalize get_model3 get_model1 get_model4 get_model2 out_A calib_camera hstack reshape pi calc_inside solvePnP model_TD Rodrigues out_A asmatrix reshape zeros pi get_opengl_matrices asmatrix multiply sqrt zeros sum extract_frustum range int read chr print len write close urlopen info open read print write close BZ2File open download_file mkdir extract_bz2 subplots to_categorical delete myfrontalize ReduceLROnPlateau flow xticks round max heatmap yticks show subplot list multiply predict_classes len map ylabel shape imshow title legend emotion head range pixels asarray plot createCLAHE astype get_model1 copy fit_generator set sqrt ImageDataGenerator compile get_model2 load int uint8 get_model3 get_model4 evaluate print reshape EarlyStopping min fit float32 xlabel confusion_matrix figure summary ModelCheckpoint array read_csv split sum epsilon round clip findNonZero get_landmarks boundingRect resize ref_U show estimate_camera frontalize range format asarray COLOR_BGR2GRAY COLOR_GRAY2BGR astype check_dlib_landmark_weights copy uint8 print float32 ThreeD_Model cvtColor append asarray range predictor asarray _shape_to_np shape_predictor append detector enumerate get_frontal_face_detector clear_overlay add_overlay image_window set_image hit_enter_to_continue ravel_multi_index arange vstack round exp ones multiply transpose sum hstack astype INTER_CUBIC tile unique GaussianBlur reshape divide float32 remap logical_or histogram zeros fliplr Sequential add Dense MaxPooling2D Convolution2D Activation BatchNormalization Flatten Dropout Sequential add Dense MaxPooling2D Convolution2D Activation BatchNormalization Flatten Dropout Sequential add Dense MaxPooling2D Convolution2D Activation BatchNormalization Flatten Dropout Sequential add Dense MaxPooling2D Convolution2D Activation BatchNormalization Flatten Dropout | # Facial-Expression-Recognition-2018 COS475 Final Project Miguel Guarniz, Azmo Rinsler Inspired by the "Challenges in Representation Learning: Facial Expression Recognition Challenge" https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data Data Set: fer2013 (48x48px grayscale images) ~28,000 training images ~ 4,000 validation images ~ 4,000 test images | 2,962 |
mhazoglou/PVM_PyCUDA | ['visual tracking', 'common sense reasoning'] | ['Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network'] | train_on_the_fly_models.py train_foveated_motion_integration_models.py train_motion_integration_models.py FormattingFiles.py client_cv.py WeightsAndBiasInitialization.py FractalConstructor.py PVM_PyCUDA.py server_cv.py train_foveated_on_the_fly_models.py panTiltDataCollector.py __init__.py RectangularGridConstructor.py rescale unpack_received_data fractal_flatten_image save_tracker_data_to unflatten_image hdf5_append_position_data hdf5_raw_data test_flatten_inverts reformat_tracker_data flatten_image fractal_unflatten_image norm_and_heatmap reformat_raw_data fractal_connections layer_fractal_connections feeds2_fractal_connections fedfrom_fractal_connections reflecting_off_walls tilt_random_trajectory pan_random_trajectory oscillation periodic_boundaries PVMtracker TransformMotionIntegrationPhantomXTurretPVM regularize TransformMotionIntegrationPVM MotionIntegrationPVM base_pvm_gpu_forward PVM_CUDA activation base_pvm_gpu_backprop update_parameters movingaverage PhantomXTurretPVM update_weights PinholeSaccadicPVM new_block_idx adam_update_parameters OnTheFlyPVM feeds2 make_connections break_stuff fedfrom shape_check main main main main weight_initialize tracker_weight_initialize ptr_to_row test_no_overlap_in_mapping encode loads dtype int list product flatten shape zeros range dtype int list product reshape zeros range join sorted ANTIALIAS DataFrame reshape size flatten_image apply resize append listdir array read_csv enumerate open join sorted ANTIALIAS DataFrame size apply resize append listdir array read_csv enumerate open rsplit list items to_csv mkdir save int reshape append shape_check range zeros len dtype list product flatten shape zeros range dtype list product reshape zeros range listdir listdir int split int split int split OrderedDict fractal_construct update range list range len list range len list range len randint randint fullmatch ones append zip grid_h2op stream1 L_input gpudata threads grid_h2op_dot prepared_async_call stream3 L_op grid_o_shuf grid_int_der_err grid_i2h_dot L_out stream2 L_pred grid_i2h stream5 copy L_full_input stream4 prepared_call L_hidden grid_h2op stream2 stream1 L_pred i2h_weights_nnz h2op_weights_nnz grid_i2h L_input gpudata threads L_op grid_o_shuf grid_int_der_err L_out prepared_call L_hidden prepared_async_call grid_h2op stream2 stream1 i2h_weights_nnz h2op_weights_nnz grid_i2h gpudata grid_update_h2op threads L_op grid_update_i2h L_hidden prepared_async_call stream2 stream1 i2h_weights_nnz h2op_weights_nnz gpudata grid_update_h2op threads grid_update_i2h prepared_async_call grid_h2op stream1 i2h_weights_nnz gpudata threads h2op_weights prepared_async_call h2op_weights_nnz stream3 L_op grid_update_i2h stream2 grad_weight_h2op grad_weight_i2h grid_i2h grid_update_h2op stream4 prepared_call L_hidden i2h_weights isinstance shape_check find_edge_pos_upper_layer find_edge_pos_lower_layer shape_check format feeds2 OrderedDict fedfrom append shape_check range enumerate len items list format OrderedDict append range split items list format TransformMotionIntegrationPVM make_connections reshape File break_stuff flatten_image shape append range adam_train_and_validate OnTheFlyPVM items list arange randn concatenate astype sqrt int32 append array range len arange randn concatenate astype sqrt int32 append shape_check array range len range enumerate len print | # PVM_PyCUDA An CUDA based optimization of the Predictive Vision Model by Piekniewski et. al. in https://arxiv.org/abs/1607.06854 as well as some extensions that I personally used in my research. ## Getting Started After installing the dependencies go through the jupyter notebook Quick_Start_Tutorial_PVM.ipynb to download data and train your first PVM. ### Prerequisites CUDA 8 (Future versions may support CUDA 9 and 10) PyCUDA OpenCV NumPy MatPlotLib | 2,963 |
mhinne/BNQD | ['causal inference', 'experimental design', 'time series'] | ['Bayesian nonparametric discontinuity design'] | test_cases/mogp_vgp.py examples/Meditation/kundalini_yoga_meditation.py utilities.py examples/Dutch_elections (MIGP)/read_election_data.py test_cases/rd_2d.py test_cases/univariate/its_1d.py simulations/sim_RD.py test_cases/multivariate/independent_kernels_MOGP.py test_cases/univariate/its_1d_counterfactual.py kernels.py BNQD.py test_cases/multivariate/rd_migp.py examples/Dutch_elections (MIGP)/Dutch_elections.py test_cases/rd_covariates.py examples/Longevity/longevity.py test_cases/univariate/independent_kernels.py test_cases/partially_shared_MOGP.py test_cases/multivariate/rd_mogp.py test_cases/univariate/rd_1d.py examples/Major depressive disorder/major_depression.py models.py simulations/sim_ITS.py test_cases/its_2d.py BNQD IndependentKernel SpectralMixture MultiOutputKernel initialize_from_emp_spec SpectralMixtureComponent DiscontinuousModel ContinuousModel extrapolate_m0 lighten_color logmeanexp logsumexp renormalize split_data plot_m0 plot_effect_size augment_output augment_input plot_m1 pred_to_dataframe is_below_border_scaled read_data total_vote_count harmonic_mean_estimator abline run_frequentist_baseline get_bnqd_results plot_mixture_component plot_f shifting_discontinuity_mean_function rmse correlation run_simulations plot_simulation_results compute_twostep_pvals plot_visualisation_example simulate_data run_visualisation_example plot_data linear_regression f_1 sigmoid f_2 f_1 plot f_2 sigmoid plot_gp linear_regression linear_regression cov2cor f_1 f_2 plot_gp line square quarter_circle f_1 f linear_regression plot_fit signal linear_regression quadratic_regression initialize_from_emp_spec show digitize plot hstack rand trapz weights_ lombscargle sqrt means_ linspace figure histogram covariances_ GaussianMixture fit isinstance shape max log len max max get plot forcing_variable predict_y flatten sqrt linspace gca fill_between get x0 plot forcing_variable predict_y sqrt scatter linspace zip gca fill_between get x0 plot forcing_variable predict_y sqrt scatter linspace zip gca fill_between rgb_to_hls get normal format set_title plot zeros_like gaussian_kde set_ylim axvline set_xlim pdf sqrt linspace bma_kde gca zeros fill_between append dict T rename range print dict scatter plot get_model_posterior get_bma_effect_size_monte_carlo get_bayes_factor get_marginal_likelihoods dict get_bma_bayes_factor discontinuous_effect_size_mean_var kernels get_hyperparameters dict rdd optimal_bandwidth truncated_data DataFrame fit plot ones squeeze min gca fill_between len yscale plot sqrt gca fill_between sqrt cdf zeros range len normal sort uniform f plot set_xlabel axvline set_ylabel gca get_results optimal_bandwidth sep DataFrame list compute_twostep_pvals rdd range format asarray BNQD truncated_data enumerate savez simulate_data tqdm isfile zeros train fit subplots arange flatten axhline show plot_2step_pval set_title set_xlabel savefig range set_xticklabels set_xlim tight_layout figlegend plot_logBF enumerate set_xticks set_ylabel get_legend_handles_labels plot_effectsize_result set_ylim savez simulate_data BNQD fit truncated_data predict_y flatten get_results rdd linspace lin_reg isfile optimal_bandwidth zeros train DataFrame enumerate subplots linspace show set_title set_xlabel f axvline scatter savefig format plot set_xlim figlegend tight_layout zip enumerate simulate_data set_ylabel get_legend_handles_labels fill_between plot show predict_f hstack plot_gp figure get_color legend sqrt diag outer plot flatten sqrt gca fill_between | # BNQD A Python toolbox for Bayesian Nonparametric Quasi-Experimental Design. The de facto standard for causal inference is the randomized controlled trial, where one compares a manipulated group with a control group in order to determine the effect of an intervention. However, this research design is not always realistically possible due to pragmatic or ethical concerns. In these situations, quasi-experimental designs (QEDs may provide a solution, as these allow for causal conclusions at the cost of additional design assumptions. In this repository, we provide the implementation of a Bayesian non-parametric model-comparison-based take on QED, called BNQD. It quantifies (the presence of) an effect using a Bayes factor and and Bayesian model averaged posterior distribution. For basic usage, see the demo.py script, and for slightly more intricate examples, see the empirical examples and simulations. The current implementation of BNQD depends on [GPy](https://github.com/SheffieldML/GPy). ## Literature * Max Hinne, Marcel van Gerven and Luca Ambrogioni, 2019. Causal inference using Bayesian non-parametric quasi-experimental design. ArXiv: https://arxiv.org/abs/1911.06722. | 2,964 |
mhwong2007/GEE | ['anomaly detection'] | ['GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection'] | build_model_input.py train_vae.py ml/vae.py utils.py feature_extraction.py main FeatureComposer main FeatureExtractor main save_parquet_for_petastorm_parquet normalise change_df_schema init_local_spark row_generator read_csv patch_time_windows Decoder VAE Encoder Unischema getLogger addHandler StreamHandler parquet change_df_schema info transform init_local_spark setLevel save_parquet_for_petastorm_parquet extract_features FeatureExtractor read_csv as_uri cpu_count VAE make_reader Trainer DataLoader save_checkpoint mkdir fit col StructType withColumn unix_timestamp csv withColumn col from_unixtime available executable getOrCreate createDataFrame as_spark_schema map as_uri | mhwong2007/GEE | 2,965 |
micahjsmith/ml-bazaar-2019 | ['automl'] | ['The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development'] | analysis.py make_figure_4 _load_baselines_df _get_datasets_df create_record _savefig compute_npipelines_maternse_5_7 _get_best_pipeline compute_total_pipelines make_table_3 get_disk_usage_compressed _add_tscores compute_tuning_improvement_sds_5_4 _load_execution_times_df _normalize_df compute_matern_wins_pct_5_7 quiet make_figure_5 getsize make_figure_x _load_pipelines_df compute_xgb_wins_pct_5_6 get_disk_usage_inflated compute_tuning_improvement_pct_of_tasks_5_4 _clear_cache compute_pipelines_second compute_npipelines_xgbrf_5_6 make_table_4 create_all_records _assert_filters main sizeof_fmt _get_tuning_results_df _make_normalizer compute_performance_vs_baseline jsoncached _load_task_characteristics_df _get_test_results_df _get_filters get_explorer get_db isinstance get_referents id add set append rmtree joinpath get_pipelines joinpath mkdir read_pickle _assert_filters _get_filters to_pickle _add_tscores set_index replace read_table joinpath _get_filters join join walk get_disk_usage_inflated y unique load_dataset getsize get_disk_usage_compressed len tolist tqdm joinpath savefig Path _make_normalizer apply join _load_pipelines_df eval rename to_frame _add_tscores _get_filters get_test_results apply get_dataset_id get_datasets set_index joinpath read_csv from_records to_csv joinpath create_all_records T applymap to_latex describe tolist to_csv get_datasets joinpath rename nan _load_task_characteristics_df to_latex sort_index _load_pipelines_df _get_datasets_df to_csv apply to_frame joinpath unique DataFrame get_dataset_id merge join _load_execution_times_df describe to_csv apply eval joinpath rename _add_tscores list join replace set_index from_records _load_baselines_df to_csv index joinpath rename agg dropna _get_tuning_results_df joinpath format _load_pipelines_df apply query joinpath _get_test_results_df sum mean dropna joinpath _get_tuning_results_df mean dropna joinpath _get_tuning_results_df sum to_csv contains joinpath DataFrame _load_pipelines_df to_csv assign joinpath _get_test_results_df sum to_frame find_tuner_test_ids to_csv joinpath _get_test_results_df isin DataFrame _load_pipelines_df len to_csv assign joinpath _get_test_results_df sum to_frame sorted obj isinstance print dir set getattr FunctionType | # ml-bazaar-analysis Replication files for The Machine Learning Bazaar. > M. Smith, C. Sala, J.M. Kanter, and K. Veeramachaneni. ["The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development."](https://www.micahsmith.com/files/mlbazaar_sigmod20.pdf) SIGMOD 2020. ## Usage Run `make`, that's it. ```shell make ``` This will check that you have [Docker](https://docs.docker.com/install/) installed and ask you to install it if you have not done so. Then, it will | 2,966 |
michael-wzhu/dynamic_routing_pytorch | ['sentiment analysis', 'text classification'] | ['Information Aggregation via Dynamic Routing for Sequence Encoding'] | src/models/__init__.py src/__init__.py src/modules/seq2vec_encoders/dynamic_routing.py src/modules/seq2seq_encoders/__init__.py tests/models/sst_5_simple_classifier_test.py tests/dataset_readers/sst_5_dataset_reader_test.py datasets/dataset_utils/extract_sst.py src/modules/seq2seq_encoders/rnn_encoder.py src/dataset_readers/__init__.py src/modules/__init__.py src/models/sst_5_encoder_aggregator_classifier.py src/modules/seq2vec_encoders/__init__.py datasets/dataset_utils/sst_5_csv2jsonl.py src/dataset_readers/sst_5.py get_label_by_value csv2jsonl SST5DatasetReader SST5EncoderAggregatorClassifier RnnEncoder DynamicRoutingAggregator squash TestSST5DatasetReader SST5SimpleClassifierTest print len write dumps close range head read_csv open sqrt norm | pytorch implementation of dynamic routing model by https://arxiv.org/pdf/1806.01501.pdf ### requirements - allennlp - pytorch ### command lines: ```bash allennlp train experiments/sst_5/sst_5_encoder_aggregator_classifier_lstm_dynamic_routing.json -s ./tmp/sst_5_0603 --include-package src ``` ### authors - michael-wzhu | 2,967 |
michaelzhang01/ISMOE | ['gaussian processes'] | ['Embarrassingly Parallel Inference for Gaussian Processes'] | code/ISMOE.py code/online_ISMOE.py _unscaled_dist bernoulli_dlogpdf_dlink bernoulli_d2logpdf_dlink2 ISMOE bernoulli_logpdf _compute_B_statistics ISMOE _unscaled_dist pad_kernel_matrix std_norm_cdf square where any isinf clip clip where std_norm_cdf where std_norm_cdf T dtrtrs diag diagflat isnan sqrt dot any jitchol eye sum log T inf square tdot dot sum clip flatten shape pad vstack | michaelzhang01/ISMOE | 2,968 |
michelesantacatterina/KOM | ['causal inference'] | ['Optimal Estimation of Generalized Average Treatment Effects using Kernel Optimal Matching'] | gp_simu_gate.py gp RBF WhiteKernel kernel_ DotProduct ConstantKernel Matern fit | # KOM: Optimal Estimation of Generalized Average Treatment Effects using Kernel Optimal Matching KOM estimates weights that minimize the worst-case Conditional Mean Squared Error (CMSE) of the weighted estimator in estimating Generalized Average Treatment Effects (GATE) over the space of weights. The manuscript can be found here: [*https://arxiv.org/pdf/1908.04748.pdf*](https://arxiv.org/pdf/1908.04748.pdf). ### Installation The package can be installed from source: ``` install.packages("https://raw.github.com/michelesantacatterina/KOM/master/releases/KOM_0.1.0.tar.gz", repos = NULL, type = "source") ``` | 2,969 |
