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MantasPtr/CERN-CMS-DQM-DT-visualization | ['anomaly detection'] | ['Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider'] | gui/guiUtils.py validators/positiveValidator.py export_import/importing.py machineLearning/model.py utils/asyncUtils.py export_import/csvFileReader.py errors/errors.py dataFetching/urlGenererators/urlGeneratorByList.py export_import/csvFileWriter.py logic/dataFetch.py dataFetching/authContainer.py validators/betweenValidator.py export_import/exporting.py machineLearning/test/modelTest.py validators/urlParamValidators.py machineLearning/test/transformTest.py machineLearning/transform.py utils/numpyUtils.py machineLearning/logic.py logic/dbIdentifierBuilder.py dataFetching/urlGenererators/urlGeneratorByRange.py machineLearning/saliency.py database/mongoDbFactory.py dataFetching/asyncRequestExecutor.py validators/abstractValidator.py logic/dataLoad.py flaskController.py database/dbSetup.py config/configUtils.py logic/dataEvaluation.py database/mongoController.py dataFetching/dataLoader.py validators/validation.py export_import/csvDataConverter.py net_scores_json _generate_json_lines evalRun score _make_response delete page_not_found fetch skip handle_invalid_usage new_net_scores runData scores contamination _render_generic_statistics_template reevaluate flask_pagination_url_formater record_lines default get_uncertain_matrix evalRunWithParam fetchRun net_scores _format_user_score getConfig getJsonConfig _get_collection _get_default_database_config get_db_controller _init_default_controller Mongo_4_DB_controller MongoDbFactory AsyncRequestExecutor AuthContainer _format_matrix_result _handle_dependant_params _extend_with_new_params_combinations _get_param_dicts get_url_generator _handle_static_params _handle_dependant_params _extend_with_new_params_combinations _get_param_dicts get_url_generator _handle_static_params ConfigError UrlError NotSingleResultError FetchError ValidationError from_row_to_record from_record_to_row read_file write_to_file _ensure_directory _load_from_database _write_to_file _process_data export_to_file _get_file_name _process_data import_from_file _save_to_db _read_file Pagination process _reevaluate_all reevaluate_all fetch_data_by_identifier get_not_evaluated_network_scores get_matrix_from_DB get_contamination_scores mark_as_skipped delete get_scores_data update_user_score get_network_scores get_one_record get_fetched_data buildDicts _append_model_estimation_scores _append_model_saliency append_saliency append_estimation _replace_negatives _predict_layers_badness get_network_score get_saliency_map _predict_badness GradientSaliency reload_model scaleMatrix resize_matrix_to_form resizeMatrix resizeLayer remove_negatives replace_positive_values scaleLayer process_matrix NetTest NetTest run_async_in_thread _loop_in_thread remove_negatives to_python_matrix validateAndRaiseException AbstractValidator BetweenValidator PositiveValidator SectorMB123Validator WheelValidator SectorMB4Validator StationValidator getSectorRangeDict validateRun get_fetched_data fetch_data_by_identifier get get_matrix_from_DB buildDicts list map buildDicts get_json get_network_scores Pagination append append Pagination get_contamination_scores append Pagination get_not_evaluated_network_scores get_not_evaluated_network_scores mark_as_skipped buildDicts get_one_record list _generate_json_lines remove_negatives enumerate reevaluate_all make_response copy ConfigParser read _get_default_database_config _get_collection getConfig _get_param_dicts list _handle_dependant_params _handle_static_params warn keys _extend_with_new_params_combinations extend list range list range range items list defaultdict sort warn append _ensure_directory makedirs _get_file_name _process_data _load_from_database _write_to_file print write_to_file _process_data _save_to_db _read_file from_row_to_record list items print list get_all_loaded_run_identifiers update_user_score append_saliency append_estimation run_async_in_thread get_one save run_async_in_thread get update print get_all process update_status int validateRun get get_network_score get get_saliency_map list map join load_model _get_candidate_names gettempdir save next transform reshape array remove_negatives scaleMatrix resizeMatrix resize_matrix_to_form place concatenate remove_negatives reshape astype array start Thread new_event_loop set_event_loop ensure_future run_until_complete function tolist validateAndRaiseException | # CERN-CMS-DQM-DT-visualization This is project I did during summer internship at European Organization for Nuclear Research. This application was developed for visualizing data from compact muon solenoid data quality monitoring drift tube chambers. ## Structure Modules by order of significance: * `logic` - contains main program logic which connects all modules * `database` - module for responsible for persisting data * `dataFetching` - module for fetching data from external api * `machineLearning` - module for processing data with neural network * `config` - common config functions | 700 |
Manthata/william- | ['multiple object tracking'] | ['Simple Online and Realtime Tracking'] | utils/utils.py utils/datasets.py object_tracker.py utils/parse_config.py models.py sort.py 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 pop int YOLOLayer Sequential ZeroPad2d MaxPool2d add_module Conv2d ModuleList EmptyLayer Upsample append BatchNorm2d LeakyReLU sum enumerate unsqueeze_ Variable Compose min type float round minimum maximum float sqrt linear_assignment iou concatenate reshape append zeros empty enumerate add_argument ArgumentParser rstrip strip open startswith append split dict strip split open data normal_ __name__ constant_ concatenate size maximum sum range clamp min max minimum eps expand_dims maximum data sort new squeeze size shape unsqueeze cuda unique bbox_iou append max is_cuda cat enumerate int fill_ FloatTensor ones concatenate size range unsqueeze bbox_iou zeros argmax log | # PyTorch 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 3. YOLO paper: https://pjreddie.com/media/files/papers/YOLOv3.pdf 4. SORT paper: https://arxiv.org/pdf/1602.00763.pdf 5. Alex Bewley's SORT implementation: https://github.com/abewley/sort | 701 |
MarcoForte/DeepInteractiveSegmentation | ['interactive segmentation'] | ['Getting to 99% Accuracy in Interactive Segmentation'] | networks/transforms.py demo.py networks/models.py dataloader.py networks/resnet_GN_WS.py networks/layers_WS.py interaction.py read_gt AlphaTestDataset read_image np_to_torch scale_input test pred str2bool NOCS remove_non_fg_connected dt get_largest_incorrect_region jaccard click_position robot_click BatchNorm2d Conv2d MattingModuleBase build_model Resnet ResnetDilated ModelBuilder usr_encoder2 MattingModuleSingleGpu InteractiveSegNet conv1x1 l_resnet50 ResNet Bottleneck conv3x3 BasicBlock groupnorm_normalise_image trimap_transform dt append range ceil int resize remove_non_fg_connected scale_input INTER_NEAREST INTER_LANCZOS4 AlphaTestDataset imwrite print NOCS jaccard mean shape pred dataset_dir robot_click zeros range __getitem__ connectedComponents uint8 zeros_like astype unique append argmax click_position get_largest_incorrect_region dt where shape unravel_index zeros argmax bool astype zeros_like connectedComponents uint8 zeros_like astype where zip append load weights build_encoder ModelBuilder cuda load_state_dict MattingModuleSingleGpu build_decoder ResNet zeros exp range range | MarcoForte/DeepInteractiveSegmentation | 702 |
MarielleJansen/Liver-metastases-detection | ['liver segmentation'] | ['Liver segmentation and metastases detection in MR images using convolutional neural networks'] | LiverMetastaseDetection_Pnet_DCEDWI.py imsave standardization load_data_val load_data build_network load_data_train iterate_in_mb_test iterate_in_mb_train read_image bbox load asarray get_data str join concatenate mean swapaxes read_image str join concatenate mean swapaxes read_image sort reshape astype flatten shape mean zeros std join str concatenate standardization mean swapaxes read_image Model Input compile concatenate int concatenate ones rotate choice zeros range int concatenate ones rotate choice zeros range sum any GetImageFromArray WriteImage | # Liver-metastases-detection Code to train and test a dual pathway P-net CNN. The CNN takes dynamic contrast enhanced MR and diffusion weighted MR images as input. Each pathway of the CNN processes one MR sequence and at the end of the CNN the feature maps are concatenated. A liver mask can be used to exclude the region outside the liver to reduce false positives. Python 3.5 code using keras and tensorflow. The training and validation data have been pre-processed, as described in the paper. This code is used for: M.J.A. Jansen, H.J. Kuijf, M. Niekel, W.B. Veldhuis, F.J. Wessels, M.A. Viergever, and J.P.W. Pluim, “Liver segmentation and metastases detection in MR images using convolutional neural networks”, J. Med. Imag. 6(4), 044003 (2019), doi: 10.1117/1.JMI.6.4.044003. | 703 |
MarielleJansen/Liver-segmentation | ['liver segmentation'] | ['Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation'] | LiverSegmentationCNN.py loadData.py dice_coef_loss diff_loss iterate_in_mb_test build_network dice_coef iterate_in_mb_train imsave load_data abs sum flatten flatten abs sum Model Input compile int concatenate ones choice zeros range int concatenate ones choice zeros range GetImageFromArray WriteImage load_test load_train | # Liver-segmentation CNN for liver segmentation with DCE-MR images. The six phases of the dynamic contrast enhanced (DCE) MRI are used as input images. The six phases are concatenated to one image with six channels. Code is based on Python 3.6, tensorflow, and keras. The script is not adjusted for general use. Directories and data should be supplied by the user. Based on: Mariëlle J. A. Jansen, Hugo J. Kuijf, and Josien P. W. Pluim "Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109491V (15 March 2019); https://doi.org/10.1117/12.2506770 | 704 |
MarvinTeichmann/ConvCRF | ['semantic segmentation'] | ['Convolutional CRFs for Semantic Segmentation'] | utils/pascal_visualizer.py demo.py utils/synthetic.py setup.py benchmark.py convcrf/convcrf.py utils/visualization.py fullcrf/fullcrf.py utils/test_utils.py get_parser plot_results do_crf_inference get_parser plot_results do_crf_inference MessagePassingCol GaussCRF get_test_conf get_default_conf ConvCRF _get_ind _negative exp_and_normalize show_memusage FullCRF PascalVisualizer augment_label np_onehot _get_simple_unary _get_simple_img SegmentationVisualizer pyinn forward default_conf cuda transpose normalize range format FullCRF debug info compute nospeed time GaussCRF Variable reshape cpu Tensor imwrite add_subplot axis warning argmax show set_title imshow format id2color concatenate debug astype tight_layout info PascalVisualizer uint8 output figure add_argument ArgumentParser new_query collect format info softmax reshape rand shape floor np_onehot resize randint reshape tuple vstack zeros sum zeros reshape tuple | ConvCRF ======== This repository contains the reference implementation for our proposed [Convolutional CRFs][4] in PyTorch (Tensorflow planned). The two main entry-points are [demo.py](demo.py) and [benchmark.py](benchmark.py). Demo.py performs ConvCRF inference on a single input image while benchmark.py compares ConvCRF with FullCRF. Both scripts output plots similar to the one shown below.  Requirements ------------- **Plattform**: *Linux, python3 >= 3.4 (or python2 >= 2.7), pytorch 0.4 (or pytorch 0.3 + pyinn), cuda, cudnn* **Python Packages**: *numpy, imageio, cython, scikit-image, matplotlib* To install those python packages run `pip install -r requirements.txt` or `pip install numpy imageio cython scikit-image matplotlib`. I recommand using a [python virtualenv][1]. ### Optional Packages: pyinn, pydensecrf | 705 |
MasazI/cnn_depth_tensorflow | ['monocular depth estimation'] | ['Depth Map Prediction from a Single Image using a Multi-Scale Deep Network'] | model_part.py train_operation.py dataset.py prepare_data.py task.py model.py output_predict DataSet _add_loss_summaries inference inference_refine loss _variable_on_gpu conv2d _variable_with_weight_decay fc convert_nyu main train train _add_loss_summaries fromarray uint8 print transpose MakeDirs save zip max enumerate conv2d reshape fc max_pool conv2d concat dropout max_pool subtract reshape multiply square reduce_sum pow reduce_mean add_to_collection name get_collection apply average ExponentialMovingAverage scalar multiply add_to_collection _variable_on_gpu l2_loss truncated_normal_initializer get_variable fromarray join uint8 remove print transpose File shuffle save zip append download max enumerate makedirs train MakeDirs int trainable_variables print name _add_loss_summaries apply apply_gradients histogram ExponentialMovingAverage exponential_decay float scalar | # cnn_depth_tensorflow cnn_depth_tensorflow is an implementation of depth estimation using tensorflow. Original paper is "Depth Map Prediction from a Single Image using a Multi-Scale Deep Network". https://arxiv.org/abs/1406.2283  # requierments - TensorFlow 0.10+ - Numpy # How to train - Download training data. Please see readme.md in data directory. | 706 |