micka-charpak/ProjetAnalyse | ['active learning'] | ['ALiPy: Active Learning in Python'] | Database_Titanic/active_villebon.py modAl/modal_petrol_pb.py Database_Titanic/Traitementdatabase_RDforest.py modAl/modAL_titanic_poolbased.py Database_Petrol/try-random_forest.py Database_Titanic/partie_estelle.py Database_Titanic/random_forest.py modAl/modal_petrol_qs.py code_active_learning.py modAl/modal_pool-based_iris.py modAl/modal_query-synthesis_iris.py Database_Titanic/Active_learning_test.py pickle_load MarginSamplingSelection pickle_save experiment BaseSelectionFunction performance_plot RfModel EntropySelection LogModel TrainModel Normalize split BaseModel download RandomSelection get_k_random_samples TheAlgorithm SvmModel strategy download randomForest split download split print shape dropna read_csv values drop check_random_state print reshape astype choice unique bincount print dump close open print close load open str pickle_save print dumps accuracies append range TheAlgorithm run show subplots arange plot set_xlim grid legend float array range set_ylim enumerate choice predict_proba RandomForestClassifier accuracy_score predict fit reset_index | # ProjetAnalyse Semi supervised learning and active learning L'active learning a pour but d'étiqueter des données non supervisées,on classifie les données, une fois quelque données étiquitées, l'AI va en augmenter de façon répétitive la taille de nos données étiquetées pré-sélectionnées, il est possible d'obtenir des performances similaires (ou supérieures) à celles d'un ensemble de données entièrement supervisé avec une fraction du coût ou du temps nécessaire à l'étiquetage de toutes les données Une donnée étiquetée veut dire que la donnée en entrée est ciblé en sortie des données supervisées sont des données étiquetées à 100% des données non supervisées sont des données étiquetées à 0%. # Initial links dia semi supervised : http://pageperso.univ-lr.fr/arnaud.revel/MesPolys/SemiSupervise.pdf article about ative learning with example code : https://towardsdatascience.com/active-learning-tutorial-57c3398e34d video semi supervised : | 2,970 |
mickaelChen/ReDO | ['unsupervised object segmentation', 'semantic segmentation'] | ['Unsupervised Object Segmentation by Redrawing'] | datasets.py train.py models.py example_load_pretrained.py FlowersDataset CUBDataset CMNISTDataset LFWDataset _netRecZ _downConv SelfAttention _netEncM _resMaskedGenerator128 _resDiscriminator128 _resBloc _upConv SelfAttentionNaive _resEncoder128 _netGenX weights_init_ortho evaluate weight orthogonal_ initOrthoGain to min netEncM item | # ReDO: Unsupervised Object Segmentation by Redrawing Code for paper [Unsupervised Object Segmentation by Redrawing](https://papers.nips.cc/paper/9434-unsupervised-object-segmentation-by-redrawing) \[[Preprint](https://arxiv.org/abs/1905.13539)\] by Mickaël Chen, Thierry Artières and Ludovic Denoyer. Presented as poster at NeurIPS 2019, Vancouver.  We discover meaningful segmentation masks by redrawing regions of the images independently. ## Table of Contents - [Random samples](#random-samples) * Samples for Flowers, LFW, CUB and toy dataset * A more diverse dataset with two classes - [Datasets instructions](#datasets-instructions) * Flowers | 2,971 |
mickeystroller/TaxoExpan | ['product recommendation'] | ['TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network'] | baselines/simple_MLP/test.py baselines/XGBoost/model_training.py baselines/XGBoost/model_prediction.py utils/util.py base/base_trainer.py train.py logger/visualization.py data_loader/dataset.py trainer/__init__.py scripts/parse_to_semeval_format.py baselines/simple_MLP/model.py baselines/XGBoost/feature_extractor.py baselines/simple_parent.py generate_dataset_binary.py logger/logger.py data_loader/data_loaders.py infer.py model/loss.py baselines/XGBoost/model_tuning.py utils/__init__.py trainer/trainer.py baselines/XGBoost/self_supervision_generation.py logger/__init__.py test_fast.py baselines/simple_MLP/train.py parse_config.py model/metric.py baselines/simple_MLP/data_loader.py model/model.py base/__init__.py base/base_model.py base/base_data_loader.py baselines/simple_structure.py model/model_zoo.py main encode_graph main _set_by_path ConfigParser _get_opt_name _update_config _get_by_path encode_graph main rearrange main BaseDataLoader BaseModel BaseTrainer main main get_one_query_rank get_agg_func collate_subgraph_large_batch AnchorParentDataLoader collate_apgraph_small_batch EdgeDataLoader EdgeDataset SubGraphDataLoader AnchorParentDataset collate_edge_large_batch collate_edge_small_batch SubGraphDataset collate_subgraph_small_batch collate_apgraph_large_batch MAGDatasetSlim macro_averaged_rank topk_hit batched_topk_hit_5 MLP batched_topk_hit_1 batched_scaled_MRR DeepAPGMLP DeepSetMLP scaled_MRR macro_mr bce_loss batched_topk_hit_3 calculate_ranks_from_distance obtain_ranks test train save_checkpoint valid_epoch eval_metrics NegativeQueue FeatureExtractor main calculate_ranks_from_distance main main main MAGDataset Taxon MaskedGraphDataset collate_graph_and_node_large_batch collate_graph_and_node_small_batch MaskedGraphDataLoader setup_logging WriterTensorboardX square_exp_loss nll_loss bce_loss info_nce_loss margin_rank_loss combined_metrics mrr_scaled_10 calculate_ranks_from_similarities hit_at_5 micro_mr macro_mr hit_at_1 calculate_ranks_from_distance obtain_ranks hit_at_3 TaxoExpan GATLayer PGCN PGAT MeanReadout ConcatReadout MLP SumReadout BIM LBM NTN GCNLayer GAT MaxReadout WeightedMeanReadout GCN parse_string Trainer Taxon read_json Taxonomy write_json Timer ensure_dir MAGDataset graph_propagate readout DataParallel _get_subgraph device dataset MaskedGraphDataLoader initialize list tolist nodes add set_sharing_strategy load_state_dict chain to sum get_logger vocab format eval resume info batch load tqdm array KeyedVectors len _set_by_path target _get_opt_name getattr flags startswith int isin cat node_list sorted all_positions append kv obtain_ranks update partial n_samples startswith test_data zeros node2parents lr_scheduler time train Trainer getattr optim train_node_ids print calculate_ranks_from_distance set load_word2vec_format test_node_ids numpy distances to_networkx str flush write set agg_func append calculate_ranks_from_distance array enumerate distances len map get_agg_func agg Pool list map zip list map zip append tensor list map zip list map zip append tensor list map zip list map zip append tensor squeeze_ float binary_cross_entropy_with_logits list sum array mean int list isinstance chain sum array len ceil chain list array list calculate_ranks_from_distance squeeze zip append numpy array len obtain_ranks obtain_ranks obtain_ranks obtain_ranks obtain_ranks load AnchorParentDataLoader n_samples MLP EdgeDataLoader print DeepAPGMLP DeepSetMLP eval set_sharing_strategy resume load_state_dict device zeros to range SubGraphDataLoader len zeros enumerate len zeros eval tolist len print format __name__ save checkpoint_dir model DeepAPGMLP zero_grad ReduceLROnPlateau save_checkpoint device SubGraphDataLoader AnchorParentDataLoader EdgeDataLoader step tolist Adam max_epoch shape to range MLP MAGDatasetSlim valid_epoch enumerate time backward print DeepSetMLP loss_fn zeros numpy len output_ranks FeatureExtractor str in_edges normalize descendants copy remove_edges_from extend validation DMatrix best_score sample_avoid_positive_set validation_node_ids save_binary extract_features NegativeQueue neg output list map stack zip tensor batch list map stack zip append tensor batch enumerate str basicConfig format list items print read_json dictConfig Path is_file sum squeeze list product margin_ranking_loss extend device zip append to numpy array range len list sum array calculate_ranks mean chain list sum array chain list sum array chain list sum array ceil chain list array macro_mr hit_at_1 max mrr_scaled_10 hit_at_3 mkdir Path | # TaxoExpan The source code used for self-supervised taxonomy expansion method [TaxoExpan](https://arxiv.org/abs/2001.09522), published in WWW 2020. ## Install Guide ### Install DGL 0.4.0 version with GPU suppert using Conda From following page: [https://www.dgl.ai/pages/start.html](https://www.dgl.ai/pages/start.html) ``` conda install -c dglteam dgl-cuda10.0 ``` ## Data Preparation For dataset used in our WWW paper, you can directly download all input files from [Google Drive](https://drive.google.com/drive/folders/1-_yaDYDbivAW_ZA3em8WTbxDSnfIZfV9?usp=sharing) and skip this section. | 2,972 |
microsoft/AEC-Challenge | ['speech recognition', 'speech enhancement'] | ['ICASSP 2022 Acoustic Echo Cancellation Challenge'] | baseline/interspeech2021/audio_utils.py AECMOS/aecmos.py baseline/icassp2022/enhance.py baseline/interspeech2021/enhance.py get_score is_interspeech2021_clip process_interspeech2021 read_and_process_audio_files get_clip_scenario get_clip_hash main get_lpb_mic_paths parse_args DECModel DECModel add_argument ArgumentParser get_score join update basename glob print read_and_process_audio_files to_csv get_clip_scenario tqdm append DataFrame get_lpb_mic_paths join basename relpath get_clip_scenario get_clip_hash split next load is_interspeech2021_clip min process_interspeech2021 int basename get_clip_scenario len post json | # AEC Challenge The ICASSP 2023 Acoustic Echo Cancellation Challenge is intended to stimulate research in acoustic echo cancellation (AEC), which is an important area of speech enhancement and is still a top issue in audio communication. This is the fourth AEC challenge and it is enhanced by adding a second track for personalized acoustic echo cancellation, reducing the algorithmic+buffering latency to 20ms, and including a full-band version of AECMOS. We open source two large datasets to train AEC models under both single talk and double talk scenarios. These datasets consist of recordings from more than 10,000 real audio devices and human speakers in real environments, as well as a synthetic dataset. We open source an online subjective test framework and provide an online objective metric service for researchers to quickly test their results. The winners of this challenge were selected based on the average Mean Opinion Score (MOS) achieved across all scenarios and the word accuracy rate. For more details about the challenge, please visit the challenge [website](https://www.microsoft.com/en-us/research/academic-program/acoustic-echo-cancellation-challenge-icassp-2023/) and refer to the [paper](https://www.researchgate.net/publication/366205532_ICASSP_2023_ACOUSTIC_ECHO_CANCELLATION_CHALLENGE). # Repo details * The datasets directory contains the real and synthetic training datasets and real test sets. * Test set for the ICASSP 2022 challenge is located at https://github.com/microsoft/AEC-Challenge/tree/main/datasets/test_set_icassp2022. * Links to newest fullband real-world recordings are located at https://github.com/microsoft/AEC-Challenge/tree/main/datasets/fullband and https://github.com/microsoft/AEC-Challenge/tree/main/datasets/fullband_mobile. # Usage 1. Set up Git Large File Storage (LFS) for faster download of the datasets. First, [download](https://git-lfs.github.com/) and install the Git LFS client. Then, set up Git LFS for your user account by running: ``` | 2,973 |
microsoft/EA-VQ-VAE | ['text generation'] | ['Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder'] | vq-vae/run.py generator/model.py vq-vae/gpt2.py data/preprocess-event2mind.py data/preprocess-atomic.py generator/run.py estimator/model.py vq-vae/model.py generator/beam.py estimator/run.py zipped_flatten zipped_flatten Model read_examples set_seed test Example convert_examples_to_features main InputFeature train Beam Model dist1 read_examples set_seed evaluate score dist2 test Example convert_examples_to_features main InputFeature train tokenize Block load_tf_weights_in_gpt2 MLP prune_conv1d_layer gelu GPT2PreTrainedModel GPT2Model Attention CodeBook Model read_examples set_seed test Example convert_examples_to_features main InputFeature train load Example append enumerate open max_event_length join format convert_tokens_to_ids category map idx info append InputFeature tokenize len seed manual_seed_all manual_seed gradient_accumulation_steps model get_linear_schedule_with_warmup tuple zero_grad DataLoader set_description output_dir save tensor round train_steps open read_examples list str sorted data_dir DistributedSampler TensorDataset convert_examples_to_features next get_lr range state_dict format param_groups test mean info fp16 keys load join warmup_proportion learning_rate loss_scale prior_distribution_dir AdamW backward RandomSampler named_parameters cycle tqdm step train_batch_size len read_examples eval_batch_size tuple DataLoader eval TensorDataset convert_examples_to_features info tensor SequentialSampler numpy range len from_pretrained DataParallel warning ArgumentParser device do_train output_dir open str basicConfig sorted set_seed DDP data_dir set_device half device_count Model load_state_dict do_label parse_args to dump format z_size init_process_group test info fp16 train keys load join n_gpu model_name_or_path prior_distribution_dir add_argument load_model_path bool local_rank lower append range len prior open max_target_length posterior range load int num_evidence posterior_dir evaluate load read_examples eval_batch_size format tuple train len DataLoader TensorDataset convert_examples_to_features info sample tensor SequentialSampler open decode score target open list append format set sample tokenize load category train n_embd round Embedding GPT2Model do_topk range codebook_path deepcopy named_parameters do_test len load_variable int format info zip squeeze fullmatch from_numpy getattr list_variables abspath append split list size len contiguous copy_ device to detach exp argmax log exp sum copy save state_dict | # Introduction This repo provides the code for the ACL 2020 paper "Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder" # Requirements - pip install torch==1.4.0 - pip install gdown transformers==2.8.0 filelock nltk # Download Dataset ### 1.Download Evidence ```shell cd data | 2,974 |
microsoft/EMNLP2019-Split-And-Recombine | ['semantic parsing'] | ['A Split-and-Recombine Approach for Follow-up Query Analysis'] | data_reader/dialogue_util.py dialogue/constant.py train_model.py dialogue/bind_define.py dialogue/refer_resolution.py dialogue/trie.py dialogue/find_lcs.py preprocess.py model/follow_up.py model/util.py data_reader/dialogue_reader.py model/metric.py construct_reader download_glove_embedding download_dataset build_vocab_embedding construct_model test construct_reader setup_seed setup_arguments construct_learning_scheduler train abstract_utterance FollowUpDataReader load_tables construct_table_entities get_val_actual_type get_entities_out_table fuzzy_matching_tokens BindingExecutor Binder transfer_to_tags get_col_actual_type StandardSpan StandardSymbol Bind display remove_stopwords get_best_conflicts find_common_snippet find_long_common_string PersonPossessSegment PronounSegment all_subclasses CalcDeterminerSegment ColSegment ConflictLinker ItemSegment PlainSegment ItemPossessSegment EntitySegment ValSegment TriState FusionMode SetDeterminerSegment BindTrie BindTrieNode FollowUpSnippetModel get_span_repr get_forward_span_repr predict_span_start_end PolicyNet sample_action get_backward_span_repr BLEUScore tokenize_sentence SymbolScore RewardScore evaluate_symbol_score evaluate_bleu_score RewardCalculator permutation prev_fol_permutation ConflictMode print get format move print SingleIdTokenIndexer TokenCharactersIndexer FollowUpDataReader CharacterTokenizer read get_token_to_index_vocabulary from_instances construct_reader uniform parse_args add_argument ArgumentParser seed manual_seed FollowUpSnippetModel LSTM BasicTextFieldEmbedder from_params PytorchSeq2SeqWrapper construct_model Trainer BucketIterator cuda Adam construct_reader load_state_dict from_instances load join serialization_dir learning_rate read rl_basic store_folder parameters construct_learning_scheduler index_with makedirs load construct_model serialization_dir read join print from_instances dumps store_folder construct_reader eval load_state_dict cuda evaluate_on_instances str replace tags len enumerate ValueDate label_ value print end text index start ValueNumber append ents SubType range enumerate join remove stem len index extractBests set append abs values enumerate deepcopy join StandardSymbol StandardSpan append range len str label_ range nlp cardinal date enumerate SubType len T len index zeros enumerate print enumerate find_long_common_string utterance remove_stopwords get_best_conflicts len RewardCalculator combination_reward_feedback append range len append range len get_forward_span_repr cat get_backward_span_repr float len float utterance tags RewardCalculator predict_span_start_end combination_reward_feedback sentence_bleu set append range copy permutation product min append len | # Split-And-Recombine This is the official code for our paper [A Split-and-Recombine Approach for Follow-up Query Analysis](https://arxiv.org/abs/1909.08905) (EMNLP2019). In this paper, we propose to achieve context-dependent semantic parsing via performing follow-up query analysis, which aims to restate context-dependent natural language queries with contextual information. To accomplish the task, we propose the approach *Split-And-Recombine(StAR)*, a novel approach with a well-designed two-phase process. # Content - [Setup](#setup) - [Preprocess Data](#preprocess) - [Training Arguments](#arguments) - [Pretrain Model](#pretrain) - [Train Model via Reinforcement Learning](#train) - [Expected Result](#result) | 2,975 |
microsoft/HoloLens2ForCV | ['mixed reality'] | ['HoloLens 2 Research Mode as a Tool for Computer Vision Research'] | Samples/StreamRecorder/StreamRecorderConverter/utils.py Samples/StreamRecorder/StreamRecorderConverter/project_hand_eye_to_pv.py Samples/StreamRecorder/StreamRecorderConverter/hand_defs.py Samples/StreamRecorder/StreamRecorderConverter/recorder_console.py Samples/StreamRecorder/StreamRecorderConverter/process_all.py Samples/StreamRecorder/StreamRecorderConverter/tsdf-integration.py Samples/StreamRecorder/StreamRecorderConverter/save_pclouds.py Samples/StreamRecorder/StreamRecorderConverter/convert_images.py get_width_and_height convert_images write_bytes_to_png get_bones HandJointIndex process_all project_hand_eye_to_pv get_eye_gaze_point load_pv_data match_timestamp process_timestamps load_extrinsics load_rig2world_transforms save_pclouds save_ply save_output_txt_files get_points_in_cam_space save_single_pcloud cam2world extract_timestamp load_lut extract_tar_file project_on_depth project_on_pv load_head_hand_eye_data check_framerates imwrite replace print reshape unlink exists split join list glob print cpu_count apply_async close get_width_and_height Pool array print extract_tar_file stem glob project_hand_eye_to_pv save_pclouds Path mkdir convert_images check_framerates exists print len int reshape len literal_eval zeros float enumerate split norm imwrite projectPoints str sorted list get_eye_gaze_point load_head_hand_eye_data imread range format replace glob mkdir Rodrigues match_timestamp enumerate int print reshape inv zfill load_pv_data array circle len Path str imwrite replace uint16 print astype project_on_depth save_ply shape get_points_in_cam_space cam2world project_on_pv Path match_timestamp imread extract_timestamp array write_point_cloud Vector3dVector estimate_normals array orient_normals_towards_camera_location PointCloud reshape reshape delete tile T hstack inv loadtxt reshape str extract_tar_file cpu_count Path Pool str sorted apply_async save_output_txt_files save_single_pcloud load_extrinsics format load_lut glob close Manager mkdir join print dict load_pv_data array extractall close open format glob print get_avg_delta next len value ones loadtxt zeros range enumerate len projectPoints zeros_like squeeze hstack inv astype logical_and shape zeros array enumerate projectPoints squeeze logical_and astype zeros enumerate | --- page_type: sample name: HoloLens2ForCV samples description: HoloLens 2 Research Mode samples showcasing raw streams on device, including depth camera, gray-scale cameras, and IMU. languages: - cpp products: - windows-mixed-reality - hololens --- | 2,976 |
microsoft/MixingBoard | ['text generation'] | ['MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platform'] | src/cmr/my_utils/word2vec_utils.py src/cmr/san_decoder.py src/cmr/batcher.py src/knowledge.py src/cmr/model.py src/ranker.py src/demo_doc_gen.py src/cmr/my_utils/eval_bleu.py src/cmr/process_raw_data.py src/mrc.py src/cmr/config.py src/cmr/encoder.py src/shared.py src/cmr/dreader.py src/grounded.py src/cmr/my_utils/tokenizer.py src/tts.py src/onmt/modules/__init__.py src/open_dialog.py src/cmr/my_utils/utils.py src/cmr/dropout_wrapper.py src/onmt/Models.py src/cmr/similarity.py src/cmr/my_utils/log_wrapper.py src/cmr/sub_layers.py src/demo_dialog.py src/todo.py src/cmr/dreader_seq2seq.py src/onmt/Optim.py src/onmt/__init__.py src/lm.py src/onmt/modules/GlobalAttention.py src/onmt/Constants.py src/cmr/my_utils/eval_nist.py src/onmt/Dataset.py src/onmt/Beam.py src/onmt/Dict.py src/cmr/common.py src/cmr/my_utils/squad_eval.py src/onmt/Translator.py src/cmr/recurrent.py src/cmr/my_optim.py src/cmr/fetch_realtime_grounding.py cmd_demo Memo web_demo restful_api_demo DialogBackend ApiResource DialogBackendLocal encode_file DialogBackendRemote cmd_demo web_demo restful_api_demo DialogBackend ApiResource DialogBackendLocal DialogBackendRemote ContentTransfer play_grounded JsonConfig OptionContentTransfer ConversingByReading KnowledgeBase extract_keyphrase play_kb pick_top play_lm LanguageModel play_mrc BidafQA DialoGPT play_dpt ScorerInfo play_ranker ScorerRepetition Ranker alnum_only get_api_key pick_tokens play_tts TextToSpeech prepare_batch_data BatchGen load_meta linear init_wrapper activation args2json train_config set_args data_config decoding_config model_config DNetwork DNetwork_Seq2seq DropoutWrapper LexiconEncoder GroudingGenerator DocReaderModel myNetwork WeightNorm EMA _dummy _norm weight_norm load_facts no_label load_conv filter_fact combine_files filter_query combine_fact filter_resp write_files filter_text BRNNEncoder OneLayerBRNN ContextualEmbedV2 ContextualEmbed SANDecoder generate_mask BilinearFlatSim Trilinear MLPSelfAttn SimilarityWrapper DeepAttentionWrapper DotProduct BilinearSum FlatSimV2 SelfAttnWrapper FlatSimilarityWrapper AttentionWrapper FlatSim Bilinear SimpleFlatSim LinearSelfAttn DotProductProject Highway LayerNorm __init__ PositionwiseNN forward _maybe_str_to_list _get_ngrams corpus_bleu is_str _lowercase sentence_bleu _maybe_str_to_list _get_ngrams sentence_nist corpus_nist is_str _lowercase create_logger evaluate_file normalize_answer metric_max_over_ground_truths evaluate get_bleu_moses_score get_bleu get_bleu_moses bleu f1_score exact_match_score reform_text normalize_text Vocabulary space_extend AverageMeter set_environment build_embedding load_glove_vocab Beam Dataset Dict StackedLSTM Decoder NMTModel Encoder Optim Translator GlobalAttention input join print append b64encode read run Memo Flask TextToSpeech add_resource Api Flask run ContentTransfer input print ConversingByReading predict load_document candidate_selection candidate_weighting TopicRank dict get_n_best append sorted print KnowledgeBase predict input input print predict LanguageModel input print predict BidafQA DialoGPT print input predict input join print Ranker open split print TextToSpeech open_audio get_audio input Tensor Vocabulary len LongTensor fill_ min eq max enumerate len add_argument add_argument add_argument add_argument train_config data_config decoding_config ArgumentParser parse_args model_config print dumps __dict__ dim apply append sub split filter_text sub startswith split filter_text sub sub join list print exit chain keys enumerate set load_facts join format print load_conv add set combine_fact listdir len bernoulli Variable size zero_ randint range Parameter zeros ones expand_as tuple range Counter len is_str _maybe_str_to_list _get_ngrams exp Counter _lowercase zip float sum range len _maybe_str_to_list _get_ngrams exp Counter mean _lowercase zip float range len stdout setFormatter getLogger addHandler StreamHandler Formatter DEBUG setLevel INFO FileHandler Counter split sum values len append metric_fn print list map sum array zip join close write encode Popen join read str print write close group float32 encode Popen strip sub seed manual_seed_all manual_seed set print zeros len | # MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platform We present [MixingBoard](https://arxiv.org/abs/2005.08365), a platform for quickly building demos with a focus on knowledge grounded stylized text generation. We unify existing text generation algorithms in a shared codebase and further adapt earlier algorithms for constrained generation. To borrow advantages from different models, we implement strategies for cross-model integration, from the token probability level to the latent space level. An interface to external knowledge is provided via a module that retrieves on-the-fly relevant knowledge from passages on the web or any document collection. A user interface for local development, remote webpage access, and a RESTful API are provided to make it simple for users to build their own demos. # News * July 6, 2020: MixingBoard repo is released on GitHub. * Apr 3, 2020: MixingBoard [paper](https://arxiv.org/abs/2005.08365) is accepted to appear on [ACL 2020](https://acl2020.org/) Demo track. # Setup We recommend using [Anaconda](https://www.anaconda.com/) to setup Firstly, create an environment with Python 3.6 ``` conda create -n mixingboard python=3.6 | 2,977 |
microsoft/art | ['style transfer', 'image retrieval'] | ['MosAIc: Finding Artistic Connections across Culture with Conditional Image Retrieval'] | backend/application.py data_prep/featurize_and_match.py data_prep/download_images.py share allowed_file install home upload ArtDataset check_call get upload_blob b64decode get_blob_client secure_filename print set_http_headers allowed_file split get | <p align="center"> <a href="https://aka.ms/mosaic" target="_blank"> <img src="./media/header-image.jpg" width="80%"/> </a> </p> ## [Live Demo at aka.ms/mosaic](https://aka.ms/mosaic) To access the search functionality, [apply to access the mosaic beta](https://forms.microsoft.com/Pages/DesignPage.aspx#FormId=v4j5cvGGr0GRqy180BHbR3nswihwe8JLvwovyYerymVUQlUzOE9VVDUyQjlJUzRFQ1pQUEJDN001Wi4u) ## About Art is one of the few languages which transcends barriers of country, culture, and time. We aim to create an algorithm that can help discover the common semantic elements of art even between **any** culture, media, artist, or collection within the combined artworks of [The Metropolitan Museum of Art](https://www.metmuseum.org/) and [The Rijksmusem](https://www.rijksmuseum.nl/en). ### Conditional Image Retrieval | 2,978 |
microsoft/methods2test | ['denoising'] | ['Unit Test Case Generation with Transformers and Focal Context'] | scripts/find_map_test_cases.py scripts/TestParser.py parse_test_cases export_mtc analyze_project match_test_cases read_repositories main find_map_test_cases export parse_potential_focal_methods parse_args TestParser join str print call rmtree find_map_test_cases makedirs check_output TestParser exists open str list append match_test_cases parse_test_cases replace export_mtc chdir close set splitlines parse_potential_focal_methods flush join items write index len pop list parse_file dict append pop list parse_file dict append str list replace write index set intersection append len read loads isfile pop deepcopy join str export add_argument ArgumentParser parse_args analyze_project | # Unit Test Generation Task The task of Automated Unit Test Case generation has been the focus of extensive research in software engineering community. Existing approaches are usually guided by test coverage criteria and generate synthetic test cases that are often difficult to read or understand even for developers familiar with the code base. # Dataset Description We introduce `methods2test`: a supervised dataset consisting of Test Cases and their corresponding Focal Methods from a large set of Java software repositories. To extract `methods2test`, we first parsed the Java projects to obtain classes and methods with their associated metadata. Next we identified each Test Class and its corresponding Focal Class. Finally, for each Test Case within a Test Class, we mapped it to the related Focal Method and obtain a set of Mapped Test Cases. ## Accessing via Git LFS The repository makes use of the Git large file storage (LFS) service. Git LFS does replacing large files in the repository with tiny pointer files. To pull the actual files do: ```bash # first, clone the repo git clone [email protected]:microsoft/methods2test.git # next, change to the methods2test folder | 2,979 |
midm/FastPhotoStyle | ['image stylization'] | ['A Closed-form Solution to Photorealistic Image Stylization'] | demo.py process_stylization_folder.py process_stylization_ade20k_ssn.py photo_smooth.py converter.py models.py download_models.py photo_gif.py demo_with_ade20k_ssn.py smooth_filter.py photo_wct.py process_stylization.py photo_wct_loader weight_assign segment_this_img download_file_from_google_drive save_response_content get_confirm_token VGGDecoder VGGEncoder GIFSmoothing Propagator PhotoWCT ReMapping stylization memory_limit_image_resize Timer overlay stylization visualize_result SegReMapping smooth_local_affine smooth_filter Parameter items float list load load_state_dict padding_constant float transpose min astype float32 copy imgSize from_numpy shape dict unsqueeze round2nearest_multiple resize transform imread append get get_confirm_token save_response_content Session items list startswith height print thumbnail BICUBIC width Canny uint8 ones dilate range zeros astype unique 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 | [](https://raw.githubusercontent.com/NVIDIA/FastPhotoStyle/master/LICENSE.md)   Script to setup a Ubuntu 16.04 to use FastPhotoStyle from Nvidia (https://github.com/NVIDIA/FastPhotoStyle) (requires a Nvidia GPU that supports CUDA 9.1) # How to use : `wget https://github.com/micodeyt/FastPhotoStyle/edit/master/setup.sh` `bash -i setup.sh` // (can take a while) `source ~/.bashrc` | 2,980 |
mihaidusmanu/cross-descriptor-vis-loc-map | ['visual localization', 'mixed reality'] | ['Cross-Descriptor Visual Localization and Mapping'] | feature-utils/extract_sift.py train.py lib/datasets.py feature-utils/convert_database_to_numpy.py local-feature-evaluation/align_and_compare.py local-feature-evaluation/utils.py lib/utils.py lib/database.py lib/losses.py local-feature-evaluation/reconstruction_pipeline_embed.py lib/networks.py feature-utils/extract_descriptors.py local-feature-evaluation/reconstruction_pipeline_subset.py local-feature-evaluation/reconstruction_pipeline_progressive.py run_epoch parse_arguments UpdatingMean get_transforms recover_database_images_and_ids BRIEFDescriptor example_usage COLMAPDatabase blob_to_array image_ids_to_pair_id pair_id_to_image_ids array_to_blob TranslationDataset autoencoder_loss subset_loss exhaustive_loss MLP create_network_for_feature qvec_to_rotmat main parse_args translate_database main parse_args main parse_args compute_extra_stats match_features_hybrid reconstruct match_features build_hybrid_database match_features_subset geometric_verification blob_to_array image_ids_to_pair_id translate_descriptors mnn_ratio_matcher array_to_blob print parse_args add_argument ArgumentParser backward set_grad_enabled step zero_grad write tqdm add loss_function set_postfix UpdatingMean to numpy keys flush enumerate Compose execute cursor close connect commit database_path float64 rand ArgumentParser add_keypoints exists add_matches connect parse_args next close create_tables execute add_image remove print add_argument dict add_camera blob_to_array randint list norm T values binary_cross_entropy relu mean unsqueeze to keys cat len norm T binary_cross_entropy relu mean unsqueeze to keys list norm T binary_cross_entropy relu shuffle keys mean unsqueeze zip to array MLP array add_argument ArgumentParser fetchall commit int cursor items remove translate_descriptors close connect copy float32 shape blob_to_array execute database_path batch_size colmap_path set_grad_enabled sparse_path dataset_path geometric_verification save device features exists exp_name image_path load_state_dict parse_args create_network_for_feature reconstruct match_list_path copy eval is_available checkpoint load join compute_extra_stats match_features build_hybrid_database empty_cache translate_database SimpleNamespace match_features_hybrid match_features_subset remove feature topk arange min t sqrt stack device cuda to numpy cat commit cursor values seed sorted list connect range RandomState close shuffle zip execute enumerate join int randint array len commit list cursor astype connect map set add float32 tqdm close blob_to_array image_ids_to_pair_id execute zeros bool uint32 mnn_ratio_matcher commit cursor mnn_ratio_matcher list connect map add image_ids_to_pair_id uint32 astype close set translate_descriptors execute float32 index tqdm blob_to_array zeros bool commit cursor mnn_ratio_matcher open connect add image_ids_to_pair_id uint32 astype close set execute items write float32 tqdm blob_to_array zeros bool cursor close connect call execute join int print check_output call mkdir startswith float listdir split split | # Cross-Descriptor Visual Localization and Mapping This repository contains the implementation of the following paper: ```text "Cross-Descriptor Visual Localization and Mapping". M. Dusmanu, O. Miksik, J.L. Schönberger, and M. Pollefeys. ICCV 2021. ``` [[Paper on arXiv]](https://arxiv.org/abs/2012.01377) ## Requirements ### COLMAP We use COLMAP for DoG keypoint extraction as well as localization and mapping. | 2,981 |
mihaidusmanu/local-feature-refinement | ['camera localization'] | ['Multi-View Optimization of Local Feature Geometry'] | reconstruction-scripts/reconstruction_pipeline.py two-view-refinement/compute_match_graph.py two-view-refinement/model.py two-view-refinement/refinement.py utils/create_exhaustive_matching_list.py two-view-refinement/feature_matchers.py eth/benchmark.py reconstruction-scripts/triangulation_pipeline.py utils/create_image_list_file.py reconstruction-scripts/colmap_utils.py utils/extract_features_sift.py utils/create_starting_database_eth.py custom_demo.py local-feature-evaluation/compare_reconstructions.py utils/create_starting_database.py local-feature-evaluation/benchmark.py utils/create_sequential_matching_list.py utils/extract_features_surf.py parse_args parse_args parse_args recover_images triangulate complete_keypoints reconstruct import_features generate_empty_reconstruction image_ids_to_pair_id array_to_blob main parse_args parse_args mnn_ratio_matcher mnn_similarity_matcher PANet extract_patches_and_estimate_displacements extract_patches estimate_displacements grid_positions refine_matches_coarse_to_fine parse_empty_reconstruction array_to_blob recover_database_images_and_ids add_argument ArgumentParser print int mkdir split commit cursor matches displacements SolutionFile fact exists dj image_pairs connect add call image_ids_to_pair_id append uint32 astype close set execute array_to_blob load join enumerate items di float32 images feature_idx MatchingFile zeros bool join int print check_output call mkdir startswith float listdir split call mkdir join SimpleNamespace database_path colmap_path reconstruct import_features match_list_path copy dataset_path sparse_path image_path matches_file method_name parse_args solution_file exists arange min t stack device max topk arange min t sqrt stack device stack repeat view size squeeze permute to grid_positions forward_sym normalize_batch to forward range cat reshape extract_patches array estimate_displacements extract_patches_and_estimate_displacements pyrUp commit cursor close connect execute execute cursor close connect | # Multi-View Optimization of Local Feature Geometry This repository contains the implementation of the following paper: ```text "Multi-View Optimization of Local Feature Geometry". M. Dusmanu, J.L. Schönberger, and M. Pollefeys. European Conference on Computer Vision 2020 (Oral). ``` [[Paper on arXiv]](https://arxiv.org/abs/2003.08348) [[Project page]](https://dsmn.ml/publications/mvolfg.html) [[Qualitative results]](https://youtu.be/eH4UNwXLsyk) ## Requirements ### C++ | 2,982 |
mikahama/fallout4-dynamic-dialog | ['word embeddings'] | ['Creative Contextual Dialog Adaptation in an Open World RPG'] | communicator/communicator.py app.py communicator/image_maker.py log_reader_daemon.py dialog_generator/dummy_dialog.py start_dialog new_options generate_dialog say show_options _draw_text options_image _trim dialog_image generate_options generate_dialog set_game_state start_dialog new_options generate_dialog say show_options _draw_text options_image _trim dialog_image generate_options generate_dialog set_game_state set_game_state get_json generate_options show_options get say get dialog_image get options_image truetype Draw new _draw_text _trim save len upper text getpixel size new add difference getbbox mode truetype Draw new _draw_text _trim save | mikahama/fallout4-dynamic-dialog | 2,983 |