MasterBin-IIAU/CSA | ['adversarial attack'] | ['Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible Noises'] | pysot/vot_iter/vot.py pysot/tools/test.py pysot/training_dataset/coco/pycocotools/cocoeval.py pix2pix/models/G_template_L2_500_model.py pysot/pysot/tracker/siamrpnlt_tracker.py pysot/toolkit/datasets/vot.py GAN_utils_template_1.py pysot/training_dataset/vid/gen_json.py pix2pix/models/G_template_L2_500_regress_model.py pysot/toolkit/datasets/dataset.py pix2pix/data/__init__.py pix2pix/models/G_search_L2_500_model.py pysot/toolkit/evaluation/f1_benchmark.py pysot/pysot/models/model_builder.py pix2pix/scripts/edges/batch_hed.py pix2pix/models/test_model.py pix2pix/scripts/eval_cityscapes/evaluate.py pix2pix/scripts/eval_cityscapes/util.py pysot/toolkit/utils/__init__.py pysot/setup.py pix2pix/scripts/test_before_push.py data_utils.py pix2pix/models/G_template_L2_1000_model.py pix2pix/options/__init__.py pysot/pysot/models/init_weight.py pix2pix/models/networks.py pysot/toolkit/visualization/draw_eao.py Unified_GOT10K_process.py siamRPNPP.py pix2pix/util/util.py pysot/pysot/datasets/augmentation.py pysot/toolkit/visualization/draw_utils.py pix2pix/scripts/eval_cityscapes/cityscapes.py pysot/toolkit/visualization/__init__.py pysot/training_dataset/yt_bb/par_crop.py pix2pix/options/test_options1.py pysot/pysot/utils/anchor.py pix2pix/datasets/prepare_cityscapes_dataset.py pysot/training_dataset/yt_bb/checknum.py pysot/tools/test_new.py pysot/tools/run_template_search_adv1.py pysot/tools/demo.py pix2pix/models/G_search_resize_512_model.py pix2pix/data/unaligned_dataset.py common_path_gauss.py pix2pix/models/G_template_L2_1000_regress_model.py pysot/training_dataset/coco/par_crop.py pysot/tools/train.py pysot/pysot/models/head/__init__.py pysot/training_dataset/coco/pycocotools/mask.py pysot/toolkit/evaluation/__init__.py pysot/pysot/core/xcorr.py pysot/pysot/models/neck/neck.py GAN_utils_template_0.py pix2pix/options/test_options0.py pysot/training_dataset/vid/visual.py pysot/tools/run_template_search_adv0.py pysot/training_dataset/det/gen_json.py pix2pix/models/G_search_L2_500_regress_model.py pix2pix/datasets/make_dataset_aligned.py pysot/training_dataset/vid/parse_vid.py pysot/training_dataset/coco/visual.py pysot/training_dataset/yt_bb/gen_json.py pix2pix/data/image_folder.py pysot/vot_iter/vot_iter.py pysot/training_dataset/coco/pycocotools/setup.py pix2pix/data/single_dataset.py pysot/training_dataset/vid/par_crop.py pysot/pysot/models/neck/__init__.py pysot/pysot/models/backbone/__init__.py pix2pix/util/image_pool.py pix2pix/models/base_model.py pysot/toolkit/datasets/__init__.py pix2pix/data/base_dataset.py pysot/toolkit/visualization/draw_f1.py pysot/pysot/utils/bbox.py pysot/tools/hp_search.py pysot/tools/run_template_adv0.py pysot/pysot/utils/lr_scheduler.py train0.py train1.py pysot/toolkit/evaluation/ar_benchmark.py pix2pix/models/cycle_gan_model.py pysot/pysot/models/head/mask.py pix2pix/data/colorization_dataset.py pysot/toolkit/utils/statistics.py pysot/pysot/tracker/siammask_tracker.py pix2pix/options/base_options1.py pysot/training_dataset/yt_bb/visual.py pix2pix/datasets/combine_A_and_B.py pysot/pysot/utils/model_load.py pysot/toolkit/datasets/trackingnet.py pysot/training_dataset/det/par_crop.py pysot/training_dataset/det/visual.py pix2pix/options/base_options0.py pysot/toolkit/datasets/nfs.py pysot/pysot/utils/average_meter.py common_path_pulse.py pix2pix/data/template_dataset.py pix2pix/models/G_search_resize_512_regress_model.py pix2pix/train.py pysot/pysot/models/backbone/alexnet.py GAN_utils_search_0.py pysot/toolkit/utils/misc.py pysot/pysot/datasets/dataset.py pix2pix/models/__init__.py GAN_utils_search_1.py siamrpnpp_utils.py pysot/pysot/utils/log_helper.py pysot/tools/vis_gauss_impulse_noise.py pysot/pysot/models/head/rpn.py pysot/pysot/utils/misc.py pysot/tools/attack_search_gauss.py pix2pix/util/visualizer.py pysot/pysot/models/loss.py pix2pix/util/html.py pysot/tools/supplementary.py pysot/pysot/tracker/siamrpn_tracker.py pysot/pysot/models/backbone/mobile_v2.py pysot/tools/run_search_adv1.py pix2pix/util/get_data.py pysot/pysot/core/config.py pysot/pysot/utils/distributed.py common_path.py pysot/training_dataset/coco/pycocotools/coco.py pix2pix/data/aligned_dataset.py pix2pix/util/__init__.py pysot/toolkit/evaluation/ope_benchmark.py pysot/tools/attack_search_pulse.py pysot/toolkit/evaluation/eao_benchmark.py pysot/toolkit/visualization/draw_success_precision.py attack_utils.py pysot/pysot/tracker/base_tracker.py pix2pix/models/template_model.py pysot/toolkit/datasets/lasot.py pix2pix/options/train_options1.py pysot/tools/run_search_adv0.py pysot/toolkit/datasets/video.py pysot/training_dataset/coco/pycocotools/__init__.py pix2pix/models/colorization_model.py pysot/pysot/models/backbone/resnet_atrous.py pix2pix/options/train_options0.py pysot/toolkit/datasets/otb.py pix2pix/test.py pysot/tools/eval_new.py pysot/pysot/tracker/tracker_builder.py pysot/training_dataset/coco/gen_json.py pysot/pysot/datasets/anchor_target.py pysot/tools/attack_search_adv.py pysot/toolkit/datasets/uav.py pysot/tools/eval.py pysot/toolkit/datasets/got10k.py pysot/tools/run_template_adv1.py add_pulse_noise adv_attack_template_S get_subwindow multi_cropx adv_attack_template adv_attack_search generate_impulse_mask add_gauss_noise adv_attack_search_new img2tensor normalize GOT10k_dataset tensor2img SiamRPNPP get_subwindow_numpy get_subwindow crop_z crop_x AlignedDataset BaseDataset __flip get_transform __crop __make_power_2 __print_size_warning __scale_width get_params ColorizationDataset is_image_file ImageFolder default_loader make_dataset SingleDataset TemplateDataset UnalignedDataset CustomDatasetDataLoader get_option_setter find_dataset_using_name create_dataset get_file_paths align_images process_cityscapes check_matching_pair load_resized_img BaseModel ColorizationModel CycleGANModel GsearchL2500Model GsearchL2500regressModel Gsearchresize512Model Gsearchresize512regressModel GtemplateL21000Model GtemplateL21000regressModel GtemplateL2500Model GtemplateL2500regressModel get_norm_layer PixelDiscriminator Identity GANLoss ResnetGenerator ResnetBlock define_D UnetGenerator UnetSkipConnectionBlock init_weights get_scheduler init_net NLayerDiscriminator cal_gradient_penalty define_G TemplateModel TestModel get_option_setter create_model find_model_using_name BaseOptions BaseOptions TestOptions TestOptions TrainOptions TrainOptions run parse_args cityscapes main segrun fast_hist get_out_scoremap feed_net get_scores GetData HTML ImagePool print_numpy diagnose_network mkdirs mkdir save_image tensor2im save_images Visualizer xcorr_depthwise xcorr_fast xcorr_slow AnchorTarget Augmentation SubDataset TrkDataset init_weights select_cross_entropy_loss weight_l1_loss get_cls_loss ModelBuilder AlexNet AlexNetLegacy alexnetlegacy alexnet conv_1x1_bn InvertedResidual conv_bn mobilenetv2 MobileNetV2 ResNet resnet50 Bottleneck conv3x3 resnet34 resnet18 BasicBlock get_backbone Refine MaskCorr UPChannelRPN DepthwiseRPN RPN DepthwiseXCorr MultiRPN get_mask_head get_rpn_head get_refine_head AdjustLayer AdjustAllLayer get_neck BaseTracker SiameseTracker SiamMaskTracker SiamRPNLTTracker SiamRPNTracker build_tracker Anchors AverageMeter Meter center2corner get_axis_aligned_bbox rect1_2_cxy_wh rect_2_cxy_wh cxy_wh_2_rect1 get_min_max_bbox IoU corner2center cxy_wh_2_rect reduce_gradients DistModule get_world_size _get_local_ip broadcast_buffers broadcast_params get_rank average_reduce _dist_init dist_init Filter log_once Dummy init_log get_format get_format_custom find_caller main LogOnce print_speed add_file_handler LRScheduler WarmUPScheduler _build_lr_scheduler build_lr_scheduler LogScheduler LinearStepScheduler Net CosStepScheduler _build_warm_up_scheduler MultiStepScheduler StepScheduler _bold commit _describe describe _exec _color load_pretrain remove_prefix restore_from check_keys Dataset GOT10kDataset GOT10kVideo LaSOTDataset LaSOTVideo NFSDataset NFSVideo OTBDataset OTBVideo TrackingNetVideo TrackingNetDataset UAVVideo UAVDataset Video VOTLTDataset VOTLTVideo VOTVideo VOTDataset DatasetFactory AccuracyRobustnessBenchmark EAOBenchmark F1Benchmark OPEBenchmark determine_thresholds calculate_accuracy calculate_expected_overlap calculate_f1 overlap_ratio success_overlap_bin success_overlap success_error determine_thresholds calculate_failures draw_eao draw_f1 draw_success_precision main main main main get_frames main main parse_range_int run_tracker parse_range _check_and_occupation main main main main main main main main main seed_torch build_opt_lr build_data_loader log_grads main train tensor2img crop_hwc printProgress pos_s_2_bbox main crop_img crop_like_SiamFC COCO _isArrayLike Params COCOeval encode decode area toBbox crop_hwc crop_xml printProgress pos_s_2_bbox main crop_like_SiamFC check_borders check_size crop_hwc printProgress crop_video pos_s_2_bbox main crop_like_SiamFC parse_and_sched crop_hwc printProgress parse_and_sched pos_s_2_bbox dl_and_cut crop_like_SiamFC VOT setup_tracker warmup normalize template_adv255 normalize normalize search_adv255 normalize tensor_adv255 int uint8 isinstance CUDA transpose astype float32 from_numpy shape any floor cuda resize zeros max INSTANCE_SIZE EXEMPLAR_SIZE get_subwindow sqrt round CONTEXT_AMOUNT zeros sum cuda range clamp size cuda randn rand generate_impulse_mask size clone from_numpy transpose astype float32 uint8 astype permute int uint8 isinstance shape any floor resize zeros max size interpolate EXEMPLAR_SIZE sqrt get_subwindow_numpy round CONTEXT_AMOUNT sum array INSTANCE_SIZE EXEMPLAR_SIZE sqrt get_subwindow_numpy round CONTEXT_AMOUNT sum array crop_size randint maximum load_size Grayscale Lambda Resize crop_size RandomCrop RandomHorizontalFlip append int size round __print_size_warning int size size print is_image_file join sorted append walk items import_module replace find_dataset_using_name CustomDatasetDataLoader load_data join sorted abspath append walk join format new makedirs len paste save range open replace join sorted load_resized_img print glob new check_matching_pair paste save zip enumerate makedirs BatchNorm2d partial InstanceNorm2d LambdaLR CosineAnnealingLR ReduceLROnPlateau StepLR print apply init_weights to DataParallel ResnetGenerator UnetGenerator get_norm_layer NLayerDiscriminator PixelDiscriminator get_norm_layer view size rand grad mean requires_grad_ netD items replace print exit import_module find_model_using_name print __name__ find_model_using_name model print system exit add_argument ArgumentParser list_label_frames gpu_id output_dir load_label palette cityscapes cityscapes_dir open caffemodel_dir str set_device len TEST imsave imresize Net preprocess classes get_scores enumerate segrun result_dir print set_mode_gpu split zeros array makedirs reshape forward feed_net bincount astype sum diag data isinstance transpose tile Tensor numpy print parameters fromarray shape BICUBIC save resize print float64 astype flatten shape mkdir makedirs join items basename get_image_dir add_images add_header append save_image tensor2im view conv2d append range cat conv2d view conv2d size view data fill_ isinstance Conv2d modules zero_ BatchNorm2d kaiming_normal_ index_select cuda view get_cls_loss size abs view MobileNetV2 ResNet ResNet ResNet isinstance isinstance minimum maximum norm size min mean sqrt max mean size min max all_reduce get_world_size FloatTensor values broadcast all_reduce _all_buffers get_world_size broadcast int init_process_group set_device get_world_size device_count SOCK_DGRAM socket connect AF_INET _dist_init data requires_grad log_once format all_reduce parameters int Filter format addFilter Formatter int Filter format addFilter Formatter setFormatter format_func getLogger addHandler StreamHandler add setLevel setFormatter getLogger addHandler get_format FileHandler floor info getLogger f_back co_filename list basename hasattr normcase f_code current_frame log format getLogger debug error init_log warning info critical _build_lr_scheduler EPOCH LR LR_WARMUP WARMUP popen requires_grad format named_children len training named_parameters append _color join format _exec dirname abspath append _describe len format set info keys len format info load remove_prefix format check_keys load_state_dict info current_device load remove_prefix check_keys load_state_dict current_device inf isinstance ones sort flatten floor linspace array len len min nanmean vot_overlap_traj range len minimum maximum arange ones overlap_ratio zeros float sum range len ones overlap_ratio len ones power sqrt zeros float sum range len astype int32 zeros mean zeros sum array enumerate len float32 logical_not isnan any zeros sum range grid add_subplot pi set_visible linspace max values show list legend append set_thetagrids format plot zip enumerate items set_yticks min figure array set_ylim subplots arange grid axis xticks argmax values yticks set_aspect show list sorted ylabel title legend plot mean enumerate items xlabel subplots arange grid axis xticks yticks set_aspect sorted ylabel title savefig legend plot autoscale mean keys enumerate items xlabel vot_overlap track_adv polylines get_axis_aligned_bbox FONT_HERSHEY_SIMPLEX MASK dataset build_tracker destroyAllWindows getTickCount list name waitKey map imshow ModelBuilder append merge_from_file getTickFrequency eval tracker_name mkdir init join putText rectangle create_dataset track_gauss track_impulse join sorted VideoCapture read glob video_name imread range config get_frames device addWeighted transpose selectROI load_state_dict to WND_PROP_FULLSCREEN snapshot track astype is_available video_name load uint8 namedWindow int32 LaSOTDataset UAVDataset tracker_path VOTLTDataset NFSDataset dirname AccuracyRobustnessBenchmark glob realpath show_result set_tracker OPEBenchmark min OTBDataset EAOBenchmark tracker_prefix num VOTDataset F1Benchmark draw_success_precision vis items map split map split format vot_overlap track getTickFrequency print get_axis_aligned_bbox init append array getTickCount enumerate makedirs isfile init_adv init_adv_S video track_supp seed str manual_seed DistributedSampler TrkDataset DataLoader info TRAIN_LAYERS REFINE build_lr_scheduler isinstance START_EPOCH SGD parameters eval MASK modules BatchNorm2d train step ADJUST items norm weights_grads replace add_scalar reduce_gradients describe model clip_grad_norm_ zero_grad save dataset GRAD_CLIP sorted BATCH_SIZE START_EPOCH len get_rank module SNAPSHOT_DIR update format EPOCH param_groups get_world_size is_valid_number avg info average_reduce item print_speed enumerate items time add_scalar build_opt_lr get_cur_lr backward AverageMeter parameters log_grads step makedirs commit PRETRAINED backbone add_file_handler dist_init START_EPOCH LOG_DIR RESUME restore_from module SummaryWriter DistModule build_data_loader cfg INFO build_opt_lr dumps load_pretrain train str int format write float round flush warpAffine float astype sqrt crop_hwc sum pos_s_2_bbox join format imwrite mean imread crop_like_SiamFC enumerate makedirs COCO imgs shape join parse imwrite replace format mean find findall imread crop_like_SiamFC enumerate makedirs sorted sqrt float prod join sorted parse int replace imwrite format glob text mean find findall imread crop_like_SiamFC makedirs listdir int join str iterrows dump print from_csv unique open str join format imwrite int iterrows check_call mean imread crop_like_SiamFC template cuda range merge_from_file eval ModelBuilder build_tracker warmup | # Cooling-Shrinking Attack (CSA) The official implementation for CVPR2020 Paper Cooling-Shrinking Attack: Blinding the tracker with imperceptible noises ## Demos <div align="center"> <img src="demo/carscale.gif" width="600px" height="450px" /> <p>Demos for Cooling-Shrinking Attack.</p> </div> Please cite our work as follows, if you find it helpful to your research. :) ``` @inproceedings{CSA-CVPR2020, | 707 |