mikahama/syntaxmaker | ['text generation'] | ['Development of an Open Source Natural Language Generation Tool for Finnish'] | syntaxmaker/head.py syntaxmaker/inflector.py syntaxmaker/syntax_maker.py syntaxmaker/adposition_tool.py syntaxmaker/noun_tool.py syntaxmaker/verb_valence.py setup.py testi.py travis_test.py test/generate_sentences.py test/wiktionary_verbs.py syntaxmaker/phrase.py syntaxmaker/__init__.py syntaxmaker/converter.py syntaxmaker/pronoun_tool.py TestFSTS load_csv preposition_case get_an_adposition postposition_case _noun_morphology convert_UD _node_to_phrase Head backup_inflect case_harmony standard_nominal_inflection has_back_vowels _filter_generated inflect get_locative resolve_locative_case Phrase pronoun is_personal_pronoun is_auxiliary_verb turn_vp_into_prefect turn_vp_into_passive create_adverb_phrase create_adjective_phrase create_verb_pharse load_grammar add_relative_clause_to_np add_np_object_to_vp turn_vp_into_question create_copula_phrase add_possessive_to_np create_adposition_phrase create_personal_pronoun_phrase add_advlp_to_vp create_phrase add_np_subject_to_vp set_vp_mood_and_tense negate_verb_pharse add_auxiliary_verb_to_vp create_noun_phrase inflect_objects most_frequent_case inflect_noun valency_count verb_objects is_copula verb_direct_objects cases_total verb_indirect_objects all_cases_total ValencyException join reader realpath dirname open choice find upostag replace startswith split rsplit replace upper title generate _filter_generated analyze is_personal_pronoun has_back_vowels case_harmony join read close realpath loads dirname open most_frequent_case valency_count verb_direct_objects Phrase verb_indirect_objects keys pronoun create_phrase Phrase insert create_phrase index insert remove append insert index create_phrase lemma insert index create_phrase lemma create_phrase append create_phrase remove insert components resolve_locative_case str startswith append preposition_case postposition_case create_phrase get_an_adposition insert title create_personal_pronoun_phrase float all_cases_total verb_objects verb_objects verb_indirect_objects verb_direct_objects upper read most_frequent_case inflect_noun valency_count verb_direct_objects verb_indirect_objects | Syntax maker ======= [](https://travis-ci.com/mikahama/syntaxmaker) [](https://doi.org/10.5281/zenodo.3483626) The tool NLG tool for Finnish by [Mika Hämäläinen](https://mikakalevi.com) Syntax maker is the natural language generation tool for generating syntactically correct sentences in Finnish automatically. The tool is especially useful in the case of Finnish which has such a high diversity in its morphosyntax. All you need to know are the lemmas and their parts-of-speech and syntax maker will take care of the rest. For instance, just throw in words `rantaleijona`, `uneksia`, `korkea` and `aalto` and you will get `rantaleijonat uneksivat korkeista aalloista`. So you will get the morphology right automatically! Don't believe me? [Just take a look at this tutorial to find out how.](https://github.com/mikahama/syntaxmaker/wiki/Creating-a-sentence,-the-basics) # Installing Run pip install syntaxmaker python -m uralicNLP.download -l fin | 2,984 |
mike-n-7/ADEM | ['response generation', 'dialogue evaluation'] | ['Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses', 'A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues'] | pretrain.py vhred/src/vhred_retrieval.py vhred/src/utils.py train.py vhred/src/eval_model_hred_fair.py vhred/src/adam.py vhred/src/vhred_state.py vhred/src/eval_model_hred.py vhred/src/hred_encoder.py vhred/src/vhred_dialog_encdec.py experiments.py preprocess.py vhred/src/numpy_compat.py apply_bpe.py vhred/src/unfair_eval_model_hred.py models.py interactive.py vhred/src/vhred_compute_dialogue_embeddings.py vhred/src/tfidf_retrieval.py vhred/src/train.py vhred/src/model.py create_parser BPE get_pairs encode paper_config standard_config demo_config AMT_DataLoader Preprocessor Adam sharedX UtteranceEncoder EncoderDecoderBase add_to_params DCGMEncoder DialogEncoder DialogLevelLatentEncoder DialogLevelRollLeft DialogEncoderDecoder DialogDummyEncoder Model Maxout OrthogonalInit GrabProbs LayerNormalization sharedX Adam stable_log Adagrad RMSProp SoftMax NormalInit3D FeedforwardBatchNormalization RecurrentBatchNormalization NormalInit BatchedDot Adadelta ConvertTimedelta UniformInit DPrint NormalizationOperator prototype_twitter_HRED prototype_test prototype_ubuntu_LSTM prototype_twitter_lstm prototype_twitter_VHRED prototype_twitter_VHRED_StandardBias prototype_twitter_VHRED_Large_SkipConnections prototype_twitter_Gauss_VHRED_NormOp prototype_twitter_HRED_NormOp prototype_state prototype_ubuntu_VHRED prototype_twitter_HRED_StandardBias prototype_test_variational prototype_twitter_HRED_Large prototype_ubuntu_HRED create_parser BPE get_pairs encode paper_config standard_config demo_config AMT_DataLoader Preprocessor Adam sharedX UtteranceEncoder EncoderDecoderBase add_to_params DCGMEncoder DialogEncoder DialogLevelLatentEncoder DialogLevelRollLeft DialogEncoderDecoder DialogDummyEncoder Model Maxout OrthogonalInit GrabProbs LayerNormalization sharedX Adam stable_log Adagrad RMSProp SoftMax NormalInit3D FeedforwardBatchNormalization RecurrentBatchNormalization NormalInit BatchedDot Adadelta ConvertTimedelta UniformInit DPrint NormalizationOperator prototype_twitter_HRED prototype_test prototype_ubuntu_LSTM prototype_twitter_lstm prototype_twitter_VHRED prototype_twitter_VHRED_StandardBias prototype_twitter_VHRED_Large_SkipConnections prototype_twitter_Gauss_VHRED_NormOp prototype_twitter_HRED_NormOp prototype_state prototype_ubuntu_VHRED prototype_twitter_HRED_StandardBias prototype_test_variational prototype_twitter_HRED_Large prototype_ubuntu_HRED add_argument ArgumentParser add set get_pairs endswith tuple min extend index append floatX items sharedX sqr get_value sqrt append append sharedX name sqr get_value float32 OrderedDict sqrt keys sharedX name sqr get_value OrderedDict sqrt keys sharedX name sqr get_value OrderedDict sqrt keys minimum int normal permutation svd xrange zeros flatten reshape minimum int normal permutation xrange zeros int NormalInit zeros range exp max dimshuffle minimum sum cast dimshuffle ones_like sum dimshuffle dimshuffle dimshuffle batched_dot prototype_state prototype_state prototype_state prototype_state prototype_state prototype_state prototype_state prototype_state prototype_state prototype_state prototype_state prototype_state prototype_state prototype_state | This repository has the code and parameters used for the ADEM model in: **Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses** Ryan Lowe, Michael Noseworthy, Iulian V. Serban, Nicolas Angelard-Gontier, Yoshua Bengio, and Joelle Pineau Due to the ethics policy for this project, we cannot release the collected human data at this time. However, we do provide the weights/parameters for a trained model and the code to train ADEM with new data. ADEM uses the VHRED model. A modified version of the code is included in this repo. The original repo and paper can be found at: https://github.com/julianser/hed-dlg-truncated https://arxiv.org/abs/1605.06069 You will need to download the weights for the pretrained VHRED model before running the code. Once downloaded from the following link, place all the files in the `./vhred` folder. [https://drive.google.com/file/d/0B-nb1w_dNuMLY0Fad3N1YU9ZOU0/view?usp=sharing](https://drive.google.com/file/d/0B-nb1w_dNuMLY0Fad3N1YU9ZOU0/view?usp=sharing&resourcekey=0-ppk_UWYQCyUXsDvI-s5D7w) | 2,985 |
mil-tokyo/dg_mmld | ['domain generalization'] | ['Domain Generalization Using a Mixture of Multiple Latent Domains'] | model/caffenet.py train/deepall.py util/util.py loss/MaximumSquareLoss.py dataloader/dataloader.py train/eval.py train/general.py model/resnet.py util/scheduler.py model/Discriminator.py clustering/domain_split.py clustering/clustering.py loss/EntropyLoss.py model/alexnet.py dataloader/Dataset.py main/main.py Clustering Agglomerative GMM preprocess_features Spectral Kmeans compute_instance_stat calc_mean_std domain_split reassign compute_features arrange_clustering random_split_dataloader DG_Dataset HLoss MaximumSquareLoss alexnet DGalexnet caffenet AlexNetCaffe Id DGcaffenet GradReverse grad_reverse Discriminator resnet DGresnet train eval_model train inv_lr_scheduler get_model_lr copy_weights show_images get_train set_parameter_requires_grad get_domain split_domain get_disc_dim get_scheduler train_to_get_label get_model get_optimizer norm PCA astype shape fit_transform var size view size linear_sum_assignment zeros max range eval enumerate print shape eval enumerate argsort extend enumerate len set_transform format images_lists normalized_mutual_info_score print compute_instance_stat labels DataLoader split arrange_clustering reassign compute_features cluster domains len deepcopy DG_Dataset random_split set_transform format print DataLoader len print in_features bias load_url AlexNet xavier_uniform_ load_state_dict weight constant_ Linear load isinstance bias xavier_uniform_ modules load_state_dict AlexNetCaffe weight constant_ Linear in_features bias xavier_uniform_ resnet18 weight constant_ Linear format criterion model backward print dataset zero_grad double to step max CrossEntropyLoss len format print eval dataset CrossEntropyLoss len MaximumSquareLoss HLoss class_criterion exp set_lambd entropy_criterion domain_criterion show set_size_inches set_title add_subplot gray imshow figure zip ceil get_size_inches float array enumerate len print parameters print deepcopy append parameters SGD named_parameters clone modules zip | # Domain Generalization Using a Mixture of Multiple Latent Domains  This is the pytorch implementation of the AAAI 2020 poster paper "Domain Generalization Using a Mixture of Multiple Latent Domains". ## Requirements - A Python install version 3.6 - A PyTorch and torchvision installation version 0.4.1 and 0.2.1, respectively. [pytorch.org](https://pytorch.org/) - The caffe model we used for [AlexNet](https://drive.google.com/file/d/1wUJTH1Joq2KAgrUDeKJghP1Wf7Q9w4z-/view?usp=sharing) - PACS dataset ([website](https://dali-dl.github.io/project_iccv2017.html), [dateset](https://drive.google.com/drive/folders/0B6x7gtvErXgfUU1WcGY5SzdwZVk?resourcekey=0-2fvpQY_QSyJf2uIECzqPuQ)) - Install python requirements ``` | 2,986 |
milandesai/yolov3 | ['action recognition'] | ['Spatiotemporal Action Recognition in Restaurant Videos'] | train.py custom/format.py utils/utils.py utils/google_utils.py utils/datasets.py utils/parse_config.py test.py models.py copy_model.py utils/adabound.py convlstm.py detect.py utils/torch_utils.py test_convlstm.py custom/generate_clips.py ConvLSTMCell ConvLSTM detect Swish YOLOLayer attempt_download LSTMLayer SwishImplementation create_grids load_darknet_weights convert MemoryEfficientSwish get_yolo_layers Mish create_modules weightedFeatureFusion Darknet save_weights test train AdaBoundW AdaBound LoadWebcam reduce_img_size exif_size letterbox cutout augment_hsv LoadStreams VideoDataLoader create_folder LoadImages load_mosaic recursive_dataset2bmp imagelist2folder random_affine load_image convert_images2bmp LoadImagesAndLabels gdrive_download upload_blob download_blob parse_model_cfg parse_data_cfg fuse_conv_and_bn model_info load_classifier scale_img init_seeds select_device time_synchronized ModelEMA compute_ap plot_images plot_evolution_results scale_coords plot_results plot_one_box xywh2xyxy labels_to_image_weights smooth_BCE plot_results_overlay init_seeds compute_loss ap_per_class fitness build_targets plot_wh_methods coco80_to_coco91_class get_yolo_layers print_mutation load_classes select_best_evolve FocalLoss apply_classifier non_max_suppression strip_optimizer plot_targets_txt create_backbone coco_single_class_labels coco_class_weights print_model_biases xyxy2xywh box_iou wh_iou labels_to_class_weights kmean_anchors coco_class_count clip_coords plot_test_txt weights_init_normal crop_images_random bbox_iou coco_only_people imwrite CAP_PROP_FRAME_HEIGHT endswith load_darknet_weights LoadImages plot_one_box Darknet unsqueeze Path export VideoWriter CAP_PROP_FPS sep round VideoWriter_fourcc exists iou_thres release str fuse name getcwd load_classifier half load_classes imshow load_state_dict printable_graph select_device names to apply_classifier sum non_max_suppression attempt_download get replace cfg eval unique conf_thres CAP_PROP_FRAME_WIDTH enumerate load int time isinstance print graph LoadStreams check_model write system rmtree time_synchronized zeros makedirs LSTMLayer Sequential ModuleList weightedFeatureFusion log YOLOLayer Parameter Swish view MaxPool2d append LeakyReLU sum nc na add_module enumerate pop print ZeroPad2d extend bias Conv2d Upsample BatchNorm2d view stride img_size meshgrid to type max name numel bias copy_ running_mean zip running_var weight view_as enumerate endswith print load_darknet_weights Darknet save_weights load_state_dict save name gdrive_download print system plot_images endswith VideoDataLoader tuple load_darknet_weights summarize scale_coords Darknet xywh2xyxy DataParallel device ap_per_class max fuse view coco80_to_coco91_class tolist numel load_classes shape int64 COCO loadRes load_state_dict select_device append to bincount attempt_download glob astype accumulate eval nonzero unique zip float xyxy2xywh enumerate int remove clip_coords COCOeval print evaluate parse_data_cfg clone tqdm zeros len data named_modules plot_images update_attr batch_size endswith LambdaLR VideoDataLoader load_darknet_weights lf model SGD zero_grad plot_results DistributedDataParallel set_description yolo_layers init_seeds randrange interpolate compute_loss save rename round max fitness initialize list name step Adam adam load_state_dict to multi_scale range attempt_download update init_process_group glob param_groups bucket test cfg accumulate print_model_biases add_param_group zip float enumerate load items remove time add_image backward print add_scalar reshape weights parse_data_cfg min system tqdm empty_cache zeros epochs ModelEMA len size imread max img_size resize uint8 COLOR_HSV2BGR astype uniform clip cvtColor concatenate load_image len copy img_size random_affine isfile append zeros clip enumerate copyMakeBorder isinstance min resize BORDER_CONSTANT max warpAffine T tan ones reshape maximum pi getRotationMatrix2D uniform eye clip len int bbox_ioa min array randint max create_folder replace imwrite glob tqdm resize imread max create_folder replace imwrite glob tqdm imread suffix replace imwrite system tqdm imread walk create_folder rmtree exists makedirs time remove print endswith system exists blob format get_bucket print Client upload_from_filename blob format get_bucket print Client download_to_filename isnumeric rstrip reshape strip startswith append sep split dict sep strip split manual_seed print device_count range len replace print named_parameters profile sum enumerate print zeros eval Parameter interpolate seed bincount int astype concatenate sum array len Tensor data normal_ __name__ constant_ max clip_coords clamp_ compute_ap cumsum argsort unique interp sum range enumerate concatenate trapz accumulate linspace interp sum flip clamp min pi t pow atan max t prod box_area prod dtype zeros_like build_targets clamp sigmoid t gr smooth_BCE type full_like bbox_iou hyp BCEWithLogitsLoss enumerate cat len view t repeat yolo_layers append max cat wh_iou nms sum t xywh2xyxy enumerate mm max triu_ cat len print na yolo_layers view load save load values save sorted glob print reshape zeros enumerate len sorted all glob reshape print enumerate print loadtxt sorted glob int sorted imwrite glob min tqdm randint imread max sorted replace glob copyfile tqdm rmtree any exists makedirs kmeans random shapes fitness max clip all ones array append range LoadImagesAndLabels print_results zip print labels tqdm repeat Tensor std print tuple loadtxt system savetxt unique keys values len argmax transpose clone scale_coords ascontiguousarray shape unsqueeze resize append xyxy2xywh long enumerate putText rectangle max exp arange plot xlabel ylabel tight_layout ylim savefig figure legend xlim numpy T plot name transpose min axis tight_layout close shape imshow title savefig figure ceil numpy range set_aspect subplots loadtxt tight_layout hist hist2d savefig xyxy2xywh T subplots set_title tight_layout hist savefig legend ravel range subplot max items plot print loadtxt rc min tight_layout title savefig figure fitness enumerate T sorted subplots set_title plot replace glob tight_layout savefig nan legend ravel range join T sorted subplots set_title plot glob system tight_layout savefig nan legend ravel range | # Recurrent YOLO with Agot dataset This README contains instructions for running both non-recurrent and recurrent YOLO on the Agot dataset, as described in [Spatiotemporal Action Recognition on Restaurant Videos](report.pdf). The code is a modification of the [YOLOv3 implementation by Ultralytics LLC](https://github.com/ultralytics/yolov3). The [original README](README-ultralytics.md) has more detailed setup instructions and information, should any problems arise with the below instructions. ## Branches - `base`: for comparing changes; we forked off of this commit from the Ultralytics repository - `non-recurrent`: for training/testing regular YOLO on the Agot dataset - `master`: for training Recurrent YOLO on the Agot dataset ## Prerequisites 1. Run: `pip install -U -r requirements.txt` 2. Obtain weight files from authors (too large to put in Git) | 2,987 |