MatteoZambra/SM_ML__MScThesis | ['unsupervised pre training'] | ['Exact solutions to the nonlinear dynamics of learning in deep linear neural networks'] | srcs/DataSets/BinaryTreeDS.py srcs/utils/train.py srcs/utils/graphds.py srcs/DataSets/ClustersDS.py srcs/utils/__init__.py srcs/utils/motifsreader.py srcs/main.py srcs/test_models_dims.py srcs/utils/streams.py srcs/utils/preprocess.py srcs/utils/postprocess.py BinaryTreeDataSet graphDataStructure proGraph MotifObj motifs_load analysis_launcher variations_distributions significance_profiles scatter_plot motifs_selection variations_comparison t_test bins_for_scheme spectrum_discretize parameters_categories spectrum_split_plot hierarchy serialize_dataframes check_create_directory efficacy_plots model_train weights_kdes load_synth_data model_init load_mnist_data zeros format sort_index variations_distributions concat variations_comparison motifs_load rename scatter_plot motifs_selection fillna append int str to_numeric T set_index abs concat argmin astype sort_values mean subgrID append float DataFrame values Zscore subplots concat Line2D linspace set_major_formatter max fillna show str set_xlabel tolist FormatStrFormatter title scatter savefig legend format plot close zip print min set_ylabel subplots concat flatten linspace xticks DataFrame max fillna values show str set_rotation tolist ylabel title savefig append gca input sort_values range format get_xticklabels close float print xlabel min hist histogram len show str format set_rotation plot sort_index set_xticklabels set_xlabel get_xticklabels tolist FormatStrFormatter title set_ylabel rename set_major_formatter savefig gca fillna subplots arange Line2D max show str set_title set_xlabel tolist scatter savefig legend append format plot set_xticklabels close min set_xticks set_ylabel set_ylim len subplots GetEdges pdf set_major_formatter xticks max values show from_dict load_model set_xlabel FormatStrFormatter title savefig legend append range format plot concatenate size close proGraph print sort min hist set_facecolor histogram set_ylabel array fit from_dict format asarray load_model check_create_directory GetEdges print proGraph parameters_categories savetxt spectrum_split_plot values ttest_ind format print write close mean zip abs std values open yscale show format subplots xlabel close FormatStrFormatter ylabel title hist set_facecolor set_major_formatter legend savefig xticks Series range zeros len system getcwd check_create_directory dump format close keys open format check_create_directory glorot_normal print Sequential SGD add Dense save Orthogonal RandomNormal compile load train_test_split close open reshape to_categorical astype load_data save load_mnist_data show subplot load_model ylabel title savefig legend format check_create_directory plot close load_synth_data evaluate print xlabel EarlyStopping figure fit subplots GetEdges kdeplot abs values show from_dict ttest_ind load_model set_title set_xlabel savefig update format size close mean proGraph print sort set_ylabel std show format arange plot xlabel ylabel model_train title savefig figure legend append xticks | # Emergence of Network Motifs in Deep Neural Networks ## Purpose This repo contains the code intended to support the developing process of my Master Degree Thesis. The same results are used in the article Zambra, Maritan, Testolin, "Emergence of network motifs in deep neural networks", _Entropy_, **2020**, 22, 204, DOI: [https://doi.org/10.3390/e22020204](https://doi.org/10.3390/e22020204). ## Summary Motivated by recent publications concerning the development and formalisation of a theoretical framework in which Deep Learning may fit into, this thesis topic and work is finalised to investigate the evolution of the topology of a simple feedforward artificial neural network during the process of learning. Network Science is thought to be an interesting standpoint. Inspired by [Milo et al. (2002)](https://science.sciencemag.org/content/298/5594/824), a simple multilayer-perceptron model is inspected as breeding ground for the emergence of network motifs, once the model is trained on different sets of synthetic data and with different initialisation schemes ([Saxe et al., 2013](https://arxiv.org/abs/1312.6120), [Glorot and Bengio, 2010](http://proceedings.mlr.press/v9/glorot10a.html)). The question addressed is whether one can appreciate the trace that the learning dynamics leaves in the form of functional modules. The neural network inspected is trained on a multi-label classification task using two different data sets. Another pivotal aspect is the influence of the initialization scheme in relationship with training efficiency. Turns out that non-trivial schemes, such as Orthogonal matrices and Glorot-Bengio schemes, could impress a sharper initial fingerprint to the initial motifs landscape, thus encouraging these motifs to develop. An endearing biological resemblance with **kinases** dynamics in transduction networks may be appreciated (see [Alon, 2006](https://www.crcpress.com/An-Introduction-to-Systems-Biology-Design-Principles-of-Biological-Circuits/Alon/p/book/9781439837177)). Moreover, signature of diversity can be appreciated for different arrangments of data sets and initialisation schemes. ## Code ### Contents | 708 |
MatusChladek/Semantic-Tissue-Segmentation | ['semantic segmentation'] | ['TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation'] | TernausNet/train.py TernausNet/validation.py TernausNet/unet_models.py TernausNet/utils.py utils.py TernausNet/dataset.py UnNormalize ImageTransforms TissuesDataset SquareCenterCrop UNet16 UNet11 ConvRelu unet11 AlbuNet conv3x3 DecoderBlockV2 DecoderBlock calculate_dice validation_binary validation_multi calculate_iou get_jaccard calculate_confusion_matrix_from_arrays load UNet11 load_state_dict isinstance sum uint32 T astype histogramdd append float sum range append float sum range | # Semantic Tissue Segmentation Disclaimer: Unfortunately dataset canot be shared publicly due to NDA. Project was done on small 27+8 microscopy images with a goal of classifying whether each pixel on the image is a human tissue or not. Main challenges were mainly related to sample size, unbalanced classes. two different augmentations pipelines were used with two different models. ## Models. * **TernausNet** ([arXiv paper](https://arxiv.org/abs/1801.05746)) - U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation implemented from [github repository](https://github.com/ternaus/TernausNet) ###### Introduction TernausNet is a modification of the celebrated UNet architecture that is widely used for binary Image Segmentation.  (Network architecure) | 709 |
MauritsBleeker/Bi-STET | ['scene text recognition'] | ['Bidirectional Scene Text Recognition with a Single Decoder'] | src/Optimizer.py src/utils/WordLengthAccuracy.py src/Trainer.py data_utils/test_sets/create_svtp.py data_utils/make_dev_set.py src/Decoder.py src/utils/avarage_checkpoints.py src/utils/Timer.py src/DecoderLayer.py data_utils/test_sets/create_icdar15.py src/utils/DataSummary.py src/PositionalEncoding.py data_utils/train_sets/process_synth90k.py data_utils/train_sets/process_synth800k.py src/MultiHeadedAttention.py data_utils/test_sets/create_icdar13.py src/BiSTET.py src/LayerNorm.py data_utils/test_sets/create_iiit5k.py src/EncoderLayer.py src/Embeddings.py data_utils/test_sets/create_svt.py data_utils/test_sets/create_icdar03.py src/utils/ExampleWriter.py src/Batch.py src/utils/visualization.py src/Dataset.py src/utils/utils_functions.py src/Config.py src/EncoderDecoder.py data_utils/train_sets/combine_synth800k_annotations.py src/utils/LexionInference.py src/PredictionLayer.py src/Validator.py src/utils/Word.py data_utils/train_sets/process_synth90k_annotations.py src/PositionwiseFeedForward.py data_utils/test_sets/create_cute80.py src/main.py src/ResNet.py src/SublayerConnection.py src/Encoder.py data_utils/test_sets/lexicons/generate_lexicon_pickles.py data_utils/train_sets/image_binaries.py src/FeatureExtractionNetwork.py create_cute80 _is_difficult _random_lexicon create_ic03 create_ic13 _is_difficult char_check create_svt_subset _random_lexicon _process_iiit5k_lex _is_difficult generate_iiit5k_lexicon generate_svt_lexicon generate_icdar03_lexicon validate_tags process_annotations Batch BiSTET Config Dataset Decoder DecoderLayer Embeddings Encoder EncoderDecoder EncoderLayer FeatureExtractionNetwork LayerNorm MultiHeadedAttention LossCompute PositionalEncoding PositionwiseFeedForward PredictionLayer ResNet BasicBlock conv3x3 conv1x1 SublayerConnection Trainer Validator main last_n_checkpoints average_checkpoints DataSummary ExampleWriter LexiconInference Timer get_latest_check_point make_logger get_dataset_loader clones print_outputs char_idxs_to_word subsequent_mask concat_ltr_rtl get_attention_distributions attention make_folder draw_decoder_self_attention draw_encoder_self_attention draw visualize_attention Word WordLengthAccuracy join format TFRecordWriter data_dir print write SerializeToString close Example lower split insert deepcopy shuffle print join format getroot glob join format print print join format getroot str dump defaultdict findall join print _random_lexicon lower open getroot find append enumerate append lower join dump defaultdict print _process_iiit5k_lex open loadmat range len join str defaultdict dump print open getroot find findall enumerate split min expand_dims array append float max range join list load items list isinstance OrderedDict HalfTensor append float keys len int group fullmatch append listdir compile format print add_argument inputs add_mutually_exclusive_group num_epoch_checkpoints output ArgumentParser last_n_checkpoints save parse_args num_update_checkpoints average_checkpoints dropout size transpose matmul sqrt masked_fill softmax print numpy cpu argmax enumerate DataLoader Dataset setFormatter basicConfig addHandler StreamHandler Formatter setLevel INFO join makedirs glob join max astype data deepcopy defaultdict layers append enumerate heatmap show subplot format set_title suptitle print transpose draw axis tight_layout GridSpec imshow figure flip range show subplot format set_title suptitle draw axis tight_layout GridSpec imshow figure range draw_decoder_self_attention draw_encoder_self_attention set | # Bi-STET This is the repository for 'Bidirectional Scene Text Recognition with a Single Decoder', by Maurits Bleeker and Maarten de Rijke [[pdf](https://arxiv.org/pdf/1912.03656.pdf)] The base source-code for this project comes from: http://nlp.seas.harvard.edu/2018/04/03/attention.html I have tried to keep the code as general as possible. However, some elements of the pipeline are specially for the environment I worked with. ## Model weights and reproducibility To reproduce the results of the paper, please use the final model parameters. https://drive.google.com/file/d/1OwJ3iVpRhnjIZyOi7aOQIeLv7N1DHZkC/view?usp=sharing In the folder data_utils/, all the scripts to generate the train and test sets as used for this paper are provided. # Python and package versions * Python 3.7 | 710 |
MaybeShewill-CV/CRNN_Tensorflow | ['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'] | local_utils/establish_char_dict.py tools/test_shadownet.py crnn_model/__init__.py tfserve/crnn_python_client_via_request.py tools/write_tfrecords.py tools/train_shadownet.py config/global_config.py tools/__init__.py tfserve/export_saved_model.py tools/evaluate_shadownet.py crnn_model/cnn_basenet.py local_utils/log_utils.py data_provider/shadownet_data_feed_pipline.py config/__init__.py crnn_model/crnn_net.py local_utils/evaluation_tools.py tools/apply_ocr_pdf.py tfserve/crnn_python_client_via_grpc.py local_utils/__init__.py tools/recongnize_chinese_pdf.py data_provider/__init__.py data_provider/tf_io_pipline_fast_tools.py CNNBaseModel ShadowNet CrnnDataProducer CrnnDataFeeder _write_tfrecords _int64_feature _is_valid_jpg_file CrnnFeatureReader _FeatureIO _bytes_feature _float_feature CrnnFeatureWriter CharDictBuilder compute_accuracy print_cm plot_confusion_matrix init_logger post_process convert_predict_response_into_nparray main make_request parse_args request_crnn_predict init_args build_saved_model evaluate_shadownet init_args args_str2bool init_args recognize split_pdf_image_into_row_image_block locate_text_area args_str2bool recognize init_args args_str2bool init_args average_gradients train_shadownet compute_net_gradients train_shadownet_multi_gpu args_str2bool init_args write_tfrecords append int float append float encode read close seek open get format Features qsize resize error debug _is_valid_jpg_file close write SerializeToString IMREAD_COLOR tostring Example strftime getpid info imread format print astype float32 mean enumerate len format arange product print yticks text xlabel astype ylabel colorbar tight_layout imshow title info xticks max range len format print max range enumerate len join setFormatter getLogger addHandler getcwd WARNING StreamHandler Formatter dirname TimedRotatingFileHandler setLevel makedirs add_argument ArgumentParser make_tensor_proto DEFAULT_SERVING_SIGNATURE_DEF_KEY CopyFrom Predict PredictRequest float32 IMREAD_COLOR insecure_channel resize imread array PredictionServiceStub tuple dim print convert_predict_response_into_nparray format post_process time format print parse_args make_request join format info tuple float32 CrnnFeatureReader IMREAD_COLOR post raise_for_status array resize imread INPUT_SIZE split add_argument ArgumentParser Session ShadowNet ctc_beam_search_decoder tuple placeholder GPU_MEMORY_FRACTION Saver INPUT_SIZE TF_ALLOW_GROWTH inference ConfigProto exists CrnnFeatureReader Saver ctc_greedy_decoder Session ShadowNet ones multiply add SEQ_LENGTH ceil inference TF_ALLOW_GROWTH format sample_counts inputs info ConfigProto int time CrnnDataFeeder GPU_MEMORY_FRACTION COLOR_BGR2GRAY len adaptiveThreshold append sum range cvtColor enumerate sum adaptiveThreshold cvtColor COLOR_BGR2GRAY int ShadowNet ctc_beam_search_decoder ConfigProto placeholder IMREAD_COLOR CrnnFeatureReader GPU_MEMORY_FRACTION split_pdf_image_into_row_image_block TF_ALLOW_GROWTH locate_text_area Saver append inference imread max Session enumerate ctc_greedy_decoder ones close float32 SEQ_LENGTH resize array concat reduce_mean zip append expand_dims compute_gradients compute_loss CrnnFeatureReader localtime Saver compute_loss ctc_greedy_decoder Session str ShadowNet BATCH_SIZE ones get_collection merge_all strftime EPOCHS SEQ_LENGTH cast TF_ALLOW_GROWTH polynomial_decay format inputs FileWriter ConfigProto join time CrnnDataFeeder Variable graph add_graph edit_distance GPU_MEMORY_FRACTION reduce_mean UPDATE_OPS int32 scalar makedirs trainable_variables localtime Saver Session str ShadowNet moving_average_variables average_gradients strftime apply EPOCHS apply_gradients append polynomial_decay TF_ALLOW_GROWTH range format MomentumOptimizer inputs group GPU_NUM FileWriter close info ConfigProto merge join time CrnnDataFeeder Variable graph add_graph GPU_MEMORY_FRACTION reduce_mean MOVING_AVERAGE_DECAY ExponentialMovingAverage scalar makedirs CrnnDataProducer generate_tfrecords makedirs | # CRNN_Tensorflow This is a TensorFlow implementation of a Deep Neural Network for scene text recognition. It is mainly based on the paper ["An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition"](http://arxiv.org/abs/1507.05717). You can refer to the paper for architecture details. Thanks to the author [Baoguang Shi](https://github.com/bgshih). The model consists of a CNN stage extracting features which are fed to an RNN stage (Bi-LSTM) and a CTC loss. ## Installation | 711 |
MaybeShewill-CV/attentive-gan-derainnet | ['rain removal'] | ['Attentive Generative Adversarial Network for Raindrop Removal from a Single Image'] | config/global_config.py tools/train_model.py attentive_gan_model/cnn_basenet.py data_provider/tf_io_pipline_tools.py attentive_gan_model/vgg16.py attentive_gan_model/discriminative_net.py data_provider/data_feed_pipline.py attentive_gan_model/derain_drop_net.py tools/freeze_attentive_gan_derain_net.py tools/test_model.py attentive_gan_model/attentive_gan_net.py attentive_gan_model/__init__.py tools/export_tf_saved_model.py attentive_gan_model/tf_ssim.py GenerativeNet CNNBaseModel DeRainNet DiscriminativeNet SsimComputer VGG16Encoder DerainDataProducer DerainDataFeeder init_args decode write_example_tfrecords augment_for_test random_crop_batch_images augment_for_train morph_process random_horizon_flip_batch_images int64_feature bytes_feature normalize test_load_saved_model init_args build_saved_model test_model visualize_attention_map init_args minmax_scale train_model init_args average_gradients compute_net_gradients train_multi_gpu add_argument ArgumentParser getStructuringElement morphologyEx MORPH_ELLIPSE MORPH_CLOSE MORPH_OPEN format info makedirs uint8 decode_raw reshape float32 stack parse_single_example cast float32 subtract constant cast divide slice random_crop concat as_list concat random_flip_left_right slice uint8 constant Session squeeze reduce_max concat placeholder TF_ALLOW_GROWTH GPU_MEMORY_FRACTION cast Saver inference append DeRainNet ConfigProto reduce_min exists split divide float32 IMREAD_COLOR expand_dims ConfigProto GPU_MEMORY_FRACTION resize TF_ALLOW_GROWTH imread array Session min max zeros array constant divide placeholder IMREAD_COLOR float32 ConfigProto GPU_MEMORY_FRACTION string Saver resize TF_ALLOW_GROWTH DeRainNet imread array Session inference concat reduce_mean zip append expand_dims compute_gradients compute_loss trainable_variables localtime Saver compute_loss exponential_decay DeRainNet ssim Session str BATCH_SIZE psnr get_collection strftime EPOCHS LEARNING_RATE DerainDataFeeder TF_ALLOW_GROWTH rgb_to_grayscale format inputs FileWriter info ConfigProto merge join time Variable graph add_graph convert_image_dtype GPU_MEMORY_FRACTION reduce_mean UPDATE_OPS scalar makedirs | # attentive-gan-derainnet Use tensorflow to implement a Deep Convolution Generative Adversarial Network for image derain task mainly based on the CVPR2018 paper "Attentive Generative Adversarial Network for Raindrop Removal from A Single Image".You can refer to their paper for details https://arxiv.org/abs/1711.10098. This model consists of a attentive attentive-recurrent network, a contextual autoencoder network and a discriminative network. Using convolution lstm unit to generate attention map which is used to help locating the rain drop, multi-scale losses and a perceptual loss to train the context autoencoder network. Thanks for the origin author [Rui Qian](https://github.com/rui1996) The main network architecture is as follows: `Network Architecture` | 712 |
MayraMacasC/AnomalyDetection | ['anomaly detection'] | ['An Unsupervised Framework for Anomaly Detection in a Water Treatment System.'] | Anomaly Detection/S_Distance_Matrix.py STAE-AD/S_ModelsProyTS_Deploy.py STAE-AD/VarConvLSTM.py distance_matrix AttenInputConvLSTM2D abs sqrt append sum range | This is the reference implementation of the STAE-AD model presented in the paper "An Unsupervised Framework for Anomaly Detection in a Water Treatment System", 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) held in Boca Raton, FL, USA, USA. # An Unsupervised Framework for Anomaly Detection in a Water Treatment System Current Cyber-Physical Systems (CPSs) are sophisticated, complex, and equipped with networked sensors and actuators. As such, they have become further exposed to cyberattacks. Recent catastrophic events have demonstrated that standard, human-based management of anomaly detection in complex systems is not efficient enough and have underlined the significance of automated detection, intelligent and rapid response. Nevertheless, existing anomaly detection frameworks usually are not capable of dealing with the dynamic and complicated nature of the CPSs. In this study, we introduce an unsupervised framework for anomaly detection based on an Attention-based Spatio-Temporal Autoencoder. In particular, we first construct statistical correlation matrices to characterize the system status across different time steps. Next, a 2D convolutional encoder is employed to encode the patterns of the correlation matrices, whereas an Attention-based Convolutional LSTM Encoder-Decoder (ConvLSTM-ED) is used to capture the temporal dependencies. More precisely, we introduce an input attention mechanism to adaptively select the most significant input features at each time step. Finally, the 2D convolutional decoder reconstructs the correlation matrices. The differences between the reconstructed correlation matrices and the original ones are used as indicators of anomalies. Extensive experimental analysis on data collected from all six stages of Secure Water Treatment (SWaT) testbed, a scaled-down version of a real-world industrial water treatment plant, demonstrates that the proposed model outperforms the state-of-the-art baseline techniques. --- ## Requirements - Python 3.5.6 - TensorFlow framework version 1.11.0 - Package PyMC3 (That allowed fitting the Bayesian model using Markov Chain Monte Carlo (MCMC)) --- ## Table Of Contents | 713 |
Meet1995/Named-Entity-Recognition-And-Relationship-Linking | ['relation extraction', 'relation classification'] | ['End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures'] | (NER)Entity Recognition _ Relationship Extraction(Drug _ Disease)/LSTM_NER.py (NER)Entity Recognition _ Relationship Extraction(Drug _ Disease)/lstm_utils.py Embeddings/cnn_char_embedding.py NER News/NER_News.py Preprocessing/NLP_Datapreprocess.py NER News/contractions.py Preprocessing/contractions.py | # End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures Python-Keras Implementation of End to End Relation Extraction using Tree and Bi-LSTMs @misc{miwa2016endtoend, title={End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures}, author={Makoto Miwa and Mohit Bansal}, year={2016}, eprint={1601.00770}, archivePrefix={arXiv}, primaryClass={cs.CL} } | 714 |
MeetKai/MK-SQuIT | ['machine translation'] | ['MK-SQuIT: Synthesizing Questions using Iterative Template-filling'] | mk_squit/generation/predicate_bank.py mk_squit/utils/entity_resolver.py mk_squit/utils/metrics.py mk_squit/generation/type_generator.py mk_squit/generation/full_query_generator.py model/score_predictions.py mk_squit/generation/template_generator.py scripts/gather_wikidata.py scripts/generate_type_list.py scripts/stats/calculate_stats.py mk_squit/utils/__init__.py scripts/preprocess.py mk_squit/generation/__init__.py scripts/tf_projector/generate_embeddings.py mk_squit/__init__.py mk_squit/generation/template_filler.py FullQueryGenerator generate PredicateBank example example TemplateFiller TemplateGenerator example TypeGenerator example EntityResolver url_to_qvalue load_entities Metrics example score main postprocess main save_json get_properties get_domains main generate_pos_examples preprocess_things preprocess_properties main tag_prop main main query_type join generate_queries mkdir FullQueryGenerator print PredicateBank TemplateFiller fill_count_query fill_single_ent_query fill_multi_ent_query templates TemplateGenerator TypeGenerator G generate_bidirectional_pred_traversal generate_unidirectional_pred_traversal Gi glob join dict url_to_qvalue evaluate average print replace score str read_csv get join list items json print save_json get join list items json print save_json items list write dumps close dict append open get_properties get_domains join list glob print add set dict append split glob join print splitext dict join append join replace glob print splitext update join list sorted print glob set dict tqdm keys generate_pos_examples preprocess_things preprocess_properties TypeGenerator generate_unidirectional_pred_traversal sorted Gi update lower keys items G TemplateGenerator generate_bidirectional_pred_traversal len load sparql write english close tqdm query_type zip open numpy makedirs | # MK SQuIT: Synthesizing Questions using Iterative Template-Filling MK-SQuIT is a synthetic dataset containing English and SPARQL query pairs. The assembly of question-query pairs is handled with very little human intervention, sidestepping the tedious and costly process of hand labeling data. A neural machine translation model can then be trained on such a dataset, allowing laymen users to access information rich knowledge graphs without an understanding of query syntax. For further details, see our publication: [MK-SQuIT: Synthesizing Questions using Iterative Template-filling](https://arxiv.org/abs/2011.02566) This repository contains all the tools needed to synthetically generate question/query pairs (Text2Sparql) from a given knowledge base: - Generation Pipeline: `mk_squit/generation` - Example Data: `data` - Generated Dataset: `out` - Baseline Model Finetuning and Evaluation: `model` - Metrics: `mk_squit/metrics` - Example Entity Resolver: `mk_squit/entity_resolver` | 715 |
Meghanshu-Bhatt/Neural-artistic-style-transfer | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | Neural-Style transfer/nst_utils.py generate_noise_image reshape_and_normalize_image save_image CONFIG load_vgg_model reshape _conv2d_relu Variable zeros _avgpool loadmat astype reshape MEANS shape MEANS imsave astype | # Neural-artistic-style-transfer Neural Style Transfer we have two images- style and content. We need to copy the style from the style image and apply it to the content image. By, style we basically mean, the patterns, the brushstrokes, etc. Used vgg-19 pretrained model :http://www.vlfeat.org/matconvnet/pretrained/#downloading-the-pre-trained-models This algorithm was created by Gatys et al. (2015) (https://arxiv.org/abs/1508.06576). Future work : I'll work on localized style transfer and mask style transfer | 716 |