mileyan/DARENet | ['person re identification'] | ['Resource Aware Person Re-identification across Multiple Resolutions'] | Datasets.py budgeted_stream/evaluation.py models/dare_densenet.py budgeted_stream/utils/logging.py create_market_dataset.py models/dare_models.py budgeted_stream/confidence_functions.py budgeted_stream/main.py my_logger.py budgeted_stream/val_confidence.py models/dare_resnet.py main.py extract_features.py budgeted_stream/budgeted_stream_plot.py main RandomErasing CUHK03EvaluateDataset MARSEvalDataset TrainingDataset Market1501EvaluateDataset extract_features_CUHK03 extract_stage_features extract_features_Market1501 extract_features extract_features_MARS adjust_lr_adam AverageMeter OptiHardTripletLoss save_checkpoint main train setup_logger main parse_args max_neg_dist_function margin_function get_rank get_good_junk evaluate get_ap get_budget_given_p get_p_given_budget_random get_eu_distance get_p_given_budget get_junk load_distances load_labels test parse_args gen_exit_stage get_junk gen_val_confidence get_eu_distance get_threshold_offline get_colored_logger set_colored_logger _add_handler add_file_handle densenet161 DenseNet densenet201 densenet169 _DenseLayer _DenseBlock _Transition densenet121 dare_R dare_D ResNet resnet50 Bottleneck conv3x3 BasicBlock join copy path parse_args listdir makedirs join Compose MARSEvalDataset DataLoader savemat Normalize info extract_features makedirs join format Compose extract_stage_features extract_features DataLoader savemat Normalize info range Market1501EvaluateDataset makedirs join CUHK03EvaluateDataset Compose DataLoader savemat Normalize info extract_features makedirs str T view Variable reshape print mean eval append numpy net range enumerate str view Variable reshape print mean eval append numpy net enumerate data pretrained DataLoader save_checkpoint arch start_iteration cuda str sorted extract_features_folder Adam crop_size TrainingDataset load_state_dict log_path extract_features extract_features_MARS extract_features_CUHK03 format setup_logger extract_features_Market1501 resume Normalize info checkpoint_folder load items info_folder error min parameters isfile train len update time format criterion model adjust_lr_adam Variable size AverageMeter zero_grad backward step info append sum enumerate copyfile join save max_iter lr_decay_point param_groups lr enumerate setFormatter getLogger addHandler StreamHandler Formatter set_name dirname DEBUG setLevel FileHandler makedirs add_argument ArgumentParser result_path open min range len range append squeeze logical_and argwhere squeeze get_ap argsort mean get_rank append zeros range get_good_junk dot T sum tile append roots sum range append range range logical_and query_exit ones get_junk logical_and logical_not copy max_neg_dist_function margin_function argwhere append zeros range len join array sort dataset_path T get_eu_distance sqrt append range dump_distance_mat dump_exit_history logger len get_p_given_budget confidence_function append sum range get_budget_given_p format dump save_path get_p_given_budget_random load_distances mean info zeros join evaluate makedirs savemat test_q load_labels gen_exit_stage ones sort logical_and append sum range len get_junk logical_not max_neg_dist_function list get_colored_logger append range format group set sqrt info T remove sort get_eu_distance match margin_function zeros array len getLevelName setLevel _add_handler getLogger addHandler debug name format setFormatter makedirs set_name _add_handler dirname FileHandler update DenseNet load_url load_state_dict state_dict update DenseNet load_url load_state_dict state_dict update DenseNet load_url load_state_dict state_dict load_url load_state_dict DenseNet update ResNet load_url load_state_dict state_dict | # Resource Aware Person Re-identification across Multiple Resolutions This repository contains the code for paper "[Resource Aware Person Re-identification across Multiple Resolutions](https://arxiv.org/abs/1805.08805)" (CVPR 2018). ## Citation ``` @inproceedings{wang2018resource, title={Resource Aware Person Re-identification across Multiple Resolutions}, author={Wang, Yan and Wang, Lequn and You, Yurong and Zou, Xu and Chen, Vincent and Li, Serena and Huang, Gao and Hariharan, Bharath and Weinberger, Kilian Q}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, | 2,988 |
milkplz/keras-frcnn | ['person re identification'] | ['Harmonious Attention Network for Person Re-Identification'] | measure_map.py keras_frcnn/roi_helpers.py test_frcnn.py keras_frcnn/config.py keras_frcnn/resnet.py keras_frcnn/data_augment.py train_frcnn.py keras_frcnn/pascal_voc_parser.py keras_frcnn/simple_parser.py keras_frcnn/RoiPoolingConv.py keras_frcnn/FixedBatchNormalization.py keras_frcnn/data_generators.py keras_frcnn/losses.py keras_frcnn/vgg.py format_img get_map get_real_coordinates format_img_channels format_img_size format_img Config augment threadsafe_generator calc_rpn iou get_new_img_size SampleSelector threadsafe_iter intersection union get_anchor_gt FixedBatchNormalization class_loss_cls rpn_loss_regr rpn_loss_cls class_loss_regr get_data conv_block get_img_output_length nn_base classifier_layers conv_block_td classifier identity_block identity_block_td get_weight_path rpn RoiPoolingConv rpn_to_roi apply_regr calc_iou non_max_suppression_fast apply_regr_np get_data get_img_output_length nn_base classifier rpn get_weight_path append int iou array int transpose astype float32 expand_dims shape im_size resize float int shape im_size resize float expand_dims transpose astype float32 format_img_channels format_img_size int round deepcopy rot_90 transpose imread flip min max union intersection int float where anchor_box_ratios log transpose logical_and expand_dims range rpn_stride concatenate astype anchor_box_scales sample img_length_calc_function float enumerate iou float32 zeros len calc_rpn transpose astype get_new_img_size shuffle SampleSelector float32 shape im_size expand_dims resize augment join int parse print text waitKey imshow rectangle getroot find findall float round imread append len str str str str conv_block identity_block Input identity_block_td conv_block_td classifier_layers get_new_img_size round log classifier_regr_std im_size append range rpn_stride format concatenate float enumerate int deepcopy iou print zeros array len int exp round float64 exp astype round minimum concatenate assert_array_less astype maximum delete argsort append len minimum apply_regr_np arange reshape transpose maximum where anchor_box_ratios delete anchor_box_scales meshgrid zeros std_scaling rpn_stride print | # keras-frcnn Keras implementation of Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. USAGE: - Both theano and tensorflow backends are supported. However compile times are very high in theano, and tensorflow is highly recommended. - `train_frcnn.py` can be used to train a model. To train on Pascal VOC data, simply do: `python train_frcnn.py -p /path/to/pascalvoc/`. - the Pascal VOC data set (images and annotations for bounding boxes around the classified objects) can be obtained from: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar - simple_parser.py provides an alternative way to input data, using a text file. Simply provide a text file, with each line containing: `filepath,x1,y1,x2,y2,class_name` | 2,989 |
min2209/dwt | ['template matching', 'instance segmentation', 'semantic segmentation'] | ['Deep Watershed Transform for Instance Segmentation'] | DN/lossFunction.py DN/train_direction.py DN/ioUtils.py E2E/forward.py E2E/forward_e2e.py E2E/io_utils.py WTN/ioUtils.py E2E/network_init.py WTN/depth_model.py E2E/model_io.py E2E/post_process.py WTN/train_depth.py WTN/lossFunction.py E2E/main.py DN/direction_model.py E2E/e2e_model.py E2E/train.py E2E/loss_function.py read_ids read_mat Batch_Feeder write_mat countTotal countCorrect angularErrorTotal exceedingAngleThreshold countTotalWeighted angularErrorLoss read_ids image_scaling ssProcess pad read_mat write_mat Batch_Feeder countTotal modelTotalLoss countCorrect depthCELoss2 countTotalWeighted depthCELoss get_model watershed_cut read_ids read_mat Batch_Feeder write_mat countTotal modelTotalLoss countCorrect depthCELoss2 countTotalWeighted depthCELoss save countTotal angularErrorTotal get_collection add_to_collection add_n astype float32 zeros uint8 astype float32 get_collection depthCELoss2 countTotalWeighted add_to_collection add_n uint16 astype remove_small_holes int32 label zeros remove_small_objects keys binary_dilation | ## Deep Watershed Transform Performs instance level segmentation detailed in the following paper: Min Bai and Raquel Urtasun, Deep Watershed Transformation for Instance Segmentation, in CVPR 2017. Accessible at https://arxiv.org/abs/1611.08303. This page is still under construction. ## Dependencies Developed and tested on Ubuntu 14.04 and 16.04. 1) TensorFlow www.tensorflow.org 2) Numpy, Scipy, and Skimage (sudo apt-get install python-numpy python-scipy python-skimage) ## Inputs 1) Cityscapes images (www.cityscapes-dataset.com). | 2,990 |
mindslab-ai/voicefilter | ['speech recognition'] | ['VoiceFilter-Lite: Streaming Targeted Voice Separation for On-Device Speech Recognition'] | utils/train.py model/model.py model/embedder.py trainer.py utils/audio.py generator.py utils/adabound.py inference.py utils/hparams.py utils/writer.py datasets/dataloader.py utils/plotting.py utils/evaluation.py vad_merge formatter train_wrapper test_wrapper mix main VFDataset create_dataloader SpeechEmbedder LinearNorm VoiceFilter AdaBound Audio validate HParam Dotdict merge_dict load_hparam_str load_hparam fig2np plot_spectrogram_to_numpy train MyWriter append list split load join int write_wav wav2spec formatter sample_rate wav trim audio_len from_numpy mag save out_dir dvec abs max sample mix choice sample mix choice eval train MSELoss join items load_all list dict open items list __setitem__ __delitem__ __getitem__ __setitem__ __delitem__ __getitem__ reshape tostring_rgb fromstring subplots xlabel draw close ylabel colorbar tight_layout imshow fig2np validate embedder model zero_grad warning save cuda list embedder_path Adam MSELoss load_state_dict append detach Audio eval stack info item log_training load join criterion backward error AdaBound parameters step | # VoiceFilter ## Note from Seung-won (2020.10.25) Hi everyone! It's Seung-won from MINDs Lab, Inc. It's been a long time since I've released this open-source, and I didn't expect this repository to grab such a great amount of attention for a long time. I would like to thank everyone for giving such attention, and also Mr. Quan Wang (the first author of the VoiceFilter paper) for referring this project in his paper. Actually, this project was done by me when it was only 3 months after I started studying deep learning & speech separation without a supervisor in the relevant field. Back then, I didn't know what is a power-law compression, and the correct way to validate/test the models. Now that I've spent more time on deep learning & speech since then (I also wrote a paper published at [Interspeech 2020](https://arxiv.org/abs/2005.03295) 😊), I can observe some obvious mistakes that I've made. | 2,991 |
minghanz/c3d | ['depth estimation'] | ['Monocular Depth Prediction through Continuous 3D Loss'] | c3d/utils/cam_proj.py c3d/cvo_ops/custom_dense/setup.py c3d/utils/io.py c3d/utils/dataset_kitti.py c3d/cvo_ops/custom_dense_with_normal/setup.py c3d/cvo_ops/custom_ori/sub.py c3d/c3d_loss_knn.py c3d/dep_loss.py c3d/utils_general/timing.py c3d/cvo_ops/__init__.py c3d/cvo_ops/custom_cross_subtract/cross_subtract.py c3d/c3d_loss.py c3d/utils_general/dataset_find.py c3d/utils/__init__.py c3d/utils_general/eval_script.py c3d/cvo_ops/custom_ori/setup.py c3d/utils_general/argparse_f.py c3d/utils/geometry.py c3d/utils_general/color.py c3d/utils/pc3d.py c3d/cvo_ops/custom_norm/sub_norm.py c3d/utils_general/pcl_funcs.py c3d/utils_general/eval.py c3d/utils_general/io.py c3d/cvo_ops/custom_norm/setup.py c3d/c3d_test.py c3d/cvo_ops/custom_cross_prod/cross_prod.py c3d/cvo_ops/custom_dense_Sigma/setup.py c3d/cvo_ops/custom_cross_subtract/setup.py c3d/__init__.py c3d/utils/cam.py c3d/cvo_ops/custom_dense_angle/setup.py c3d/c3d_loader.py c3d/utils_general/calib.py c3d/utils_general/__init__.py c3d/pho_loss.py c3d/cvo_ops/custom_dense_normal/setup.py c3d/utils_general/vis.py c3d/utils_general/dataset_read.py c3d/cvo_funcs.py c3d/cvo_ops/custom_cross_prod/setup.py c3d/cvo_ops/custom_dense_dist/setup.py c3d/utils/cam_proj_old.py setup.py c3d/utils_general/tensorboard_event.py seq_ops_on_pts seq_ops_on_K_pts C3DLoader PCL_C3D_Grid PCL_C3D_Flat transform_pc3d sub2ind load_pc3d C3DLoss PCL_C3D load_simp_pc3d relative_T flow_pc3d pcl_from_knnidx knn_pcl mask_from_pcl C3dLossKnnBtwnGT C3DLossKnn load_pcl_from_unpacked create_sample_input PtSampleInGridAngle PtSampleInGridWithNormal PtSampleInGridCalcNormal cross_subtract CrossSubtractFunction cross_prod PtSampleInGrid PtSampleInGridSigmaGrid CrossProdFunction SubNormFunction PtSampleInGridSigma DepthL1Loss wrap_xyz_group wrap_image PhoLoss reproj_error_group_to_side_batch relative_T_to_side compute_reprojection_loss SSIM wrap_xyz_group_to_side wrap_xyz reproj_error_group_to_side relative_T reproj_error_group CrossProdFunction CrossSubtractFunction SubNormFunction SubFunction scale_depth_from_lidar crop_and_scale_depth_from_lidar scale_depth_torch_through_pil_batch scale_image gen_uv_grid xy1_from_uv scale_depth_torch_through_pil extract_single_op np2Image set_from_intr CamInfo CamInfo_from_K_batched CamProj CamInfo_from_InExs seq_ops_on_cam_info batch_cam_infos CamInfo seq_ops_on_cam_info CamProjOld preload_K tan_from_grad normal_from_tan SobelGrad get_stat_lidar_dist calc_normal NormalFromDepthDense res_normal_dense recall_grad normal_from_grad save_nkern grid_from_concat_flat_func init_argparser_f convert_arg_line_to_args crop_and_scale_K sub2ind lidar_to_proj_pts scale_from_size flip_K scale_K lidar_to_depth projected_pts_to_img InExtrKunit InExtr K_mat2py crop_K hsv_to_rgb rgbmap rgb_to_hsv DataFinder DataFinderVKITTI2 DataFinderKITTI DataFinderWaymo retrieve_at_level DataReaderVKITTI2 DataReaderKITTI inex_from_calib_kitti DataReaderWaymo DataReader AverageMeter compute_errors eval_depth_error Metrics eval_preprocess read_ground_truth_depth load_velodyne_points write_np_to_txt_like_kitti read_calib_file pcl_from_grid_xy1_dep pcl_vis pcl_from_np_single pcl_read pcl_clip_distance pcl_xyzi2xyzrgb pcl_write pcl_vis_seq pcl_load_viewer_fromfile pcl_load_viewpoint pcl_from_flat_xyz event_modify_tags save_images_from_event event_reduce_image event_reduce_image_given_tags event_merge event_fpath_from_dir event_view event_extract_tags TimingSingerLayer Timing uint8_np_from_img_np overlay_dep_on_rgb vis_depth_err overlay_dep_on_rgb_np vis_normal vis_depth_np uint8_np_from_img_tensor uint16_np_from_img_np save_np_to_img uint16_np_from_img_tensor mask_from_dep_np vis_pts_dist save_np_img_single comment_on_img visdepth2realdepth_np vprint vis_depth dep_img_bw hori isinstance vert flip scale crop hori isinstance vert int reshape uvb append sum range cat EasyDict reshape min_dist_2 uvb shape load_simp_pc3d res_normal_dense ignore_ib normal_nrange calc_normal dense_nml_op grid_from_concat_flat_func split PCL_C3D_Flat nb feature matmul append relative_T range cat append inverse range matmul PCL_C3D_Grid PCL_C3D_Flat round log mask matmul min_dist_2 shape range cat detach grid_from_concat_flat_func reshape uvb any ignore_ib normal_nrange calc_normal split reshape transpose Pointclouds matmul dense_normal_op min_dist_2 res_normal_dense ignore_ib normal_nrange append calc_normal range cat knn_points num_points_per_cloud points_padded num_points_per_cloud range zeros_like len reshape points_padded flatten stack append range int max items ones print min randint dict CamInfo_from_InExs InExtr eye cuda zeros numpy array range apply apply isinstance wrap_xyz_group reshape reproj_error_group_to_side_batch relative_T_to_side mean wrap_xyz_group_to_side unpack relative_T reproj_error_group append wrap_xyz stack range append wrap_xyz range reshape permute matmul grid_sample min compute_reprojection_loss append range cat len min compute_reprojection_loss stack append range len append min range compute_reprojection_loss mean abs append inverse range matmul op_type type int astype float32 from_numpy meshgrid range concatenate ones reshape matmul cat scale_K reshape inv copy from_numpy repeat gen_uv_grid xy1_from_uv to range fromarray transpose uint8 astype int ndarray NEAREST isinstance Image squeeze transpose BILINEAR astype float32 from_numpy interpolate resize array np2Image append scale_depth_torch_through_pil to range NEAREST ToPILImage transpose BILINEAR from_numpy resize to array scale_K int lidar_to_depth int lidar_to_depth crop_and_scale_K height all isinstance CamInfo_from_K_batched from_numpy stack any width CamInfo dtype reshape repeat inverse gen_uv_grid xy1_from_uv device to range scale crop isinstance CamInfo height width range cat join read_calib_file scale_K reshape hstack astype float32 inv dot int32 InExtr K_mat2py listdir reshape min clone sqrt stack to sum max requires_grad norm ones_like view get_stat_lidar_dist clamp contiguous register_hook where apply sqrt pow normalize sum reshape norm sum meshgrid arange cat cross meshgrid reshape arange cat uint8_np_from_img_np int save_np_to_img sqrt append numpy grid_from_concat_flat_func split zeros unsqueeze split ArgumentParser copy copy scale_from_size detach copy detach scale_K int crop_K copy detach projected_pts_to_img lidar_to_proj_pts log T scale_K transpose copy matmul dot round log detach int sub2ind clip clamp min shape zeros log len ones_like where zeros_like where stack floor expand_as range invert arange reshape min astype shape floor zeros max reshape hstack astype float32 inv dot int32 InExtr K_mat2py update maximum mean sqrt dict log10 abs log int ones_like mean unsqueeze interpolate resize Tensor numpy eval_preprocess isinstance astype float32 open set str join format ndarray isinstance reshape write reshape append_fields concatenate reshape create_xyzrgb create_xyzi create_normal create_xyz pcl_from_np_single swapaxes append numpy range pcl_from_np_single numpy append moveaxis range format save_pcd load_pcd dtype to_ndarray dict empty PointCloud len to_ndarray create_xyzrgb stack rgbmap array split Visualizer addCoordinateSystem setCameraPosition pcl_load_viewpoint setSize format isinstance addPointCloud spinOnce saveScreenshot close addPointCloudNormals removeAllPointClouds spin pcl_load_viewer_fromfile setPointCloudRenderingProperties enumerate Visualizer isinstance addPointCloud addCoordinateSystem spin string decode_image placeholder InteractiveSession join flush wall_time close FileWriter add_event WhichOneof Event event_fpath_from_dir step Summary summary_iterator value tag append event_fpath_from_dir summary_iterator event_reduce_image_given_tags event_extract_tags value print tag WhichOneof event_fpath_from_dir step summary_iterator close FileWriter add_event event_fpath_from_dir flush summary_iterator flush format value wall_time close FileWriter tag add_event Event append event_fpath_from_dir step Summary summary_iterator uint8 astype uint8_np_from_img_np transpose squeeze uint8 min astype squeeze transpose uint16_np_from_img_np min astype uint16 clamp zeros_like where clip zeros_like where join uint8_np_from_img_tensor imwrite mkdir uint8_np_from_img_np isinstance transpose mask_from_dep_np Tensor abs uint8_np_from_img_np isinstance transpose Tensor max putText format FONT_HERSHEY_SIMPLEX uint8_np_from_img_tensor join uint8 imwrite astype dstack mask_from_dep_np COLOR_RGB2BGR bitwise_and add mkdir rgbmap cvtColor vprint save_np_img_single format range fromarray imwrite dirname save makedirs print | # c3d This package implements the C3D loss (continuous 3D loss) in the paper [Monocular Depth Prediction Through Continuous 3D Loss](https://arxiv.org/abs/2003.09763). The paper is accepted by IROS 2020. Video Demo: [](https://youtu.be/gDfAfD4yHuM) This repo contains the python package for computing Continuous 3D Loss. For complete networks conducting monocular depth prediction trained with this loss, please check this [DORN](https://github.com/minghanz/SupervisedDepthPrediction) implementation. Implementation of the C3D loss in BTS and Monodepth2 network will be released soon. ## Overview The interface for using the C3D loss is the `C3DLoss` class in `c3d_loss.py`. You can import the class to your script to augment your own depth prediction project with C3D loss. The implementation of its operations are in `cvo_funcs.py` and `cvo_ops` folder. The class also needs some other util classes to run properly, which you can find in `utils` folder. ## Getting Started ### Prerequisite | 2,992 |
minhmanho/deep_preset | ['style transfer'] | ['Deep Preset: Blending and Retouching Photos with Color Style Transfer'] | networks/network.py run.py networks/blurpool.py networks/components.py networks/norm.py utils.py dp.py DeepPreset ToTensor main size_str2tuple PresetHandler get_pad_layer BilinearUpsample get_blurpool Downsample get_subsampler BasicLayer get_block get_layer ResLayer ConvBlock Net get_model EvoNorm2D group_std instance_std get_norm content join stylize format replace print sort style add_argument len DeepPreset ArgumentParser parse_args out enumerate makedirs print ReplicationPad2d ZeroPad2d ReflectionPad2d zeros any shape expand_as reshape size expand_as | # Deep Preset: Blending and Retouching Photos with Color Style Transfer (WACV'2021) #### [[Page]](https://minhmanho.github.io/deep_preset/) [[Paper]](https://arxiv.org/abs/2007.10701) [[SupDoc]](https://openaccess.thecvf.com/content/WACV2021/supplemental/Ho_Deep_Preset_Blending_WACV_2021_supplemental.pdf) [[SupVid]](https://drive.google.com/file/d/1hF7clPr6jitjDRBCJCiMwTlYjDEknO8P/view?usp=sharing) [[5-min Presentation]](https://drive.google.com/file/d/1WHt3rPXd-FiUOj_Xnb7tQnQJoXAQY9zz/view?usp=sharing) [[Slides]](https://drive.google.com/file/d/1B4aaP-EWIC5zkd35yw-VXiSlWgkJ87o-/view?usp=sharing) #### [Man M. Ho](https://minhmanho.github.io/), [Jinjia Zhou](https://www.zhou-lab.info/jinjia-zhou)  ## Prerequisites - Ubuntu 16.04 - Pillow - [PyTorch](https://pytorch.org/) >= 1.1.0 - Numpy - gdown (for fetching pretrained models) | 2,993 |
minoriwww/MeterDetection | ['time series'] | ['Deep Learning Detection of Inaccurate Smart Electricity Meters: A Case Study'] | draw_k_lstm.py single_input_wave.py change_bomb_rate.py check.py samples.py svr.py input.py combine_model.py more_lstm.py data_processing0.py bomb.py single_bomb_wave.py k_lstm.py bomb random_pick csv_processing rec_plot single_input generate_sample op Combine_model kfold_evaluation_plot single_bomb helper run_cnn date_helper csv_folder_processing convert_y png_folder_processing check csv_processing Combine_model kfold_evaluation_plot helper date_helper csv_folder_processing convert_y png_folder_processing super_and_sub common_process_toCSV KW_process date_format month_map caculate22 calculate_UIT week_map year_map AV_process draw_scatter split_CV get_data_single_user draw_line inverse_xy_transform draw_error_line stat_metrics get_data draw_error_bar LSTM2 run_regressor draw_scatter split_CV get_data_single_user draw_line inverse_xy_transform draw_error_line stat_metrics run_network get_data draw_error_bar LSTM2 run_regressor random_pick op rec_plot single_bomb rec_plot single_input helper rename read_excel drop_duplicates list tolist map scatter savefig legend append range update reset_index size SMID zip sample deepcopy print to_datetime to_csv read_csv drop uniform zip print single_bomb random_pick single_input pdist floor squareform update str reset_index single_input rec_plot size yticks axis to_csv drop imshow savefig sample xticks range append makedirs str date_format rec_plot to_datetime yticks axis sort_values to_csv imshow savefig xticks DataFrame makedirs reset_index to_datetime size op rename read_excel sort_values drop_duplicates range to_frame str list int map split enumerate len seed sorted list img_to_array print list_images resize append imread array join csv_processing print endswith isfile zeros listdir walk enumerate print DataFrame copy apply pad sample StandardScaler fit_transform read_csv column_stack VGG16 Adadelta output Model summary Input compile str time print EarlyStopping fit roc_curve Combine_model mean split linspace interp append ModelCheckpoint auc convert_y std predict KFold append csv_folder_processing kfold_evaluation_plot png_folder_processing show arange plot print size tolist apply rename read_csv legend fill_between xlim range drop endswith shape Bidirectional Sequential add Dense ResNet50 LSTM Flatten Dropout subplot ylabel precision_recall_curve ylim array savefig legend plot xlim minimum xlabel maximum figure fill_between where super_and_sub DataFrame to_csv sort_values rename read_excel sum drop_duplicates merge to_numeric unstack read_csv apply multiply index apply reindex DataFrame xs reset_index multiply select index apply reindex sum reset_index endswith apply rename read_excel drop_duplicates read_csv list list list to_datetime map apply MaxAbsScaler str columns print tolist copy shape as_matrix inverse_transform train_test_split DataFrame fit_transform read_csv column_stack MaxAbsScaler columns print tolist draw_error_line copy shape draw_error_bar as_matrix inverse_transform DataFrame fit_transform read_csv column_stack print reshape inverse_transform append print reshape copy shape append train_test_split array range len time Adadelta print Sequential Adam SGD add RMSprop Dense LSTM Activation BatchNormalization compile Dropout str list grid add_subplot tight_layout bar title savefig figure legend range len str list plot grid add_subplot tight_layout title savefig figure legend range len str list xlabel grid add_subplot tight_layout ylabel bar title savefig figure legend scatter ylim xlim range len str list plot xlabel grid add_subplot tight_layout ylabel bar title savefig figure legend scatter ylim xlim range len std print reshape min mean abs max str draw_scatter his_figures mean_squared_error inverse_xy_transform draw_line score print KerasRegressor EarlyStopping to_csv get_data append ModelCheckpoint DataFrame predict fit draw_error_line draw_error_bar draw_scatter str his_figures print get_config EarlyStopping tolist stat_metrics to_csv get_data shape save_weights load_weights LSTM2 DataFrame predict fit DATA_PATH | # Meter Detection Detecting malfunctional smart meters based on electricity usage and targeting them for replacement can save significant resources. For this purpose, we developed a novel deep-learning method for malfunctional smart meter detection based on long short-term memory (LSTM) and a modified convolutional neural network (CNN). Our method uses LSTM to predict the reading of a master meter based on data collected from submeters. If the predicted value is significantly different from master meter reading data over a period of time, the diagnosis part will be activated, classifying every submeter to identify the malfunctional submeter based on CNN. We propose a time series-recurrence plot (TS-RP) CNN, by combining the sequential raw data of electricity and its recurrence plots in the phase space as dual input branches of CNN. **For more details, please refer to the [paper](http://arxiv.org/abs/1907.11377).** If you are using our work in your research, please cite us as ``` @ARTICLE{2019arXiv190711377L, author = {{Liu}, Ming and {Liu}, Dongpeng and {Sun}, Guangyu and {Zhao}, Yi and {Wang}, Duolin and {Liu}, Fangxing and {Fang}, Xiang and {He}, Qing and {Xu}, Dong}, title = "{Detection of Malfunctioning Smart Electricity Meter}", | 2,994 |
minostauros/READ | ['person re identification', 'video based person re identification'] | ['READ: Reciprocal Attention Discriminator for Image-to-Video Re-Identification'] | model/NonLocalBlock.py utils/ckpt_utils.py model/MemoryNetwork.py dataloader/DukeVideoReIDLoader.py model/ResNetNonLocal.py utils/misc_utils.py utils/metric_utils.py utils/time_utils.py dataset/mars/mars_extract_json.py model/Encoder.py model/FullyConnected.py utils/iter_utils.py utils/args.py run/read.py dataset/mars/mars_jpgs_to_h5.py play.py dataloader/MARSLoader.py play_main DukeVideoReID DukeVideoReIDLoader MARS MARSLoader extract_length main get_args get_args get_names asMinutes timeSince process_data main tracklets_to_h5 Encoder FullyConnected MemoryEncoder MemoryNetwork NonLocalBlock CNN Stripe_NonLocalBlock ResNet_Video_nonlocal_stripe_hr Resnet50_NL ResNet ResNet_Video_nonlocal ResNet_Video_nonlocal_hr Bottleneck Resnet50_s1 ResNet_Video_nonlocal_stripe weights_init_classifier NonLocalBlock weights_init_kaiming main build_models main_test run get_args setCheckpointFileDict loadCheckpoints testIters _is_integral_float auprc_average_precision_score type_of_target check_array stable_cumsum _parse_version precision_recall_curve array_equal _assert_all_finite assert_all_finite is_multilabel check_consistent_length _average_binary_score auc column_or_1d _ensure_sparse_format roc_curve _num_samples _binary_clf_curve cleanList correctK timeSince asDays main get_args print DukeVideoReID format DataLoader print DataLoader format MARS extract_length join h5_dir format get_args int format File close len parse_args add_argument ArgumentParser top_dir squeeze get_names process_data output_dir append listdir tracklets_to_h5 makedirs int list tuple tolist set append range len floor time join time format str read special_dtype print File fromstring close output_dir create_dataset timeSince enumerate create_group open affine bias kaiming_normal_ weight __name__ constant_ bias normal_ weight __name__ constant_ setCheckpointFileDict main_test device build_models checkpoint_files MemoryNetwork DataParallel MemoryEncoder is_available to testIters auprc_average_precision_score zero_grad correctK view from_numpy device_count masked_select append to expand_dims cat format astype eval stack softmax item enumerate uint8 print repeat cpu train numpy array len str sorted format join replace print split find vars pop load update format print load_state_dict keys state_dict batch_size print parameters ceil loadCheckpoints len append int split slice _binary_clf_curve searchsorted column_or_1d assert_all_finite unique check_consistent_length cumsum sum shape warn _assert_all_finite all isinstance column_or_1d lexsort trapz any memmap type check_consistent_length diff binary_metric type_of_target multiply reshape check_array ravel repeat zeros sum range check_consistent_length issparse atleast_2d _shape_repr isinstance float64 astype _ensure_sparse_format warn shape _num_samples getattr _assert_all_finite array __name__ data dtype format asformat astype copy warn _assert_all_finite warn shape _binary_clf_curve repeat nan asarray is_multilabel issparse asarray hasattr tocsr isinstance unique unique asarray hasattr asanyarray topk size eq expand_as gather int | # READ: Reciprocal Attention Discriminator for Image-to-Video Re-Identification #### [Minho Shim](https://research.minhoshim.com), [Hsuan-I Ho](https://azuxmioy.github.io), Jinhyung Kim, and Dongyoon Wee This repository contains demo software for the READ (ECCV 2020). [[Paper](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590324.pdf)] [[Supp](https://drive.google.com/file/d/1S8u7qzzZz6STP0U6Rx5qLvcJ5pwUwswP/view?usp=sharing)] ### Environment - CUDA 9.0 - Python 3.5.2 - PyTorch 0.4.1 - OpenCV 3.4.7 - H5py | 2,995 |
mir-group/flare | ['active learning'] | ['Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene'] | tests/test_kernel.py tests/test_grid_kernel.py flare/parameters.py tests/test_gp_from_aimd.py flare/gp_from_aimd.py tests/fake_gp.py tests/test_parse_otf.py tests/mgp_test.py tests/test_flare_io.py tests/test_predict.py tests/test_parameters.py flare/mgp/map2b.py flare/struc.py flare/kernels/kernels.py flare/kernels/__init__.py flare/kernels/mc_mb_sepcut.py flare/utils/parameter_helper.py flare/kernels/sc.py flare/kernels/three_body_mc_simple.py flare/dft_interface/__init__.py flare/utils/md_helper.py tests/test_otf.py setup.py flare/utils/env_getarray.py tests/test_gp_algebra.py tests/test_OTF_vasp.py flare/ase/dft.py flare/dft_interface/qe_util.py tests/test_env.py flare/kernels/coordination_mc_simple.py flare/ase/calculator.py docs/source/conf.py tests/test_cp2k_util.py flare/mgp/cubic_splines_numba.py flare/mgp/__init__.py flare/ase/npt.py flare/ase/otf.py flare/utils/element_coder.py flare/env.py flare/dft_interface/vasp_util.py tests/test_mc_sephyps.py flare/otf.py flare/otf_parser.py tests/test_qe_util.py tests/test_stress_kernels.py tests/test_rbcm.py flare/gp.py tests/test_vasp_util.py flare/utils/__init__.py flare/kernels/utils.py flare/kernels/cutoffs.py flare/kernels/two_body_mc_simple.py tests/test_files/dummy_vasp.py flare/dft_interface/cp2k_util.py flare/md.py tests/test_str_to_kernel.py flare/mgp/mapxb.py flare/output.py flare/learning_protocol.py flare/mgp/map3b.py tests/test_mgp.py flare/rbcm.py tests/test_struc.py flare/kernels/mc_sephyps.py flare/predict.py flare/utils/learner.py flare/mgp/splines_methods.py tests/test_util.py flare/lammps/lammps_calculator.py tests/test_ase_otf.py flare/kernels/mc_simple.py tests/test_gp.py flare/ase/nosehoover.py flare/mgp/mgp.py flare/mgp/grid_kernels.py flare/utils/flare_io.py flare/kernels/mc_3b_sepcut.py flare/ase/atoms.py flare/gp_algebra.py AtomicEnvironment GaussianProcess get_neg_like_grad get_force_energy_block_pack force_force_vector efs_force_vector_unit get_Ky_mat partition_matrix partition_vector energy_force_vector get_force_block_pack en_kern_vec get_distance_mat_pack update_force_block get_ky_mat_update queue_wrapper get_force_block get_kernel_vector get_ky_and_hyp_pack energy_force_vector_unit force_energy_vector force_force_vector_unit partition_matrix_custom obtain_noise_len get_force_energy_block get_energy_block kernel_distance_mat get_neg_like efs_kern_vec partition_update partition_force_energy_block get_energy_block_pack efs_energy_vector update_force_energy_block update_energy_block get_like_from_mats parallel_matrix_construction get_like_grad_from_mats energy_energy_vector force_energy_vector_unit multiple_array_construction efs_energy_vector_unit efs_force_vector energy_energy_vector_unit get_ky_and_hyp parallel_vector_construction parse_frame_block TrajectoryTrainer parse_trajectory_trainer_output structures_from_gpfa_output TrainFromAIMD LearningProtocol parse_trajectory_trainer_output update_positions calculate_temperature OTF parse_header_information extract_gp_info extract_global_info get_thermostat OtfAnalysis strip_and_split parse_snapshot split_blocks parse_frame_line append_atom_lists get_header_item add_file Output set_logger add_stream Parameters predict_on_atom_efs predict_on_atom_mgp predict_on_atom predict_on_structure_mgp predict_on_atom_en_std write_efs_to_structure predict_on_structure_par predict_on_atom_en predict_on_structure predict_on_structure_par_en predict_on_structure_efs predict_on_structure_en predict_on_structure_efs_par plot_mat rbcm_get_neg_like RobustBayesianCommitteeMachine rbcm_get_neg_like_grad sort_matrix StructureSource ForceSource Trajectory Structure get_unique_species FLARE_Atoms FLARE_Calculator get_rebuild_from_err parse_dft_input run_dft_par NoseHoover NPT_mod ASE_OTF parse_dft_forces edit_dft_input_positions parse_dft_input run_dft_en_par run_dft_par parse_dft_forces_and_energy dft_input_to_structure run_dft_en_npool parse_dft_forces parse_dft_input edit_dft_input_positions run_dft_en_par run_dft_par parse_dft_forces_and_energy dft_input_to_structure parse_dft_forces parse_dft_input edit_dft_input_positions run_dft_par md_trajectory_from_vasprun run_dft parse_dft_forces_and_energy check_vasprun dft_input_to_structure ManyBodyKernel hard_cutoff quadratic_cutoff_bound quadratic_cutoff cubic_cutoff cosine_cutoff three_body_grad_helper_1 k_sq_exp_dev k_sq_exp_double_dev three_body_grad_perm coordination_number three_body_ss_perm q_value_mc force_helper grad_constants three_body_sf_perm three_body_helper_2 three_body_fe_perm three_body_ss_2 mb_grad_helper_ls three_body_en_helper q_value three_body_ee_perm three_body_sf_1 force_energy_helper three_body_helper_1 three_body_grad_helper_2 grad_helper three_body_sf_2 mb_grad_helper_ls_ three_body_se_perm three_body_ss_1 three_body_ff_perm three_body_se_helper three_body_mc_sepcut_jit three_body_mc_en_sepcut_jit three_body_mc_grad_sepcut_jit three_body_mc_force_en_sepcut_jit many_body_mc_sepcut_jit many_body_mc_grad_sepcut_jit many_body_mc_force_en_sepcut_jit many_body_mc_en_sepcut_jit three_body_mc_en_jit two_body_mc_force_en_jit two_body_mc_grad_jit two_body_mc_jit many_body_mc_grad three_body_mc_jit two_plus_three_mc_en two_body_mc_grad many_body_mc_en three_body_mc_grad_jit two_three_many_body_mc two_body_mc two_plus_three_body_mc two_body_mc_en two_three_many_mc_force_en two_plus_three_mc_force_en three_body_mc_en three_body_mc_force_en three_body_mc_force_en_jit two_body_mc_en_jit two_three_many_mc_en three_body_mc_grad many_body_mc_force_en two_plus_three_body_mc_grad two_body_mc_force_en many_body_mc two_three_many_body_mc_grad three_body_mc two_plus_three_efs_force two_plus_three_efs_energy