Megum1/DFST | ['backdoor attack', 'adversarial attack'] | ['Deep Feature Space Trojan Attack of Neural Networks by Controlled Detoxification'] | train.py CycleGAN/networks.py implement.py run_re.py retrain.py CycleGAN/utils.py CycleGAN/CycleGAN.py neuron_detection.py CycleGAN/data_poisoning.py detoxification.py build_generator reverse_engineer preprocess build_vgg deprocess make_retrain linear_test scheduler make_noise_trigger_test build_generator test make_denoise_train preprocess retrain deprocess noise_filter preprocess getlayer_output color_preprocessing test scheduler retrain scheduler VGG test color_preprocessing train GAN make_benign_test make_seed_test make_data make_retrain_data ResidualBlock Generator ConvLayer Discriminator UpsampleConvLayer DataLoader preprocess make_CycleGAN_data deprocess astype range astype range conv2d Input deconv2d Sequential add Dense MaxPooling2D Conv2D InputLayer Activation BatchNormalization Flatten Dropout str int RE_model constant minimize print build_generator float32 placeholder output output_shape reduce_sum greater clip_by_value build_vgg total_variation cond asarray RE_model reshape variable build_generator preprocess eval clip set_weights format load_model evaluate print to_categorical astype SGD append noise_filter range compile len concatenate print astype shape append range len concatenate print astype shape append range len print shape concatenate format load_model print to_categorical SGD fit_generator preprocess save_weights flow save ImageDataGenerator compile fit load_model evaluate print to_categorical SGD preprocess compile function astype range batch_size add_argument color_preprocessing ArgumentParser parse_args epochs astype range batch_size to_categorical SGD save_weights flow ArgumentParser save parse_args format build_model NiN fit_generator color_preprocessing ImageDataGenerator compile print add_argument epochs fit asarray load_model print astype shape preprocess append range predict print shape concatenate print astype shape append range print astype shape append range Conv2D Lambda Sequential add UpSampling2D Lambda Sequential add Conv2D Sequential InstanceNormalization add ConvLayer Activation Input ResidualBlock range Sequential InstanceNormalization add ConvLayer Activation print astype shape append listdir range len | # DFST This is the repository for DFST paper *Deep Feature Space Trojan Attack of Neural Networks by Controlled Detoxification*.<br> See https://arxiv.org/abs/2012.11212.<br> ## Dependences Python3.6, tensorflow=1.13.1, keras=2.2.4, numpy, pickle, PIL.<br> ## How to use this repository Note that currently we only provide codes on **VGG** and **CIFAR-10** and the attack target label is **0**.<br> ### Prepare dataset Create some folders: `./dataset`, `./model`, `./weights`.<br> <br> | 717 |
Megvii-Nanjing/ML_GCN | ['word embeddings', 'multi label classification'] | ['Multi-Label Image Recognition with Graph Convolutional Networks'] | util.py models.py engine.py demo_coco_gcn.py coco.py demo_voc2007_gcn.py voc.py COCO2014 categoty_to_idx download_coco2014 main_coco main_voc2007 GraphConvolution gcn_resnet101 GCNResnet Warp AveragePrecisionMeter gen_A download_url MultiScaleCrop gen_adj find_images_classification read_object_labels_csv read_image_label write_object_labels_csv Voc2007Classification download_voc2007 read_object_labels categoty_to_idx values open Popen sorted list add call append dump format chdir set load join items print system makedirs len data workers evaluate lrp epoch_step learning COCO2014 SGD get_config_optim gcn_resnet101 GCNMultiLabelMAPEngine lr is_available parse_args MultiLabelSoftMarginLoss data workers evaluate lrp epoch_step learning SGD get_config_optim gcn_resnet101 GCNMultiLabelMAPEngine Voc2007Classification lr is_available parse_args MultiLabelSoftMarginLoss resnet101 urlretrieve load int identity sum open matmul t pow float diag print dict join items read_image_label dict zeros range len print close print join join basename format print getcwd chdir extractall download_url close path open urlparse makedirs | # ML-GCN.pytorch PyTorch implementation of [Multi-Label Image Recognition with Graph Convolutional Networks](https://arxiv.org/abs/1904.03582), CVPR 2019. ## Update 1. In our original conference paper, we report the baseline classification results using GAP for comparison, because GAP is the default choice for feature aggregation in ResNet series. In our experiments, we found that replacing GAP with GMP leads to improved performance, and thus adopt GMP with our GCN method -- we regard GMP as one part of our method. For clarification, we re-run the baselines and here report the corresponding results in the following table. | Method | COCO | NUS-WIDE |VOC2007 | |:---------:|:-------:|:-------:|:--------:| | Res-101 GAP | 77.3 | 56.9 | 91.7| | Res-101 GMP | 81.9 | 59.7 | 93.0 | | Ours | 83.0 | 62.5 | 94.0 | 2. We correct the typos in Eq. (8) as follows. | 718 |
MendelXu/ANN | ['semantic segmentation'] | ['Asymmetric Non-local Neural Networks for Semantic Segmentation'] | models/cls/nets/googlenet.py efficiency_statics/block/null_block.py utils/helpers/dc_helper.py models/gan/modules/lightcnn.py models/det/model_manager.py models/seg/utils/apnb.py extensions/ops/roi_align/functions/roi_align.py efficiency_statics/time_detail.py utils/tools/progressbar.py efficiency_statics/block/base_oc_block.py utils/tools/average_meter.py extensions/ops/roi_pool/gradcheck.py models/cls/loss/cls_modules.py models/cls/nets/vgg.py metrics/det/coco_evaluator.py datasets/test/loader/list_loader.py datasets/seg/preprocess/cityscapes/cityscapes_seg_generator.py extensions/ops/roi_pool/modules/roi_pool.py methods/tools/controller.py metrics/det/voc_evaluator.py models/det/loss/det_modules.py utils/visualizer/det_visualizer.py models/seg/model_manager.py methods/pose/conv_pose_machine.py datasets/gan/data_loader.py efficiency_statics/flops_count.py metrics/seg/cityscapes/helpers/annotation.py models/backbones/densenet/densenet_models.py efficiency_statics/modified_package/torch_stat/reporter.py models/backbones/densenet/densenet_backbone.py models/cls/nets/densenet.py models/cls/nets/resnet.py extensions/ops/sync_bn/src/gpu/setup.py models/det/layers/ssd_target_generator.py utils/parser/cls_parser.py demos/personseg/person_segmentor.py models/backbones/mobilenet/mobilenet_backbone.py methods/tools/blob_helper.py models/backbones/resnet/resnet_backbone.py models/backbones/vgg/vgg_backbone.py models/det/nets/darknet_yolov2.py extensions/ops/nms/setup.py methods/method_selector.py models/gan/modules/discriminator.py docs/sphinx/source/conf.py models/backbones/backbone_selector.py imagesite/src/config.py models/cls/nets/pnasnet.py imagesite/src/utils.py extensions/ops/roi_pool/setup.py models/det/nets/vgg512_ssd.py datasets/gan/loader/facegan_loader.py datasets/det/data_loader.py extensions/ops/roi_align/__init__.py models/pose/nets/open_pose_org.py efficiency_statics/modified_package/torch_stat/stat_tree.py metrics/seg/cityscapes/helpers/labels.py models/cls/nets/resnext.py datasets/tools/pil_aug_transforms.py datasets/seg/data_loader.py utils/visualizer/seg_visualizer.py methods/det/yolov3_test.py methods/pose/conv_pose_machine_test.py metrics/seg/cityscapes/helpers/labels_cityPersons.py models/seg/nets/annn.py demos/personseg/model/dmnetU.py datasets/ins/loader/default_loader.py datasets/cls/preprocess/cifar/cifar_cls_generator.py metrics/seg/cityscapes/setup.py datasets/tools/cv2_aug_transforms.py extensions/ops/dcn/__init__.py methods/tools/runner_helper.py utils/tools/configer.py models/det/nets/vgg300_ssd.py utils/helpers/mask_helper.py models/cls/nets/shufflenet.py models/backbones/mobilenet/mobilenet_models.py models/det/layers/fr_roi_sampler.py datasets/ins/data_loader.py methods/tools/trainer.py datasets/gan/loader/default_loader.py extensions/ops/dcn/functions/deform_conv.py models/det/nets/darknet_yolov3.py metrics/pose/coco_evaluator.py metrics/seg/ade20k_evaluator.py utils/helpers/det_helper.py utils/helpers/json_helper.py metrics/seg/cityscapes/evaluation/csHelpers.py datasets/det/preprocess/fashion/fashion_det_generator.py utils/helpers/image_helper.py models/seg/utils/afnb.py methods/seg/fcn_segmentor.py extensions/ops/sync_bn/src/__init__.py demos/personseg/model/ICNet_model.py models/pose/nets/cpn.py models/cls/nets/senet.py metrics/seg/cityscapes_evaluator.py models/pose/nets/open_pose.py efficiency_statics/block/apnb.py datasets/test/loader/default_loader.py extensions/ops/roi_pool/__init__.py extensions/ops/sync_bn/__init__.py metrics/seg/cityscapes/evaluation/instances2dict.py methods/gan/face_gan_test.py extensions/apis/cocoapi/PythonAPI/pycocotools/__init__.py datasets/seg/loader/default_loader.py methods/cls/image_classifier.py methods/pose/open_pose_test.py datasets/pose/preprocess/coco/coco_pose_generator.py datasets/seg/preprocess/ade20k/ade20k_seg_generator.py main.py datasets/seg/preprocess/pascal_context/pascal_context_generator.py models/cls/nets/dpn.py methods/det/single_shot_detector_test.py imagesite/image_site.py datasets/pose/utils/paf_generator.py models/gan/nets/cycle_gan.py efficiency_statics/totaltime_test.py models/backbones/vgg/vgg_models.py models/seg/utils/cbam.py utils/tools/history_plotter.py extensions/ops/dcn/functions/deform_pool.py models/cls/nets/mobilenetv2.py datasets/pose/loader/openpose_loader.py datasets/pose/loader/default_loader.py extensions/ops/sync_bn/syncbn.py extensions/ops/sync_bn/comm.py extensions/ops/sync_bn/src/cpu/setup.py models/det/layers/fr_roi_generator.py datasets/test/loader/json_loader.py extensions/apis/cocoapi/PythonAPI/pycocotools/coco.py extensions/tools/parallel/scatter_gather.py datasets/gan/loader/cyclegan_loader.py utils/parser/pose_parser.py models/backbones/squeezenet/squeezenet_backbone.py extensions/tools/parallel/_functions.py methods/cls/image_classifier_test.py datasets/pose/utils/heatmap_generator.py efficiency_statics/modified_package/torch_stat/__init__.py extensions/ops/roi_align/gradcheck.py extensions/tools/parallel/__init__.py datasets/gan/preprocess/face/casia_nir2vis.py metrics/cls/cls_running_score.py models/cls/nets/mobilenet.py utils/parser/seg_parser.py models/det/nets/fpn_rcnn.py models/det/layers/yolo_target_generator.py metrics/det/det_running_score.py models/cls/nets/shufflenetv2.py utils/parser/det_parser.py utils/visualizer/pose_visualizer.py models/pose/layers/proposal_layer.py datasets/det/preprocess/coco/coco_det_generator.py methods/det/faster_rcnn.py utils/tools/logger.py methods/det/single_shot_detector.py methods/gan/image_translator_test.py models/gan/modules/subnet_selector.py efficiency_statics/modified_package/torch_stat/compute_flops.py models/det/layers/ssd_detection_layer.py efficiency_statics/modified_package/torch_stat/__main__.py methods/seg/fcn_segmentor_test.py utils/visualizer/log_visualizer.py models/det/layers/yolo_detection_layer.py efficiency_statics/modified_package/torch_stat/compute_madd.py efficiency_statics/modified_package/torch_stat/statistics.py extensions/ops/sync_bn/functions.py models/gan/tools/image_pool.py models/pose/model_manager.py metrics/seg/coco_evaluator.py datasets/cls/preprocess/flower/flower_cls_generator.py datasets/tools/transforms.py models/backbones/resnet/resnet_models.py models/det/nets/tiny_yolov2.py extensions/ops/dcn/setup.py datasets/pose/data_loader.py extensions/tools/parallel/data_parallel.py methods/det/faster_rcnn_test.py utils/helpers/tensor_helper.py utils/parser/ins_parser.py models/tools/module_helper.py methods/det/yolov3.py models/pose/loss/pose_modules.py methods/pose/open_pose.py metrics/seg/seg_running_score.py models/cls/model_manager.py extensions/apis/cocoapi/PythonAPI/pycocotools/cocoeval.py utils/tools/timer.py extensions/apis/cocoapi/PythonAPI/setup.py extensions/ops/nms/__init__.py extensions/ops/dcn/modules/deform_pool.py models/det/nets/faster_rcnn.py metrics/seg/cityscapes/evaluation/instance.py datasets/det/preprocess/kitti/kitti_det_generator.py metrics/seg/pascal_context_evaluator.py datasets/det/preprocess/voc/voc_det_generator.py metrics/pose/pose_running_score.py extensions/apis/cocoapi/PythonAPI/pycocotools/mask.py models/seg/utils/gloreblock.py models/seg/utils/a2block.py models/backbones/squeezenet/squeezenet_models.py extensions/ops/nms/nms_wrapper.py datasets/det/loader/fasterrcnn_loader.py models/det/layers/rpn_detection_layer.py datasets/cls/preprocess/imagenet/imagenet_cls_generator.py models/backbones/darknet/darknet_backbone.py efficiency_statics/modified_package/torch_stat/compute_memory.py extensions/tools/parallel/distributed.py extensions/ops/roi_align/modules/roi_align.py extensions/ops/sync_bn/parallel.py datasets/cls/data_loader.py models/det/layers/ssd_priorbox_layer.py models/gan/nets/pix2pix.py models/det/layers/rpn_target_assigner.py models/pose/nets/cpm_net.py utils/helpers/file_helper.py models/cls/nets/preact_resnet.py utils/tools/requires_checker.py models/gan/modules/generator.py methods/gan/face_gan.py models/gan/loss/gan_modules.py utils/helpers/video_helper.py extensions/ops/dcn/modules/deform_conv.py methods/gan/image_translator.py utils/visualizer/tensor_visualizer.py extensions/ops/roi_pool/functions/roi_pool.py datasets/ins/preprocess/coco/coco_ins_generator.py models/pose/layers/subtree_generator.py models/det/layers/fr_priorbox_layer.py models/seg/loss/seg_modules.py datasets/det/loader/default_loader.py extensions/tools/parallel/data_container.py datasets/cls/loader/default_loader.py datasets/test/test_data_loader.py datasets/tools/collate.py models/backbones/darknet/darknet_models.py models/seg/utils/cgnl.py extensions/ops/roi_align/setup.py models/gan/model_manager.py efficiency_statics/modified_package/torch_stat/model_hook.py str2bool DataLoader DefaultLoader Cifar100ClsGenerator Cifar10ClsGenerator FlowerClsGenerator ImageNetClsGenerator DataLoader DefaultLoader FasterRCNNLoader CocoDetGenerator FashionDetGenerator KittiDetGenerator VocDetGenerator DataLoader CycleGANLoader DefaultLoader FaceGANLoader trans_gt DataLoader DefaultLoader CocoDetGenerator DataLoader DefaultLoader OpenPoseLoader CocoPoseGenerator HeatmapGenerator PafGenerator DataLoader DefaultLoader ADE20KSegGenerator CityscapesSegGenerator str2bool PascalContextGenerator TestDataLoader DefaultLoader JsonLoader ListLoader stack collate RandomPad RandomHue RandomBrightness RandomSaturation RandomRotate RandomDetCrop RandomHFlip RandomContrast RandomResizedCrop Resize RandomPerm RandomResize RandomCrop RandomFocusCrop CV2AugCompose Padding RandomPad RandomHue RandomBrightness RandomGaussBlur RandomSaturation RandomRotate RandomDetCrop RandomHFlip RandomContrast RandomHSV RandomResizedCrop Resize RandomPerm RandomResize RandomCrop RandomFocusCrop Padding PILAugCompose DeNormalize ToLabel ReLabel ToTensor Compose Normalize PersonSegmentor BottleneckDim3x3 DMNetBottleFSimple l_Net Bottleneck5x5 BottleneckDim5x5 DMNetBottleF Bottleneck3x3 make_grid Bottleneck psp_pooling init_s2 CFFConv2d TrimapNet save_image ICNet test_multi write2csv filter_end test test_multi filter_blank forward test_multi test PSPModule SelfAttentionBlock2D APNB _SelfAttentionBlock _SelfAttentionBlock SelfAttentionBlock2D BaseOC_Module MatMul NullBlock compute_Pool2d_flops compute_Upsample_flops compute_flops compute_BatchNorm2d_flops compute_adap_avgpool compute_matmul_flops compute_ReLU_flops compute_Linear_flops compute_Conv2d_flops compute_Conv2d_madd compute_BatchNorm2d_madd compute_ConvTranspose2d_madd compute_AvgPool2d_madd compute_Softmax_madd compute_ReLU_madd compute_Linear_madd compute_madd compute_MaxPool2d_madd compute_Bilinear_madd compute_BatchNorm2d_memory compute_Conv2d_memory compute_Linear_memory compute_memory compute_Pool2d_memory num_params compute_ReLU_memory compute_PReLU_memory ModelHook round_value report_format convert_leaf_modules_to_stat_tree get_parent_node ModelStat stat StatNode StatTree main arg COCO _isArrayLike Params COCOeval encode decode area toBbox DeformConvFunction ModulatedDeformConvFunction DeformRoIPoolingFunction ModulatedDeformConv DeformConv ModulatedDeformConvPack DeformRoIPoolingPack ModulatedDeformRoIPoolingPack DeformRoIPooling nms soft_nms customize_compiler_for_nvcc custom_build_ext RoIAlignFunction RoIAlign RoIPoolFunction RoIPool SyncMaster FutureResult SlavePipe _batchnormtrain batchnormtrain _sum_square sum_square CallbackContext allreduce AllReduce DataParallelModel execute_replication_callbacks Reduce patch_replication_callback BatchNorm3d SharedTensor _SyncBatchNorm BatchNorm1d BatchNorm2d DataContainer assert_tensor_type CallbackContext DataParallelModel _criterion_parallel_apply execute_replication_callbacks Reduce patch_replication_callback DataParallelCriterion MMDistributedDataParallel scatter_kwargs scatter scatter Scatter get_input_device synchronize_stream list_desdir list_params generate_command getNetworkIp is_img_file list_dir list_params list_jsons MethodSelector ImageClassifier ImageClassifierTest FasterRCNN FastRCNNTest SingleShotDetector SingleShotDetectorTest YOLOv3 YOLOv3Test FaceGAN FaceGANTest ImageTranslator ImageTranslatorTest ConvPoseMachine ConvPoseMachineTest OpenPose OpenPoseTest FCNSegmentor FCNSegmentorTest BlobHelper Controller RunnerHelper Trainer ClsRunningScore CocoEvaluator DetRunningScore VOCEvaluator CocoEvaluator PoseRunningScore ADE20KEvaluator CityscapesEvaluator CArgs generateMatrix getInstanceIouScoreForCategory getMatrixFieldValue printConfMatrix EvalPixel getIouScoreForCategory getIouScoreForLabel generateInstanceStats getPrior getPrediction writeJSONFile createResultDict getScoreAverage printCategoryScores printClassScores getInstanceIouScoreForLabel CocoEvaluator PASCALEvaluator SegRunningScore getCoreImageFileName printError getCsFileInfo getColorEntry getDirectory ensurePath writeDict2JSON colors Instance main instances2dict CsPoly CsObjectType CsBbox CsObject Annotation assureSingleInstanceName BackboneSelector NormalDarknetBackbone DilatedDarknetBackbone DarkNetBackbone DarkNet DarkNetModels BasicBlock DenseNetBackbone NormalDensenetBackbone DilatedDensenetBackbone DenseNet DenseNetModels _DenseLayer _DenseBlock _Transition MobileNetBackbone conv_1x1_bn MobileNetModels InvertedResidual conv_bn MobileNetV2Dilated8 MobileNetV2 DilatedResnetBackbone NormalResnetBackbone ResNetBackbone ResNet ResNetModels Bottleneck conv3x3 BasicBlock SqueezeNetBackbone SqueezeNet DilatedSqueezeNet SqueezeNetModels Fire AtrousFire VGGBackbone VGGModels make_layers VGG ModelManager ICCELoss ICCenterLoss ContrastiveCenterLoss get_densenet Bottleneck Transition DenseNet DPN Bottleneck test DPN92 get_densenet DPN26 GoogLeNet Inception Block MobileNet Block MobileNetV2 PNASNetB PNASNetA SepConv PNASNet CellB CellA PreActBlock PreActResNet50 PreActResNet PreActResNet18 PreActResNet152 PreActBottleneck PreActResNet101 PreActResNet34 ResNet BasicBlock Bottleneck get_resnet Block ResNeXt29_4x64d ResNeXt ResNeXt29_2x64d ResNeXt29_32x4d ResNeXt29_8x64d PreActBlock BasicBlock SENet18 SENet ShuffleNetG2 ShuffleNet Bottleneck ShuffleBlock ShuffleNetG3 ShuffleNetV2ResBlock ShuffleResNetV2 ShuffleNetV2 ConvBn ConvBnRelu ShuffleNetV2Block ShuffleUnit SELayer VGG ModelManager FRPriorBoxLayer FRROIGenerator FRROISampler RPNDetectionLayer RPNTargetAssigner SSDDetectionLayer SSDPriorBoxLayer SSDTargetGenerator YOLODetectionLayer YOLOTargetGenerator SSDFocalLoss FRLocLoss SSDMultiBoxLoss YOLOv3Loss FRLoss reorg _make_layers Conv2d_BatchNorm Conv2d DarkNetYolov2 DarkNetYolov3 Yolov3Head BBoxHead FasterRCNN NaiveRPN normal_init VGGModel NaiveRPN FpnRCNN normal_init RoIHead TinyYolov2 MaxPoolStride1 vgg_backbone SSDHead Vgg300SSD L2Norm VGGModel vgg_backbone SSDHead Vgg512SSD L2Norm VGGModel ModelManager CMD_Loss GANLoss CMD_K_Loss NLayerDiscriminator FCDiscriminator PixelDiscriminator ResNetGenerator ResnetBlock UNetGenerator UnetSkipConnectionBlock LightCNN_29v2 LightCNN_29 group LightCNN_9 mfm resblock SubNetSelector init_weights CycleGAN Pix2Pix ImagePool ModelManager ProposalLayer SubtreeGenerator EmbeddingLoss CapsuleLoss PartLoss OPMseLoss CPMNet CPN globalNet Bottleneck refineNet PoseModel OpenPose make_layers OpenPose get_open_pose_org ModelManager FSOhemCELoss FSFocalLoss FSAuxCELoss FSEmbedLoss FSAuxEncCELoss FSEncLoss FSCELoss FSAuxOhemCELoss asymmetric_non_local_network A2Block PSPModule AFNB SelfAttentionBlock2D _SelfAttentionBlock PSPModule SelfAttentionBlock2D APNB _SelfAttentionBlock CBAM_Module CGNL GloReModule ModuleHelper DCHelper DetHelper FileHelper ImageHelper JsonHelper MaskHelper TensorHelper VideoHelper Cache VideoReader ClsParser DetParser InsParser PoseParser SegParser AverageMeter Configer HistoryPlotter Logger track_progress init_pool ProgressBar track_parallel_progress requires_executable _check_executable requires_package check_prerequisites _check_py_package Timer DetVisualizer LogVisualizer PoseVisualizer SegVisualizer TensorVisualizer join list format write dumps close dict open isinstance return_dc default_collate samples_per_gpu append range len data list format error size min exit pad DataContainer resize randint keys range len int isinstance view Size fill_ size new min copy_ ceil max range cat len fromarray make_grid save cpu numpy NullBlock BaseOC_Module profile empty_cache APNB print append append float strip isinstance table len eval append range cuda filter_blank split write2csv extend test append synchronize model print isinstance print Conv2d Upsample BatchNorm2d AdaptiveAvgPool2d Linear kernel_size groups shape affine prod Tensor squeeze prod numel size kernel_size groups kernel_size groups kernel_size isinstance kernel_size isinstance Softmax isinstance AvgPool2d print MaxPool2d Conv2d Bilinear BatchNorm2d ConvTranspose2d Linear PReLU isinstance print Conv2d BatchNorm2d Linear numel num_params numel num_params numel size numel num_params numel numel sum list format fillna str parameter_quantity name duration Series inference_memory apply MAdd append DataFrame Flops join find_child_index split range len join get_parent_node list items add_child tolist len StatNode range split ModelStat show_report add_argument ArgumentParser arg exec_module tuple module_from_spec stat file spec_from_file_location shape ndarray isinstance new_tensor get_device Tensor numpy array is_cuda ndarray isinstance cpu_soft_nms Tensor numpy append _compile compiler_so list hasattr __data_parallel_replicate__ modules enumerate len replicate join isinstance _worker len start is_grad_enabled append range Lock tuple extend Tensor contiguous isinstance len isinstance Tensor range len Tensor isinstance rstrip split rstrip split socket setsockopt SOL_SOCKET AF_INET connect SO_BROADCAST SOCK_DGRAM join DATASET_ROOT list format is_img_file isdir PROJECT_ROOT STATIC_SERVICE dict splitext append listdir split join list PROJECT_ROOT dict append listdir join list PROJECT_ROOT dict append listdir split join predictionWalk format printError getCsFileInfo predictionPath realpath filter city sequenceNb dirname append frameNb walk append evalLabels max id append id sum normalized sum longlong ignoreInEval name sum longlong ignoreInEval sum longlong sum longlong evalLabels tolist getPrior getScoreAverage ensurePath writeDict2JSON exportFile dirname format nocol getMatrixFieldValue print name evalLabels getPrior getColorEntry range len str format nocol print name evalLabels getColorEntry quiet ignoreInEval bold format nocol all print getColorEntry quiet bold print str exit format basename printError CsFile split len getCsFileInfo dirname makedirs format toDict print Instance len unique abspath append array flush open append instances2dict Conv2d error format exit randn Variable print DPN92 net error format exit size permute view contiguous append Conv2d_BatchNorm MaxPool2d normal_ add_ zero_ get load list format items state_dict update dict load_state_dict info keys VGGModel apply format info list keys range len get make_layers range Conv2d update isinstance write ProgressBar func Iterable append len update join isinstance init_pool write ProgressBar close start imap_unordered imap Iterable append len import_module | # ANN This repository is for Asymmetric Non-local Neural Networks for Semantic Segmentation (to appear in ICCV 2019), by [Zhen Zhu](https://zzhu.vision), [Mengde Xu](https://github.com/MendelXu), [Song Bai](http://songbai.site), [Tengteng Huang](https://github.com/tengteng95) and [Xiang Bai](https://scholar.google.com/citations?hl=en&user=UeltiQ4AAAAJ). The source code is in preparing. We will release as soon as possible. ### citation If you find our paper useful in your research, please consider citing: @inproceedings{annn, author = {Zhen Zhu and Mengde Xu and Song Bai and | 719 |
MengzhangLI/STFGNN | ['traffic prediction'] | ['Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting'] | main_4n0_3layer_12T_res.py data/fastDTW_adj_gen.py utils_4n0_3layer_12T_res.py data/stsgcn-dtw.py data/Temporal_Graph_gen.py data/STSGCN_data_preprocession2.py data/STSGCN_data_preprocession.py models/stsgcn_4n_res.py training construct_model generate_data mask_np construct_adj masked_mse_np masked_mae_np construct_adj_fusion generate_from_train_val_test get_adjacency_matrix generate_seq masked_mape_np generate_from_data normalize compute_dtw gen_data normalize compute_dtw df_fill load_pickle df_pad normalize compute_dtw gen_data output_layer huber_loss weighted_loss stsgcn sthgcn_layer_individual position_embedding stsgcl stsgcm gcn_operation update time format print forward_backward extend dict masked_mae_np reset update_metric zip append label range asnumpy enumerate var format Activation FullyConnected print Variable BlockGrad shape construct_adj_fusion Constant get_adjacency_matrix stsgcn array read_csv zeros zeros range len zeros range len mean transformer generate_seq std mean transformer generate_seq std load generate_from_data generate_from_train_val_test keys isnan mask_np abs mask_np reshape mean std norm reshape min normalize range max zeros nan ffill replace bfill mean int replace var format broadcast_add dot FullyConnected split append range gcn_operation len tanh Convolution slice transpose reshape concat sigmoid expand_dims swapaxes append position_embedding range stsgcm reshape Activation FullyConnected swapaxes MakeLoss abs square where arange broadcast_mul huber_loss expand_dims flip var format concat output_layer huber_loss append stsgcl range enumerate | # AAAI-2021 Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting <p align="center"> <img width="800" height="400" src=./documents/stfgnn.png> </p> ## Requirements This is the MXNet implementation of STFGNN in the paper: [Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting, AAAI 2021] (https://arxiv.org/abs/2012.09641). This framework is built based on framework of [STSGCN(AAAI-20)](https://github.com/Davidham3/STSGCN). Being familiar with its pipeline is strongly recommended. - python 3 - see `requirements.txt` ## Data Preparation | 720 |