two_body_efs_energy three_body_efs_energy three_body_mc_en_jit two_body_mc_force_en_jit two_body_mc_stress_en_jit two_body_mc_grad_jit two_body_mc_stress_stress_jit two_body_mc_jit many_body_mc_grad three_body_mc_jit two_plus_three_mc_en two_plus_three_efs_self two_body_ss many_body_mc_grad_jit two_plus_three_ss two_plus_three_plus_many_body_mc three_body_se two_body_sf_jit two_body_mc_grad two_plus_three_plus_many_body_mc_force_en many_body_mc_en two_plus_three_plus_many_body_mc_en three_body_mc_grad_jit two_body_sf two_body_se_jit three_body_ss_jit two_plus_many_body_mc two_plus_many_body_mc_en two_plus_many_body_mc_force_en three_body_sf two_body_efs_self two_body_mc two_plus_three_body_mc two_body_mc_en two_plus_three_mc_force_en three_body_mc_en two_plus_three_plus_many_body_mc_grad three_body_efs_self three_body_mc_force_en three_body_mc_force_en_jit two_body_mc_en_jit many_body_mc_force_en_jit three_body_efs_force two_plus_three_sf three_body_sf_jit many_body_mc_en_jit two_body_efs_force three_body_mc_grad many_body_mc_force_en two_body_mc_stress_force_jit two_plus_three_body_mc_grad two_plus_three_se two_body_mc_force_en many_body_mc many_body_mc_jit three_body_ss two_body_ss_jit two_plus_many_body_mc_grad two_body_se three_body_se_jit three_body_mc many_body_jit two_plus_three_plus_many_body many_body_force_en_jit three_body_jit three_body three_body_en_jit two_plus_many_body_grad three_body_grad two_plus_three_plus_many_body_force_en many_body_grad many_body_grad_jit two_body_jit two_plus_many_body two_body_en two_plus_three_body two_body_force_en three_body_grad_jit triplet_kernel many_body_en many_body_force_en two_plus_three_plus_many_body_grad three_body_en two_plus_many_body_force_en triplet_force_en_kernel three_body_force_en two_plus_three_en two_body_grad many_body two_plus_many_body_en three_body_force_en_jit triplet_kernel_grad str_to_kernel many_body_en_jit two_body_grad_jit two_body_force_en_jit two_plus_three_force_en two_plus_three_body_grad two_body two_body_en_jit two_plus_three_plus_many_body_en efs_self energy_energy stress_stress efs_energy force_force stress_force stress_energy force_force_gradient efs_force ThreeBodyKernel force_energy efs_self energy_energy stress_stress efs_energy TwoBodyKernel force_force stress_energy stress_force force_force_gradient efs_force force_energy str_to_kernel_set kernel_str_to_array from_mask_to_args from_grad_to_mask lammps_dat_charged write_text run_lammps lammps_pos_text lammps_pos_text_charged lammps_parser generic_lammps_input ewald_input lammps_cell_text lammps_dat filter_data filter_coeffs_3d filter_coeffs vec_eval_cubic_spline_2 vec_eval_cubic_splines_G_1 vec_eval_cubic_spline_3 vec_eval_cubic_splines_G_3 solve_deriv_interp_1d filter_coeffs_1d vec_eval_cubic_spline_1 filter_coeffs_2d find_coefs_1d self_kernel en_en grid_kernel en_force grid_kernel_env get_bonds_for_kern get_bonds_for_kern_jit get_permutations bonds_cutoff get_hyps_for_kern get_bonds SingleMap2body Map2body get_bonds_for_kern Map3body get_permutations bonds_cutoff get_hyps_for_kern get_triplets get_triplets_for_kern_jit SingleMap3body MapXbody SingleMapXbody get_kernel_term MappedGaussianProcess PCASplines vec_eval_cubic_splines_G vec_eval_cubic_spline CubicSpline element_to_Z Z_to_element inject_user_definition NumpyEncoder q2_grads_mc get_m2_body_arrays q3_grads_mc get_m3_body_arrays q3_value_mc get_2_body_arrays q3_neigh_grads_mc get_3_body_arrays md_trajectory_to_file md_trajectory_from_file is_force_in_bound_per_species subset_of_frame_by_element is_std_in_bound evaluate_training_atoms get_max_cutoff is_std_in_bound_per_species get_supercell_positions multicomponent_velocities supercell_custom get_random_velocities ParameterHelper get_gp generate_hm generate_envs generate_mb_envs generate_mb_envs_pos get_random_structure generate_mb_twin_envs get_tstp another_env get_params get_triplet_env predict_atom_diag_var_2b predict_struc_diag_var predict_atom_diag_var_3b get_grid_env compare_triplet clean predict_atom_diag_var test_otf_parser super_cell md_params qe_calc flare_calc test_load_checkpoint test_otf_md read_qe_results test_cell_parsing test_input_to_structure cleanup test_species_parsing test_cp2k_input_edit test_cp2k_calling test_2bspecies_count test_env_methods test_backwards_compatibility generate_mask structure test_auto_sweep test_read_write_trajectory TestIO test_training_statistics two_plus_three_gp TestAlgebra TestTraining TestConstraint params validation_env TestHelper test_remove_force_data all_gps TestInitialization dumpcompare TestDataUpdating test_efs_kern_vec test_ky_and_hyp get_random_training_set test_kernel_vector test_ky_mat params test_en_kern_vec ky_mat_ref test_ky_mat_update test_active_learning_simple_run test_seed_and_run test_mgp_gpfa test_load_one_frame_and_run fake_gp test_load_trained_gp_and_run test_pred_on_elements test_passive_learning test_instantiation_of_trajectory_trainer test_parse_gpfa_output methanol_gp get_grid_env parameter test_start get_reference test_force_en test_force test_hyps_grad generate_hm test_force_en_multi_vs_simple test_check_sig_scale test_hyps_grad generate_same_hm test_force_en test_force_bound_cutoff_compare generate_diff_hm test_force test_constraint all_gp test_build_map test_lmp_predict test_write_model test_cubic_spline test_predict test_load_model test_init all_lmp all_mgp get_gp cleanup test_otf_parser_from_checkpt test_otf_parser test_otf test_otf_par test_load_checkpoint test_otf_h2 test_initialization test_generate_by_line test_from_dict test_constraints1 test_initialization_allsep test_opt test_initialization5 test_constraints2 test_generate_by_list2 test_initialization3 test_check_one_conflict test_generate_by_list test_generate_by_list_error test_initialization2 test_generate_by_line2 test_initialization4 test_parse_header test_otf2xyz test_output_md_structures test_replicate_gp test_md_parser test_gp_parser fake_predict two_plus_three_gp test_predict_on_structure_en test_predict_on_atoms test_predict_efs fake_predict_local_energy test_predict_on_structure_par test_espresso_input_edit test_cell_parsing test_input_to_structure test_espresso_calling test_species_parsing cleanup_espresso_run test_expert_growth_and_training test_basic test_prediction test_convenience_methods test_to_from_gp test_io test_stress_stress force_envs test_stress_energy test_efs strucs test_force_energy test_force_force test_stress_force test_efs_self struc_envs test_force_grad stress_envs test_is_valid test_indices_of_specie test_rep_methods test_struc_from_ase test_file_load test_wrapped_coordinates test_raw_to_relative test_to_pmg_structure test_prev_positions_arg test_from_pmg_structure test_to_from_methods test_struc_to_ase test_to_xyz varied_test_struc test_random_structure_setup test_stk test_force_in_bound_per_species test_std_in_bound_per_species test_element_to_Z test_subset_of_frame_by_element test_Z_to_element test_elt_warning test_md_trajectory test_run_dft_par test_input_to_structure test_structure_parsing cleanup_vasp_run test_vasp_calling_fail test_vasp_calling test_check_vasprun test_vasp_input_edit put ceil int min sqrt ceil int min sqrt ceil int min ceil int min int min sqrt ceil range get len get Process T join start Queue append zeros range get Process join start Queue append zeros range get Process join start Queue append zeros range len kernel zeros from_mask_to_args range parallel_matrix_construction get_distance_mat_pack partition_matrix cpu_count len kernel zeros from_mask_to_args range zeros from_mask_to_args range zeros from_mask_to_args range parallel_matrix_construction get_force_block_pack obtain_noise_len partition_matrix cpu_count len parallel_matrix_construction partition_matrix cpu_count get_energy_block_pack len parallel_matrix_construction get_force_energy_block_pack cpu_count partition_force_energy_block len get_force_energy_block get_energy_block get_force_block transpose zeros len parallel_matrix_construction get_force_block_pack obtain_noise_len transpose cpu_count partition_update zeros len parallel_matrix_construction transpose cpu_count partition_update get_energy_block_pack zeros len partition_matrix_custom parallel_matrix_construction get_force_energy_block_pack cpu_count zeros len update_force_block transpose zeros update_force_energy_block update_energy_block len zeros from_mask_to_args range kernel zeros from_mask_to_args range zeros from_mask_to_args range kernel zeros from_mask_to_args range zeros efs_force_kernel from_mask_to_args range efs_energy_kernel zeros from_mask_to_args range partition_vector cpu_count parallel_vector_construction len partition_vector cpu_count parallel_vector_construction len partition_vector cpu_count parallel_vector_construction len partition_vector cpu_count parallel_vector_construction len partition_vector cpu_count multiple_array_construction len partition_vector cpu_count multiple_array_construction len zeros force_force_vector force_energy_vector len zeros energy_force_vector energy_energy_vector len zeros efs_force_vector efs_energy_vector len obtain_noise_len from_grad_to_mask zeros kernel_grad range from_mask_to_args get Process obtain_noise_len T join get_ky_and_hyp_pack partition_matrix cpu_count start Queue eye append zeros range len concatenate matmul pi diagonal sum log time getLogger get_force_block debug inv matmul cholesky get_like_from_mats time getLogger debug get_ky_and_hyp info get_like_grad_from_mats reshape inv matmul pi range trace cholesky diagonal zeros sum log strip loads split append float array range len get int list strip set loads nan append parse_frame_block union range values len append Structure float split positions prev_positions copy zeros enumerate len range positions prev_positions int replace strip set upper lower eval append float array enumerate get_header_item split value_type lower strip parse_snapshot append append zeros parse_frame_line enumerate split str array split replace get_thermostat split append enumerate append float lower int startswith append float array enumerate split isinstance handlers addHandler StreamHandler DEBUG setLevel isinstance handlers addHandler upper movefile getattr isfile DEBUG setLevel FileHandler getLogger upper add_file add_stream getattr setLevel list set_logger keys values cutoffs predict_force_xyz sqrt AtomicEnvironment abs cutoffs predict_local_energy predict_force_xyz sqrt AtomicEnvironment abs cutoffs sqrt predict_local_energy_and_var AtomicEnvironment abs cutoffs AtomicEnvironment cutoffs nat predict_force_xyz sqrt AtomicEnvironment fill zeros abs range get join nat apply_async close fill zeros Pool range append nat cutoffs write_efs_to_structure predict_efs sqrt AtomicEnvironment zeros abs range get join nat write_efs_to_structure apply_async close sqrt append zeros abs Pool range det local_energies cell sqrt array sum partial_stress_stds nat cutoffs predict_local_energy predict_force_xyz sqrt AtomicEnvironment fill zeros abs range get join nat apply_async close predict_on_structure_en fill zeros Pool range append cutoffs nat absolute sqrt AtomicEnvironment zeros predict predict_on_atom_mgp nat fill write_to_output zeros range get_neg_like_grad time get_hyps getLogger info range enumerate time get_hyps getLogger get_neg_like info range enumerate range zeros_like close tight_layout imshow title savefig figure append array index len append index warn enumerate str get_masses positions copy range get_cell array get_chemical_symbols len deepcopy set_calculator get_potential_energy get_forces get_stress edit_dft_input_positions remove call edit_dft_input_positions parse_dft_forces_and_energy remove int list map keys split append enumerate get_unique_species parse_dft_input int nat join append range enumerate array nan parse_dft_forces_and_energy call edit_dft_input_positions format parse_dft_forces_and_energy join strip fromstring set float str array nan cell species_labels from_pmg_structure structure chdir getcwd edit_dft_input_positions call parse_func split get Poscar to_pmg_structure copyfile isfile write_file check_vasprun check_vasprun get check_vasprun append array from_pmg_structure cos sin exp exp exp force_helper force_helper exp exp exp exp grad_helper grad_helper exp three_body_grad_helper_2 three_body_grad_helper_1 exp exp cutoff_func q_func range q_func len exp mb_grad_helper_ls_ remove range cutoff_func grad_constants remove three_body_grad_helper_2 hstack three_body_grad_helper_1 cutoff_func zeros range remove range cutoff_func remove range cutoff_func list set k_sq_exp_double_dev intersection array range list set k_sq_exp_double_dev mb_grad_helper_ls_ intersection zeros array range list k_sq_exp_dev set intersection array range set list array intersection triplet_counts q_neigh_grads cross_bond_inds etypes_mb cross_bond_dists mbmcj etypes q_neigh_array unique_species two_body_mc_jit bond_array_3 q_array tbmcj bond_array_2 ctype triplet_counts q_neigh_grads cross_bond_inds etypes_mb cross_bond_dists mbmcj etypes q_neigh_array unique_species two_body_mc_grad_jit bond_array_3 q_array tbmcj bond_array_2 ctype triplet_counts q_neigh_grads cross_bond_inds etypes_mb cross_bond_dists mbmcj two_body_mc_force_en_jit etypes q_neigh_array unique_species bond_array_3 q_array tbmcj bond_array_2 ctype triplet_counts cross_bond_inds cross_bond_dists mbmcj etypes unique_species two_body_mc_en_jit bond_array_3 q_array tbmcj bond_array_2 ctype triplet_counts cross_bond_inds cross_bond_dists etypes two_body_mc_jit bond_array_3 tbmcj bond_array_2 ctype triplet_counts cross_bond_inds cross_bond_dists hstack etypes two_body_mc_grad_jit bond_array_3 tbmcj bond_array_2 ctype triplet_counts cross_bond_inds cross_bond_dists two_body_mc_force_en_jit etypes bond_array_3 tbmcj bond_array_2 ctype triplet_counts cross_bond_inds cross_bond_dists etypes two_body_mc_en_jit bond_array_3 tbmcj bond_array_2 ctype remove range cutoff_func grad_constants remove three_body_grad_helper_2 three_body_grad_helper_1 cutoff_func zeros range remove range cutoff_func remove range cutoff_func range cutoff_func grad_helper hstack cutoff_func zeros range range cutoff_func range cutoff_func three_body_mc_jit three_body_mc_grad_jit three_body_mc_force_en_jit three_body_mc_en_jit triplet_counts cross_bond_inds cross_bond_dists etypes two_body_se_jit bond_array_3 three_body_se_jit bond_array_2 ctype triplet_counts two_body_sf_jit cross_bond_inds cross_bond_dists etypes bond_array_3 three_body_sf_jit bond_array_2 ctype triplet_counts cross_bond_inds cross_bond_dists etypes two_body_ss_jit bond_array_3 three_body_ss_jit bond_array_2 ctype triplet_counts cross_bond_inds cross_bond_dists etypes efs_energy bond_array_3 bond_array_2 ctype triplet_counts cross_bond_inds cross_bond_dists etypes bond_array_3 efs_force bond_array_2 ctype triplet_counts efs_self cross_bond_inds cross_bond_dists etypes bond_array_3 bond_array_2 ctype q_neigh_grads etypes_mb etypes q_neigh_array many_body_mc_jit unique_species two_body_mc_jit q_array bond_array_2 ctype q_neigh_grads etypes_mb etypes ctype q_neigh_array two_body_mc_grad_jit unique_species q_array bond_array_2 many_body_mc_grad_jit q_neigh_grads etypes_mb two_body_mc_force_en_jit etypes q_neigh_array unique_species many_body_mc_force_en_jit q_array bond_array_2 ctype many_body_mc_en_jit etypes unique_species two_body_mc_en_jit q_array bond_array_2 ctype triplet_counts q_neigh_grads cross_bond_inds etypes_mb cross_bond_dists etypes q_neigh_array many_body_mc_jit unique_species two_body_mc_jit bond_array_3 three_body_mc_jit q_array bond_array_2 ctype triplet_counts q_neigh_grads cross_bond_inds etypes_mb cross_bond_dists etypes q_neigh_array unique_species two_body_mc_grad_jit three_body_mc_grad_jit many_body_mc_grad_jit bond_array_3 q_array bond_array_2 ctype triplet_counts q_neigh_grads cross_bond_inds etypes_mb cross_bond_dists two_body_mc_force_en_jit etypes q_neigh_array unique_species three_body_mc_force_en_jit many_body_mc_force_en_jit bond_array_3 q_array bond_array_2 ctype triplet_counts many_body_mc_en_jit cross_bond_inds cross_bond_dists three_body_mc_en_jit etypes unique_species two_body_mc_en_jit bond_array_3 q_array bond_array_2 ctype zeros range cutoff_func zeros range cutoff_func zeros range cutoff_func force_energy_helper range cutoff_func range cutoff_func force_helper range cutoff_func force_helper zeros force_energy_helper range cutoff_func zeros range cutoff_func force_helper zeros range cutoff_func force_helper list set k_sq_exp_double_dev intersection array range list set k_sq_exp_double_dev mb_grad_helper_ls_ intersection array range list k_sq_exp_dev set intersection array range set list array intersection triplet_counts cross_bond_inds cross_bond_dists three_body_jit bond_array_3 bond_array_2 two_body_jit triplet_counts cross_bond_inds cross_bond_dists three_body_grad_jit bond_array_3 two_body_grad_jit bond_array_2 triplet_counts cross_bond_inds cross_bond_dists three_body_force_en_jit bond_array_3 two_body_force_en_jit bond_array_2 triplet_counts cross_bond_inds cross_bond_dists bond_array_3 three_body_en_jit two_body_en_jit bond_array_2 q_neigh_grads many_body_jit q_neigh_array q_array bond_array_2 two_body_jit q_neigh_grads q_neigh_array two_body_grad_jit q_array many_body_grad_jit bond_array_2 q_neigh_grads many_body_force_en_jit q_neigh_array two_body_force_en_jit q_array bond_array_2 triplet_counts cross_bond_inds cross_bond_dists many_body_en_jit bond_array_3 q_array three_body_en_jit two_body_en_jit bond_array_2 triplet_counts q_neigh_grads many_body_jit cross_bond_inds cross_bond_dists three_body_jit q_neigh_array bond_array_3 q_array bond_array_2 two_body_jit triplet_counts q_neigh_grads cross_bond_inds cross_bond_dists three_body_grad_jit q_neigh_array bond_array_3 two_body_grad_jit q_array many_body_grad_jit bond_array_2 triplet_counts q_neigh_grads many_body_force_en_jit cross_bond_inds cross_bond_dists q_neigh_array three_body_force_en_jit bond_array_3 two_body_force_en_jit q_array bond_array_2 triplet_counts cross_bond_inds cross_bond_dists many_body_en_jit bond_array_3 q_array three_body_en_jit two_body_en_jit bond_array_2 array bond_array_2 two_body_grad_jit triplet_counts cross_bond_inds cross_bond_dists three_body_grad_jit bond_array_3 array q_neigh_grads q_neigh_array