MhLiao/MaskTextSpotter | ['text spotting', 'semantic segmentation', 'scene text recognition'] | ['Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes'] | maskrcnn_benchmark/modeling/rpn/loss.py maskrcnn_benchmark/data/datasets/scut.py maskrcnn_benchmark/utils/logging.py maskrcnn_benchmark/layers/roi_align.py maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_seq_predictors.py maskrcnn_benchmark/data/datasets/total_text.py maskrcnn_benchmark/utils/model_zoo.py evaluation/icdar2015/e2e/weighted_editdistance.py maskrcnn_benchmark/solver/__init__.py maskrcnn_benchmark/layers/nms.py maskrcnn_benchmark/engine/text_inference.py maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py maskrcnn_benchmark/utils/imports.py maskrcnn_benchmark/data/samplers/distributed.py maskrcnn_benchmark/utils/env.py tests/checkpoint.py maskrcnn_benchmark/data/samplers/iteration_based_batch_sampler.py maskrcnn_benchmark/layers/_utils.py maskrcnn_benchmark/modeling/detector/__init__.py evaluation/icdar2015/e2e/script.py maskrcnn_benchmark/utils/metric_logger.py maskrcnn_benchmark/modeling/backbone/__init__.py maskrcnn_benchmark/modeling/rpn/__init__.py maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_feature_extractors.py evaluation/icdar2015/e2e/rrc_evaluation_funcs.py maskrcnn_benchmark/structures/bounding_box.py setup.py maskrcnn_benchmark/data/samplers/grouped_batch_sampler.py maskrcnn_benchmark/utils/comm.py maskrcnn_benchmark/layers/__init__.py maskrcnn_benchmark/engine/trainer.py maskrcnn_benchmark/structures/segmentation_mask.py maskrcnn_benchmark/data/datasets/coco.py maskrcnn_benchmark/modeling/poolers.py maskrcnn_benchmark/layers/misc.py tools/demo.py maskrcnn_benchmark/data/transforms/__init__.py maskrcnn_benchmark/modeling/rpn/rpn.py maskrcnn_benchmark/utils/chars.py maskrcnn_benchmark/layers/smooth_l1_loss.py maskrcnn_benchmark/utils/miscellaneous.py tools/train_net.py maskrcnn_benchmark/data/build.py maskrcnn_benchmark/modeling/backbone/resnet.py maskrcnn_benchmark/modeling/roi_heads/mask_head/inference.py maskrcnn_benchmark/modeling/roi_heads/mask_head/loss.py maskrcnn_benchmark/utils/collect_env.py maskrcnn_benchmark/layers/batch_norm.py maskrcnn_benchmark/utils/checkpoint.py maskrcnn_benchmark/data/datasets/concat_dataset.py maskrcnn_benchmark/structures/image_list.py maskrcnn_benchmark/data/collate_batch.py maskrcnn_benchmark/modeling/roi_heads/mask_head/mask_head.py maskrcnn_benchmark/modeling/utils.py evaluation/icdar2015/e2e/prepare_results.py maskrcnn_benchmark/modeling/roi_heads/roi_heads.py maskrcnn_benchmark/modeling/detector/generalized_rcnn.py maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_predictors.py maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_predictors.py maskrcnn_benchmark/data/samplers/__init__.py maskrcnn_benchmark/config/paths_catalog.py maskrcnn_benchmark/modeling/box_coder.py tools/test_net.py maskrcnn_benchmark/data/transforms/transforms.py maskrcnn_benchmark/modeling/backbone/backbone.py maskrcnn_benchmark/modeling/roi_heads/box_head/box_head.py maskrcnn_benchmark/structures/boxlist_ops.py maskrcnn_benchmark/data/datasets/icdar.py maskrcnn_benchmark/config/defaults.py tests/test_data_samplers.py maskrcnn_benchmark/modeling/rpn/anchor_generator.py maskrcnn_benchmark/data/datasets/__init__.py maskrcnn_benchmark/data/__init__.py maskrcnn_benchmark/solver/lr_scheduler.py maskrcnn_benchmark/utils/model_serialization.py maskrcnn_benchmark/modeling/backbone/fpn.py maskrcnn_benchmark/utils/c2_model_loading.py maskrcnn_benchmark/data/datasets/synthtext.py maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py maskrcnn_benchmark/modeling/roi_heads/box_head/loss.py maskrcnn_benchmark/config/__init__.py maskrcnn_benchmark/modeling/rpn/inference.py maskrcnn_benchmark/solver/build.py maskrcnn_benchmark/modeling/matcher.py maskrcnn_benchmark/modeling/detector/detectors.py maskrcnn_benchmark/layers/roi_pool.py maskrcnn_benchmark/engine/inference.py maskrcnn_benchmark/data/datasets/list_dataset.py maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_feature_extractors.py maskrcnn_benchmark/data/transforms/build.py get_extensions nms list_from_str find_match_word polygon_iou packing polygon_from_list test_single prepare_results_for_evaluation validate_point_inside_bounds load_zip_file_keys validate_clockwise_points validate_lines_in_file decode_utf8 print_help main_validation get_tl_line_values load_zip_file get_tl_line_values_from_file_contents validate_tl_line main_evaluation evaluation_imports evaluate_method default_evaluation_params validate_data ed_delect_cost ed_insert_cost char2num ed_replace_cost weighted_edit_distance add_gt_path DatasetCatalog ModelCatalog make_data_sampler _quantize make_data_loader make_batch_data_sampler build_dataset _compute_aspect_ratios BatchCollator COCODataset MixDataset ConcatDataset IcdarDataset ListDataset ScutDataset SynthtextDataset TotaltextDataset DistributedSampler GroupedBatchSampler IterationBasedBatchSampler build_transforms RandomHue RandomSaturation RandomRotate Compose ToTensor RandomContrast Resize RandomCrop _boxlist2quads Normalize RandomHorizontalFlip RandomGamma _quad2minrect RandomBrightness _rotate_polygons COCOResults check_expected_results prepare_for_coco_segmentation evaluate_predictions_on_coco _accumulate_predictions_from_multiple_gpus compute_on_dataset inference evaluate_box_proposals prepare_for_coco_detection process_char_mask format_output _accumulate_predictions_from_multiple_gpus mask2polygon inference compute_on_dataset creat_color_map get_tight_rect visualization prepare_results_for_evaluation do_train reduce_loss_dict FrozenBatchNorm2d _NewEmptyTensorOp interpolate ConvTranspose2d Conv2d ROIAlign _ROIAlign _ROIPool ROIPool smooth_l1_loss _load_C_extensions BalancedPositiveNegativeSampler BoxCoder Matcher LevelMapper Pooler cat build_backbone build_resnet_fpn_backbone build_resnet_backbone LastLevelMaxPool FPN ResNetHead _make_stage register_stage_spec ResNet StemWithFixedBatchNorm register_stem_module _register_generic BottleneckWithFixedBatchNorm register_transformation_module build_detection_model GeneralizedRCNN CombinedROIHeads build_roi_heads build_roi_box_head ROIBoxHead PostProcessor make_roi_box_post_processor make_roi_box_loss_evaluator FastRCNNLossComputation make_roi_box_feature_extractor FPN2MLPFeatureExtractor ResNet50Conv5ROIFeatureExtractor FPNPredictor make_roi_box_predictor FastRCNNPredictor paste_mask_in_image expand_boxes Masker make_roi_mask_post_processor CharMaskPostProcessor MaskPostProcessorCOCOFormat expand_masks MaskPostProcessor CharMaskRCNNLossComputation make_roi_mask_loss_evaluator MaskRCNNLossComputation project_masks_on_boxes keep_only_positive_boxes ROIMaskHead build_roi_mask_head project_char_masks_on_boxes MaskRCNNFPNFeatureExtractor make_roi_mask_feature_extractor MaskRCNNC4Predictor SeqCharMaskRCNNC4Predictor make_roi_mask_predictor CharMaskRCNNC4Predictor make_roi_seq_predictor reduce_mul Attn SequencePredictor BahdanauAttnDecoderRNN check_all_done AnchorGenerator generate_anchors _scale_enum _whctrs make_anchor_generator _ratio_enum _generate_anchors BufferList _mkanchors make_rpn_postprocessor RPNPostProcessor RPNLossComputation make_rpn_loss_evaluator build_rpn RPNModule RPNHead make_optimizer make_lr_scheduler WarmupMultiStepLR BoxList cat_boxlist boxlist_iou boxlist_nms remove_small_boxes _cat ImageList to_image_list Mask SegmentationMask shrink_poly Polygons shrink_rect SegmentationCharMask is_poly_inbox CharPolygons _rename_basic_resnet_weights load_c2_format _rename_weights_for_resnet _load_c2_pickled_weights _rename_fpn_weights seg2text num2char char2num get_tight_rect getstr_grid DetectronCheckpointer Checkpointer collect_env_info get_pil_version scatter_gather _decode synchronize get_world_size get_rank _encode is_main_process setup_environment setup_custom_environment import_file Logger setup_logger SmoothedValue MetricLogger mkdir strip_prefix_if_present load_state_dict align_and_update_state_dicts cache_url TestCheckpointer SubsetSampler TestGroupedBatchSampler TestIterationBasedBatchSampler main TextDemo main main train test glob join dirname abspath split reshape convex_hull concatenate reshape area float convex_hull sorted polygon_iou polygon_from_list range len join listdir system mkdir join str nms print strip readlines len tqdm dict upper packing mkdir isfile append range open len upper dict eval append abs weighted_edit_distance test_single mkdir write exit group match namelist append ZipFile group match namelist append ZipFile decode BOM_UTF8 replace startswith encode validate_tl_line decode_utf8 replace split get_tl_line_values validate_point_inside_bounds int replace validate_clockwise_points group match float replace argsort append get_tl_line_values split update default_evaluation_params_fn validate_data_fn writestr items write dumps close dict print_help evaluate_method_fn ZipFile makedirs update default_evaluation_params_fn validate_data_fn print exit dict load_zip_file validate_lines_in_file compute_ap area include_in_dictionary_transcription decode_utf8 append polygon_from_points range import_module get_intersection_over_union load_zip_file empty get_tl_line_values_from_file_contents float items namedtuple int8 rectangle_to_polygon Rectangle get_intersection zeros len ed_delect_cost min ed_insert_cost range ed_replace_cost len char2num char2num char2num print lower exit ord get RATIOS getattr MixDataset append factory SequentialSampler RandomSampler list sorted map copy get_img_info append float range len BatchSampler IterationBasedBatchSampler GroupedBatchSampler _quantize _compute_aspect_ratios format import_file make_data_sampler getLogger IMS_PER_BATCH PATHS_CATALOG MAX_ITER get_world_size NUM_WORKERS BatchCollator DataLoader warning make_batch_data_sampler SIZE_DIVISIBILITY build_transforms build_dataset DatasetCatalog append TO_BGR255 RANDOM_CROP_PROB MIN_SIZE_TEST Compose MIN_SIZE_TRAIN Normalize MAX_SIZE_TRAIN MAX_SIZE_TEST zeros array enumerate Polygon print rotate _boxlist2quads append update tqdm eval device to enumerate convert tolist extend resize enumerate decode Masker tolist extend masker tqdm resize get_field enumerate arange zeros_like resize max boxlist_iou append sum loadAnns range cat getAnnIds mean float enumerate reshape sort convert min zeros as_tensor len str evaluate COCOeval summarize accumulate loadRes update scatter_gather list sorted getLogger warning keys error format info getLogger COCOResults check_expected_results getLogger prepare_for_coco_segmentation save device dataset str _accumulate_predictions_from_multiple_gpus format synchronize timedelta info item evaluate_box_proposals prepare_for_coco_detection join time items compute_on_dataset len sorted list approxPolyDP threshold where RETR_LIST vstack resize CHAIN_APPROX_NONE list transpose morphologyEx map MORPH_RECT minAreaRect findContours astype dilate get_tight_rect GaussianBlur uint8 getStructuringElement print boxPoints reshape float32 THRESH_BINARY erode convexHull contourArea MORPH_CLOSE arcLength array len join str write close save enumerate open list map copy append getstr_grid range int ceil power range append polygon Draw enumerate format_output save resize open list squeeze tolist map append sum process_char_mask visualization size mask2polygon creat_color_map float enumerate join items numpy array len load isfile MIN_SIZE_TEST prepare_results_for_evaluation get_world_size getLogger model clip_grad_norm_ zero_grad save str MetricLogger to sum USE_ADAM is_main_process update format timedelta info item reduce_loss_dict enumerate items time backward parameters global_avg train step scalar_summary len _output_size tuple abs where join glob extend dirname abspath OrderedDict ResNet Sequential FPN ResNet Sequential OrderedDict OUT_CHANNELS RES2_OUT_CHANNELS endswith append transformation_module range _register_generic _register_generic _register_generic append MASK_ON CombinedROIHeads BoxCoder DETECTIONS_PER_IMG PostProcessor BBOX_REG_WEIGHTS USE_FPN NMS SCORE_THRESH POSITIVE_FRACTION FG_IOU_THRESHOLD BATCH_SIZE_PER_IMAGE BoxCoder BalancedPositiveNegativeSampler BBOX_REG_WEIGHTS BG_IOU_THRESHOLD Matcher FastRCNNLossComputation zeros_like new_zeros fromarray bool expand_masks min astype from_numpy array int32 resize zeros to numpy max CHAR_MASK_ON CharMaskPostProcessor MaskPostProcessor zip convert device resize append to crop FG_IOU_THRESHOLD CHAR_MASK_ON MaskRCNNLossComputation BG_IOU_THRESHOLD Matcher CharMaskRCNNLossComputation RESOLUTION get_field squeeze append zip convert device resize append to crop FG_IOU_THRESHOLD BG_IOU_THRESHOLD Matcher AnchorGenerator STRADDLE_THRESH ANCHOR_SIZES ANCHOR_STRIDE USE_FPN ASPECT_RATIOS vstack _ratio_enum array hstack sqrt _whctrs round _mkanchors _whctrs _mkanchors NMS_THRESH FPN_POST_NMS_TOP_N_TRAIN POST_NMS_TOP_N_TRAIN RPNPostProcessor POST_NMS_TOP_N_TEST MIN_SIZE PRE_NMS_TOP_N_TRAIN FPN_POST_NMS_TOP_N_TEST PRE_NMS_TOP_N_TEST POSITIVE_FRACTION FG_IOU_THRESHOLD RPNLossComputation BATCH_SIZE_PER_IMAGE BalancedPositiveNegativeSampler BG_IOU_THRESHOLD Matcher WEIGHT_DECAY_BIAS Adam SGD named_parameters parameters BASE_LR USE_ADAM BIAS_LR_FACTOR WEIGHT_DECAY convert _box_nms get_field bbox mode squeeze unbind bbox clamp min area max len add_field size set BoxList _cat fields mode int list isinstance tuple copy_ zero_ zip ceil Tensor norm min range arctan2 min max min max enumerate max _rename_basic_resnet_weights sorted format getLogger OrderedDict from_numpy info keys _rename_fpn_weights CONV_BODY _load_c2_pickled_weights replace _rename_weights_for_resnet uint8 argmax astype seg2text approxPolyDP threshold num2char boundingRect argmax RETR_TREE list sorted map shape append sum range findContours astype mean float uint8 drawContours CHAIN_APPROX_SIMPLE reshape THRESH_BINARY arcLength len get_pretty_env_info _send_and_wait get_world_size get_rank to from_buffer dumps numel to load join remove format _decode synchronize mkdtemp get_world_size save get_rank append rmdir empty _encode range broadcast get setup_custom_environment setup_environment import_file spec_from_file_location exec_module module_from_spec setFormatter join getLogger addHandler StreamHandler Formatter DEBUG setLevel FileHandler makedirs max list sorted format view getLogger tuple tolist shape info keys enumerate len OrderedDict items sorted keys strip_prefix_if_present align_and_update_state_dicts state_dict join basename format replace synchronize write search group getenv path _download_url_to_file expanduser urlparse makedirs merge_from_file run_on_opencv_image imwrite config_file TextDemo image_path imread visualization visu_path ArgumentParser make_data_loader opts OUTPUT_DIR collect_env_info set_device MASK_ON get_rank freeze parse_args to inference TEST DEVICE format build_detection_model init_process_group synchronize setup_logger WEIGHT merge_from_list mkdir info zip enumerate load join add_argument DetectronCheckpointer local_rank len DEVICE make_optimizer load update build_detection_model CHECKPOINT_PERIOD make_data_loader WEIGHT DistributedDataParallel DetectronCheckpointer Logger RESUME device do_train to OUTPUT_DIR make_lr_scheduler join zip synchronize MASK_ON inference mkdir make_data_loader empty_cache OUTPUT_DIR module TEST enumerate len test distributed train | # MaskTextSpotter This is the code of "Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes" (TPAMI version). It is an extension of the ECCV version while sharing the same title. For more details, please refer to our [TPAMI paper](https://ieeexplore.ieee.org/document/8812908). This repo is inherited from [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) and follows the same license. ## ToDo List - [x] Release code - [x] Document for Installation - [x] Trained models - [x] Document for testing - [x] Document for training | 721 |