q_array array many_body_grad_jit range cutoff_func grad_helper grad_constants range cutoff_func range cutoff_func range cutoff_func range cutoff_func triplet_kernel_grad grad_constants range cutoff_func range cutoff_func exp range cutoff_func k_sq_exp_double_dev sum range k_sq_exp_double_dev sum range sum range k_sq_exp_dev exp sum three_body_helper_1 three_body_helper_2 three_body_grad_helper_2 three_body_grad_helper_1 three_body_en_helper range cutoff_func zeros range cutoff_func zeros range cutoff_func zeros range cutoff_func zeros range cutoff_func zeros range cutoff_func grad_constants three_body_grad_perm cutoff_func zeros range zeros range cutoff_func zeros range cutoff_func zeros range cutoff_func force_energy_helper force_helper force_helper grad_helper force_energy_helper force_helper force_helper _str_to_kernel isinstance get zeros ones get_component_hyps zeros enumerate len print startswith append float enumerate split zip zip positions zip enumerate positions zip enumerate int min floor max range int min floor max range int min floor max range int min floor max range int min floor expand_dims max range range zeros solve_deriv_interp_1d range zeros find_coefs_1d zeros find_coefs_1d range zeros find_coefs_1d range reshape array exp get_bonds_func bonds_cutoff_func kern_func meshgrid array range append T sum shape T range zeros exp sum take get_permutations cutoff_func from_mask_to_args append range index len append range arange zeros expand_dims range index sqrt append sum array range append remove get_permutations hstack index take vstack append zeros array range len get_component_mask str_to_kernel_set vec_eval_cubic_spline_2 vec_eval_cubic_spline_3 vec_eval_cubic_spline_4 vec_eval_cubic_spline_1 empty array vec_eval_cubic_splines_G_4 vec_eval_cubic_splines_G_1 vec_eval_cubic_splines_G_2 vec_eval_cubic_splines_G_3 empty array isinstance warn isnumeric int isinstance argsort sqrt zeros range len zeros max range sqrt q2_grads_mc list set get_2_body_arrays coordination_number zeros range array q_value_mc len zeros range len list q3_grads_mc q3_value_mc get_2_body_arrays set q3_neigh_grads_mc get_3_body_arrays zeros array range len zeros range len zeros range q_func len zeros range q_func len list stds len argsort append zeros abs max enumerate min argsort isnan append abs flip len argsort append abs flip amax len update items sort set choice species_labels range len dot zeros norm sqrt get items list is_force_in_bound_per_species min union species_labels max is_std_in_bound_per_species normal sqrt sum normal sqrt zeros array enumerate len append range append range list keys values seed random Structure set_parameters set_constraints ParameterHelper as_dict define_group generate_hm print update_db GaussianProcess get_random_structure check_L_alpha diag get_random_structure AtomicEnvironment eye ones hstack shuffle shape array generate_mb_envs_pos array deepcopy range len deepcopy list vstack AtomicEnvironment eye Structure max values deepcopy vstack AtomicEnvironment Structure eye remove isdir search rmtree rmdir listdir get_triplet_env T ones_like find_map_index zeros_like print get_grid_env get_arrays predict_local_energy_and_var append sum array range predict enumerate print array etypes ctype list cutoffs isinstance AtomicEnvironment eye Structure max values cutoffs nat AtomicEnvironment zeros range predict_atom_diag_var cutoffs print predict copy sqrt predict_local_energy_and_var AtomicEnvironment eye Structure zeros array range bond_array_2 ctype triplet_counts cutoffs cross_bond_inds print cross_bond_dists etypes sqrt predict_local_energy_and_var bond_array_3 eye Structure AtomicEnvironment zeros array range predict ctype print decode read results calc call sleep label split print remove update glob positions crystal FixAtoms range set_constraint print FLARE_Calculator GaussianProcess ParameterHelper as_dict MappedGaussianProcess LennardJones sigma seed ASE_OTF remove listdir glob OtfAnalysis MaxwellBoltzmannDistribution rmtree kB Stationary ZeroRotation run from_checkpoint number_of_steps run remove print glob OtfAnalysis make_gp remove parse_dft_input enumerate parse_dft_input get nat cleanup parse_dft_forces parse_dft_input run_dft_par copyfile Structure range remove cleanup edit_dft_input_positions parse_dft_input Structure get_unique_species Structure array eye print generate_mask AtomicEnvironment __dict__ deepcopy from_dict remove from_file random as_str generate_mask AtomicEnvironment as_dict deepcopy from_dict delattr generate_mask AtomicEnvironment as_dict arange ones zeros array len sweep_val arange etypes AtomicEnvironment Structure array compute_env len system md_trajectory_from_file zip md_trajectory_from_vasprun md_trajectory_to_file generate_hm update_db GaussianProcess get_random_structure eye ones update_db GaussianProcess get_random_structure eye array seed get_tstp sorted ndarray isinstance zip keys training_statistics update_db GaussianProcess get_random_structure eye update_db GaussianProcess get_random_structure predict_on_structure eye range remove_force_data len get_random_training_set seed set_constraints hstack ParameterHelper uniform AtomicEnvironment eye as_dict Structure randint array range append len print str_to_kernel_set time get_Ky_mat print str_to_kernel_set time get_Ky_mat str_to_kernel_set seed str_to_kernel_set get_kernel_vector get_tstp len seed str_to_kernel_set en_kern_vec get_tstp len seed str_to_kernel_set efs_kern_vec get_tstp len str_to_kernel_set copy func range get_like_grad_from_mats len from_dict set_L_alpha GaussianProcess add_one_env TrajectoryTrainer glob remove TrajectoryTrainer run remove glob GaussianProcess TrajectoryTrainer run remove glob GaussianProcess TrajectoryTrainer run deepcopy remove glob len GaussianProcess TrajectoryTrainer range run seed get_gp build_map remove glob get_random_structure TrajectoryTrainer set_L_alpha eye MappedGaussianProcess array run join parse_trajectory_trainer_output structures_from_gpfa_output zip deepcopy join from_file GaussianProcess set run_passive_learning TrajectoryTrainer training_statistics join remove glob from_file random GaussianProcess run_passive_learning TrajectoryTrainer run_active_learning len get_bonds_for_kern list ones_like permutations print bonds_cutoff get_hyps_for_kern etypes get_reference hstack kernel get_grid_env set zeros combinations_with_replacement ctype seed generate_mb_envs generate_same_hm eye generate_diff_hm array str_to_kernel_set force_en_kernel zeros from_mask_to_args range seed str_to_kernel_set generate_hm force_en_kernel ones generate_mb_envs print en3_kernel en2_kernel eye range __name__ len seed str_to_kernel_set generate_hm ones generate_mb_envs print en3_kernel kernel en2_kernel eye range len seed str_to_kernel_set generate_hm ones print copy kernel eye randint kernel_grad range len str_to_kernel_set print generate_mb_envs generate_same_hm eye zeros from_mask_to_args range len str_to_kernel_set kg force_en_kernel print copy generate_mb_twin_envs kernel en_kernel eye generate_diff_hm range from_mask_to_args __name__ seed str_to_kernel_set kg force_en_kernel generate_mb_envs kernel en_kernel eye generate_diff_hm from_mask_to_args seed str_to_kernel_set force_en_kernel print generate_mb_envs en3_kernel en2_kernel eye generate_diff_hm from_mask_to_args generate_diff_hm from_mask_to_args get_component_mask generate_diff_hm from_mask_to_args get_hyps generate_mb_envs generate_diff_hm from_mask_to_args set_parameters random ParameterHelper as_dict define_group set_parameters random ParameterHelper as_dict define_group seed get_gp join LAMMPS training_statistics clean MappedGaussianProcess build_map write_model as_dict from_file remove sum deepcopy maps species_code print bounds reshape __coeffs__ mean array abs max range len cutoffs nat predict_force_xyz positions print predict_efs get_random_structure predict_local_energy_and_var AtomicEnvironment eye species_labels range predict predict_atom_diag_var seed cutoffs chdir FLARE_Calculator get_potential_energy print get_random_structure get_forces get_stress from_ase_atoms to_ase_atoms clean array diag array glob rmtree listdir get get_gp OTF isdir print getcwd skip copy rmtree run get get_gp OTF isdir print getcwd skip copy rmtree run gp_cell_list skip my_otf print skip cleanup move OtfAnalysis skip mkdir make_gp listdir OTF system GaussianProcess array run ParameterHelper check_instantiation as_dict ParameterHelper check_instantiation as_dict find_group ParameterHelper check_instantiation as_dict range ParameterHelper check_instantiation as_dict ParameterHelper check_instantiation as_dict ParameterHelper check_instantiation as_dict set_parameters set_constraints ParameterHelper as_dict define_group check_instantiation set_parameters ParameterHelper check_instantiation as_dict define_group ParameterHelper as_dict check_instantiation list_parameters list_groups ParameterHelper as_dict check_instantiation list_parameters list_groups ParameterHelper list_groups ParameterHelper check_instantiation as_dict compare_dict from_dict ParameterHelper check_instantiation as_dict ParameterHelper check_instantiation as_dict ParameterHelper check_instantiation as_dict system header OtfAnalysis system gp_position_list OtfAnalysis system OtfAnalysis system position_list force_list output_md_structures OtfAnalysis system position_list force_list predict_on_structure make_gp output_md_structures to_xyz OtfAnalysis system read flatten predict_on_structure_par nan get_random_structure predict_on_structure predict_on_structure_efs eye predict_on_structure_efs_par predict_on_atom predict_on_atom_en deepcopy uniform predict_on_structure_par_en glob rmtree remove isfile get nat parse_dft_forces parse_dft_input run_dft_par copyfile Structure cleanup_espresso_run range remove edit_dft_input_positions parse_dft_input system Structure get_unique_species RobustBayesianCommitteeMachine add_one_env force RobustBayesianCommitteeMachine train array T update_db add_one_env get_kernel_vector name force GaussianProcess RobustBayesianCommitteeMachine kernel hyps matmul ky_mat_inv energy_force_kernel alpha log predict from_gp update_db GaussianProcess range get_full_gp update_db from_file write_model RobustBayesianCommitteeMachine update_db training_statistics RobustBayesianCommitteeMachine seed rand Structure append nat range AtomicEnvironment nat positions coded_species cell copy AtomicEnvironment Structure append range nat positions coded_species cell copy AtomicEnvironment Structure append range zeros energy_energy range len zeros energy_energy range len energy_energy force_force zeros range len zeros energy_energy range len zeros energy_energy range len force_force force_force_gradient range energy_energy efs_energy force_force stress_force stress_energy efs_force force_energy efs_self print energy_energy stress_stress force_force get_random_structure seed uniform eye Structure append range raw_to_relative positions rand cell_dot_inverse Structure randint cell_transpose len raw_to_relative positions rand len cell_dot_inverse Structure cell_transpose wrapped_positions eye array Structure from_dict loads as_dict as_str str SinglePointCalculator Atoms from_ase_atoms to_ase_atoms rand Structure randn from_pmg_structure Structure to_pmg_structure to_xyz range enumerate split from_file remove get_random_structure Structure stks range zip range zip eye array is_std_in_bound_per_species get_random_structure eye array get_random_structure is_force_in_bound_per_species Structure system Vasprun species_labels enumerate structure dft_input_to_structure nat parse_dft_forces cleanup_vasp_run run_dft parse_dft_forces_and_energy range dft_input_to_structure dft_input_to_structure edit_dft_input_positions system dft_input_to_structure system run_dft_par dft_input_to_structure md_trajectory_from_vasprun | [](https://github.com/mir-group/flare/actions) [](https://pypi.org/project/mir-flare/) [](https://github.com/mir-group/flare/commits/master) [](https://codecov.io/gh/mir-group/flare) ***NOTE: This is the latest release [1.3.3](https://github.com/mir-group/flare/releases/tag/1.3.3) which includes significant changes compared to the previous version [0.2.4](https://github.com/mir-group/flare/releases/tag/0.2.4). Please check the updated tutorials and documentations from the links below.*** # FLARE: Fast Learning of Atomistic Rare Events <p align="center"> <img width="527" height="242" src="https://github.com/mir-group/flare/blob/master/docs/images/Flare_logo.png?raw=true"> </p> FLARE is an open-source Python package for creating fast and accurate interatomic potentials. ## Major Features <p align="center"> <img src="https://github.com/mir-group/flare/blob/development/docs/images/Flare_features.jpg?raw=true"> | 2,996 |
mirandrom/HipoRank | ['extractive summarization', 'unsupervised extractive summarization'] | ['Discourse-Aware Unsupervised Summarization of Long Scientific Documents', 'Discourse-Aware Unsupervised Summarization for Long Scientific Documents'] | hipo_rank/__init__.py hipo_rank/similarities/cos.py exp5_run.py hipo_rank/scorers/multiply.py exp1_run.py exp2_run.py hipo_rank/directions/order.py exp8_run.py hipo_rank/similarities/__init__.py hipo_rank/summarizers/textrank.py hipo_rank/summarizers/oracle.py hipo_rank/summarizers/default.py hipo_rank/evaluators/rouge.py hipo_rank/embedders/bert.py hipo_rank/dataset_iterators/cnn_dm.py exp3_run.py hipo_rank/directions/undirected.py hipo_rank/embedders/__init__.py exp6_run.py hipo_rank/dataset_iterators/pubmed.py exp11_run.py hipo_rank/directions/edge.py exp7_run.py hipo_rank/embedders/rand.py hipo_rank/embedders/w2v.py hipo_rank/scorers/__init__.py hipo_rank/dataset_iterators/__init__.py hipo_rank/embedders/sent_transformers.py exp10_run.py hipo_rank/directions/__init__.py exp9_run.py exp4_run.py hipo_rank/scorers/add.py hipo_rank/summarizers/lead.py SectionEmbedding SectionSimilarities SentenceEmbeddings Embeddings Document SentenceSimilarities Section Similarities CnndmDoc CnndmDataset PubmedDataset PubmedDoc EdgeBased OrderBased Undirected BertEmbedder RandEmbedder SentTransformersEmbedder W2VEmbedder evaluate_rouge AddScorer MultiplyScorer CosSimilarity DefaultSummarizer LeadSummarizer OracleSummarizer TextRankSummarizer join parent zip print Rouge155 output_to_dict enumerate convert_and_evaluate rmtree choices digits ascii_uppercase makedirs | # HipoRank Unsupervised and extractive long document summarization with **Hi**erarchichal and **Po**sitional information. Contains code for [Discourse-Aware Unsupervised Summarization of Long Scientific Documents](https://arxiv.org/abs/2005.00513) accepted at EACL 2021. # Requirements ## Libraries `pip install -r requirements.txt` ## Datasets Original: https://github.com/armancohan/long-summarization Val/Test sets used: https://zenodo.org/record/5718829 ## ROUGE `pyrouge_set_rouge_path /absolute/path/to/ROUGE-1.5.5/directory` | 2,997 |
misogil0116/recipe_generation_from_an_image_sequence.pytorch | ['visual storytelling', 'story generation'] | ['GLAC Net: GLocal Attention Cascading Networks for Multi-image Cued Story Generation'] | utils/vocabulary.py src/common.py src/models/glacnet.py src/train.py src/dataset_utils.py src/evaluate.py preprocess/build_dataset.py preprocess/convert_pickles_into_trainable_format.py build_vocabulary download_images dump_to_pickle load_and_extract_recipes remove_introduction build_dataset tokenize load_pickle convert_data_to_vectors image_pickup_an_image_randomly image2vec extract_feature_vector text2vec RecipeEvaluator RecipeGenerator calculate_mask calculate_mask_NLL_loss StoryBoardingDataset collate_fn batchfy evaluate EncoderCNN EncodeStory BatchGLACNet Vocabulary append remove_introduction join system load lower tqdm update Vocabulary print sort Counter dict len build_vocabulary join directory download_images set_trace shuffle load_and_extract_recipes dump_to_pickle dl tokenize max pad array append full enumerate len choice Compose transformer unsqueeze cnn_model open join image_pickup_an_image_randomly Sequential tqdm eval resnet34 extract_feature_vector append zeros numpy enumerate join str image2vec text2vec enumerate float size sum view batchfy model calculate_mask_NLL_loss StoryBoardingDataset eval DataLoader calculate_mask enumerate max shape zeros to full enumerate | # recipe_generation_from_an_image_sequence.pytorch This repo is implementations of neural recipe generators using PyTorch. Now we implemented the following 5 models: - Images2seq (https://www.aclweb.org/anthology/N16-1147v2.pdf) - GLAC Net (https://arxiv.org/pdf/1805.10973.pdf) - Retrieval Attention (RetAttn) (https://www.aclweb.org/anthology/W19-8650.pdf) - SSiD (https://www.aclweb.org/anthology/P19-1606.pdf) - SSiL (https://www.aclweb.org/anthology/P19-1606.pdf) **Note** We could not implement the SSiD and SSiL perfectly due to lack of details of a finite state machine (FSM). ## Requirements | 2,998 |
mit-acl/clear | ['graph clustering'] | ['CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multi-View Data Association'] | CLEAR_Python/util.py CLEAR_Python/CLEAR.py CLEAR estimate_num_obj block_svd suboptimal_assignment normalize_lap pivot_rows P2L L2P estimate_num_obj T norm block_svd suboptimal_assignment cumsum diag normalize_lap pivot_rows matmul shape P2L eye append zeros sum array range sum diag diag eye T range diag where svd T connected_components reshape shape ix_ zeros range count_nonzero block_svd reshape normalize_lap delete where L2P abs max diag diff T matmul nanargmin nan zeros abs range inf size argmin shape any zeros range | # CLEAR: Consistent Lifting, Embedding, and Alignment Rectification Algorithm The Matlab, Python, and C++ implementation of the CLEAR algorithm, as described in [[1]](https://arxiv.org/abs/1902.02256). [1] K. Fathian, K. Khosoussi, Y. Tian, P. Lusk, J.P. How, "CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multi-View Data Association", [arXiv:1902.02256](https://arxiv.org/abs/1902.02256), 2019. ## Video: A video summary of the CLEAR algorithm: [](https://youtu.be/RBxq9KYcgTY "CLEAR Algorithm for Multi-View Data Association") ## Matlab syntax: ``` [Pout, Puni, numObjEst] = CLEAR(Pin, numSmp, numAgt) ``` | 2,999 |
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