MichaelChirico/portland | ['density estimation', 'gaussian processes'] | ['Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ "Real-Time Crime Forecasting Challenge"'] | smint_getout.py smint_template/make_experiment_file.py bo_scripts_in_r/bo.py bo_scripts_in_r/bo_ar.py smint_template/main_arws_constr.py ar_ws.py | Forecasting Crime in Portland * [Contest Guidelines](http://www.nij.gov/funding/Pages/fy16-crime-forecasting-challenge.aspx) # Creating our analysis data sets Required packages: `rvest`, `data.table`, `foreign`, `zoo`, `sp`, `rgeos`, `maptools`, `rgdal` 1. Run `data_download.R` to scrape the NIJ website for links to all .zip files and download them into `./data`. 2. Run `crimes_to_csv.R` to convert these shapefiles' .dbf databases into our analysis .csv files: `crimes_all.csv`, `crimes_bur.csv`, `crimes_str.csv` and `crimes_veh.csv`. 3. Make sure you have the provided `Portland_Police_Districts.shp` shapefile of police districts in Portland in `./data`, then run `extract_portland_boundary_to_csv.R` to create a text file containing all x-y coordinates of the (500-ft-buffered) vertices of the outer boundary of Portland into `./data/portland_coords.csv`. # Converting chosen parameters into output shapefiles | 722 |
MichaelKonobeev/adashift | ['stochastic optimization'] | ['Adam: A Method for Stochastic Optimization'] | author_code_base/nmt/nmt/utils/common_test_utils.py wgan/model.py author_code_base/nmt/nmt/scripts/bleu.py author_code_base/nmt/nmt/utils/evaluation_utils.py author_code_base/WGAN/common/msssim.py wgan/lipschitz.py wgan/logger.py author_code_base/densenet_tiny_imagenet/data_providers/utils.py author_code_base/WGAN/common/logger.py author_code_base/WGAN/common/score.py author_code_base/nmt/nmt/attention_model.py author_code_base/WGAN/common/data_loader.py author_code_base/densenet_tiny_imagenet/data_providers/downloader.py author_code_base/Densenet-Tensorflow/Cifar10/optimizer.py author_code_base/nmt/nmt/utils/evaluation_utils_test.py adashift/adashift_check.py author_code_base/optimizer_all.py author_code_base/MulitiLayer_MNIST/optimizer_shift.py author_code_base/nmt/nmt/gnmt_model.py author_code_base/nmt/nmt/utils/standard_hparams_utils.py author_code_base/densenet_tiny_imagenet/models/optimizer_all.py author_code_base/MulitiLayer_MNIST/common/GPUtil.py author_code_base/nmt/nmt/utils/nmt_utils.py author_code_base/WGAN/common/utils.py author_code_base/WGAN/wgan_gp-master/optimizer_shift.py author_code_base/densenet_tiny_imagenet/run_dense_net.py author_code_base/nmt/nmt/nmt_test.py author_code_base/nmt/nmt/model_test.py author_code_base/MulitiLayer_MNIST/common/utils.py author_code_base/nmt/nmt/model_helper.py author_code_base/nmt/nmt/inference_test.py author_code_base/Densenet-Tensorflow/Cifar10/cifar10.py author_code_base/__init__.py author_code_base/ResNet-CIFAR10/common/GPUtil.py author_code_base/nmt/nmt/nmt.py author_code_base/nmt/nmt/scripts/rouge.py author_code_base/nmt/nmt/utils/misc_utils.py author_code_base/WGAN/common/optimizer.py author_code_base/WGAN/wgan_gp-master/gan_realdata4.py author_code_base/nmt/nmt/model.py author_code_base/Densenet-Tensorflow/Cifar10/Densenet_Cifar10.py author_code_base/TF_MNIST_Logistic/mnist_logistic.py author_code_base/MulitiLayer_MNIST/neural_network_raw.py author_code_base/ResNet-CIFAR10/main.py author_code_base/densenet_tiny_imagenet/data_providers/base_provider.py author_code_base/ResNet-CIFAR10/ResNet.py author_code_base/nmt/nmt/utils/iterator_utils.py wgan/progress.py wgan/inception_score.py train_wgan.py author_code_base/nmt/nmt/utils/vocab_utils_test.py author_code_base/MulitiLayer_MNIST/draw_TF_result.py author_code_base/ResNet-CIFAR10/optimizer.py author_code_base/WGAN/common/ops.py author_code_base/nmt/nmt/inference.py author_code_base/ResNet-CIFAR10/utils.py author_code_base/nmt/nmt/train.py author_code_base/ResNet-CIFAR10/ops.py author_code_base/nmt/nmt/utils/vocab_utils.py author_code_base/WGAN/common/GPUtil.py author_code_base/WGAN/plot_log.py author_code_base/nmt/nmt/utils/iterator_utils_test.py author_code_base/correlation_coefficient/optimizer_all.py author_code_base/densenet_tiny_imagenet/models/dense_net.py author_code_base/nmt/nmt/optimizer_all.py author_code_base/nmt/nmt/utils/misc_utils_test.py author_code_base/ResNet-CIFAR10/common/utils.py author_code_base/Densenet-Tensorflow/MNIST/optimizer.py adashift/optimizers.py author_code_base/correlation_coefficient/main.py author_code_base/densenet_tiny_imagenet/data_providers/svhn.py author_code_base/Densenet-Tensorflow/MNIST/Densenet_MNIST.py author_code_base/densenet_tiny_imagenet/data_providers/cifar.py loop_data_loader calculate_disc_gradients calculate_gen_gradients get_device main parse_args compute_inception_score AdaShift AMSGrad Adam AdaShift Mom Grad AdamMax correlation_arr correlation AMSGrad Adam AdaShift Mom Grad AdamMax _random_flip_leftright prepare_data download_data color_preprocessing load_data load_data_one data_augmentation _random_crop unpickle Batch_Normalization Drop_out DenseNet Global_Average_Pooling Max_Pooling Concatenation Relu Evaluate Average_pooling find_next_time conv_layer Linear AdamShiftMoving AMSGrad AdamShiftN Adam AdamShift Mom Grad AdamMax Batch_Normalization Drop_out DenseNet Global_Average_Pooling Max_Pooling Concatenation Relu Average_pooling conv_layer Linear AdamShiftMoving AdamShiftN Adam AddSign AdamShift PowerSign Mom Grad AdamMax get_train_params_by_name DataProvider ImagesDataSet DataSet augment_all_images Cifar10AugmentedDataProvider Cifar10DataProvider Cifar100AugmentedDataProvider CifarDataProvider CifarDataSet TinyDataProvider Cifar100DataProvider plot_images_labels augment_image report_download_progress download_data_url SVHNDataProvider plot_images_labels SVHNDataSet get_data_provider_by_name DenseNet AMSGrad Adam AdaShift Mom Grad AdamMax color_map_fun get_label smooth find_next_time neural_net AMSGrad Adam AdaShift Mom Grad AdamMax safeFloatCast getAvailability getFirstAvailable showUtilization getGPUs getAvailable GPU save_model save_images rampup random_augment_image_nhwc ini_model data_gen_epoch data_gen_random labeled_data_gen_epoch clip_truncated_normal load_model center_crop batch_resize shuffle_datas str_flags random_augment_image_nchw imread copydir prod removedirs get_name imresize labeled_data_gen_random shuffle_datas_and_labels mean allocate_gpu softmax merge remove collect sigmoid clear_duplicated_layers random_augment_all_images std makedirs create_attention_mechanism AttentionModel _create_attention_images_summary GNMTModel gnmt_residual_fn GNMTAttentionMultiCell single_worker_inference start_sess_and_load_model _decode_inference_indices multi_worker_inference get_model_creator load_data inference InferenceTest TrainOutputTuple Model EvalOutputTuple BaseModel InferOutputTuple _create_or_load_embed gradient_clip create_train_model load_model create_emb_for_encoder_and_decoder get_device_str create_infer_model compute_perplexity _get_embed_device create_or_load_model TrainModel _cell_list get_initializer create_rnn_cell avg_checkpoints _create_pretrained_emb_from_txt InferModel EvalModel print_variables_in_ckpt create_eval_model ExtraArgs _single_cell ModelTest create_or_load_hparams run_main ensure_compatible_hparams _add_argument add_arguments extend_hparams main create_hparams NMTTest _update_flags AMSGrad Adam AdaShift Mom Grad AdamMax run_internal_and_external_eval get_model_creator _sample_decode init_stats run_internal_eval update_stats print_step_info run_full_eval before_train _format_results process_stats _internal_eval _external_eval add_info_summaries run_avg_external_eval run_external_eval run_sample_decode get_best_results train _get_ngrams compute_bleu _len_lcs _get_ngrams rouge rouge_l_summary_level rouge_n _recon_lcs _split_into_words _lcs rouge_l_sentence_level _union_lcs _f_p_r_lcs _get_word_ngrams create_test_hparams create_test_iterator _clean evaluate _word_accuracy _accuracy _rouge _bleu _moses_bleu EvaluationUtilsTest get_iterator BatchedInput get_infer_iterator IteratorUtilsTest debug_tensor check_tensorflow_version format_spm_text format_text print_time print_hparams get_config_proto print_out load_hparams format_bpe_text add_summary safe_exp maybe_parse_standard_hparams save_hparams MiscUtilsTest get_translation decode_and_evaluate create_standard_hparams load_vocab load_embed_txt check_vocab tokens_to_bytes _string_to_bytes create_vocab_tables VocabUtilsTest check_args find_next_time parse_args global_avg_pooling relu batch_norm fully_conneted avg_pooling flatten classification_loss conv bottle_resblock get_residual_layer resblock AdamShiftMoving AMSGrad AdamShiftN Adam AdamShift Mom Grad AdamMax ResNet get_annotations_map _random_flip_leftright load_tiny check_folder load_mnist load_fashion load_cifar10 show_all_variables data_augmentation _random_crop str2bool safeFloatCast getAvailability getFirstAvailable showUtilization getGPUs getAvailable GPU save_model save_images rampup random_augment_image_nhwc ini_model data_gen_epoch data_gen_random labeled_data_gen_epoch clip_truncated_normal load_model center_crop batch_resize shuffle_datas str_flags random_augment_image_nchw imread copydir prod removedirs get_name imresize labeled_data_gen_random shuffle_datas_and_labels mean allocate_gpu softmax merge remove collect sigmoid clear_duplicated_layers random_augment_all_images std makedirs map_label smooth smooth get_name load_cifar10 load_toy_data_weight load_mnist load_toy_data load_toy_data_cov safeFloatCast getAvailability getFirstAvailable showUtilization getGPUs getAvailable GPU Logger main _SSIMForMultiScale MultiScaleSSIM _FSpecialGauss set_enable_bias initializer set_enable_sn random avgpool set_default_gain spectral_normed_weight normalized_orthogonal_initializer identity lnoise conv2d set_enable_snk normalize activate channel_concat batch_norm dropout set_data_format image_nn_double_size cond_batch_norm set_scale_weight_after_init set_enable_wn norm linear minibatch_feature maxpool noise deconv2d set_init_type set_init_weight_stddev AdamShiftN Adam AdamSpaceShift Mom Grad AdamTimeShift AdamMax InceptionScore MSSSIM AMScore PreTrainedInception PreTrainedDenseNet FID save_model save_images rampup random_augment_image_nhwc ini_model data_gen_epoch data_gen_random labeled_data_gen_epoch clip_truncated_normal load_model center_crop batch_resize shuffle_datas str_flags random_augment_image_nchw imread copydir prod removedirs get_name imresize labeled_data_gen_random shuffle_datas_and_labels mean allocate_gpu softmax merge remove collect sigmoid clear_duplicated_layers random_augment_all_images std makedirs get_score gen_with_generator discriminator_dcgan param_count generator_mlp uniform_gen gen_images_with_noise load_dataset find_next_time log_netstate sort_images gen_images generator_block path2 discriminator_block path generator_dcgan sample_z discriminator_mlp AdamShiftMoving AMSGrad AdamShiftN Adam AdamShift Mom Grad AdamMax IgnoreLabelDataset inception_score GradientPenalty WeightClipping LipschitzConstraint Logger GeneratorBlock Generator avg_pool2d Discriminator DiscriminatorBlock bar data generator calculate_loss_penalty backward Variable type_as zero_grad mean parameters tensor prepare_discriminator generator backward Variable zero_grad mean parameters tensor cuda device add_argument ArgumentParser batch_size calculate_disc_gradients DataLoader Logger save cuda loop_data_loader WeightClipping Adam epochs unimproved calculate_gen_gradients bar parse_args next range compute_inception_score Compose gen_iters CIFAR10 AdaShift item optimizer join disc_iters GradientPenalty print makedirs parameters step add_scalar mean reshape sum sqrt reshape sum sqrt tile urlretrieve print endswith extractall close open print unpickle reshape transpose load_data_one array append permutation print len download_data load_data unpickle shape pad append randint range len bool fliplr range getrandbits len mean astype std _random_crop _random_flip_leftright range Summary run shape zeros getrandbits randint zeros range augment_image shape set_title text set_axis_off imshow zip float format write flush join urlretrieve print endswith extractall makedirs argmax print exit size mean shape zeros range startswith matmul add float decode int GPU safeFloatCast linesep split append empty range Popen len getAvailability sort array getGPUs str getAvailable print sleep range id attrTransform str name int64 getattr encode memoryFree memoryUtil serial format memoryTotal uuid memoryUsed display_mode enumerate load isinstance print maximum getGPUs display_active len append str global_variables_initializer run save makedirs print model_checkpoint_path restore get_checkpoint_state removedirs exists copytree exists rmtree exists sort list keys minimum cast float32 clip concatenate zeros range zeros enumerate imresize int min round zeros enumerate imresize concatenate reshape ndarray isinstance shape str permutation permutation choice len len range shuffle concatenate choice len get_state shuffle set_state range len transpose randint shape zeros getrandbits transpose randint shape zeros getrandbits zeros range random_augment_image_nchw shape exp max LuongAttention BahdanauAttention expand_dims stack image transpose assert_same_structure map_structure time print_out print_time GNMTModel Model AttentionModel Session single_worker_inference start_sess_and_load_model multi_worker_inference close get_model_creator create_infer_model inference_indices load_data int min load_data len src_vocab_file tgt_vocab_file Graph src_vocab_file tgt_vocab_file Graph Graph tgt_vocab_file src_vocab_file constant slice load_embed_txt load_vocab print_out array _create_pretrained_emb_from_txt fixed_size_partitioner BasicLSTMCell print_out DeviceWrapper DropoutWrapper NASCell LayerNormBasicLSTMCell GRUCell ResidualWrapper __name__ append print_out single_cell_fn range _cell_list clip_by_global_norm scalar global_norm append sorted NewCheckpointReader print_out keys get_variable_to_shape_map time tables_initializer print_out run join dtype get_checkpoint_state load_checkpoint get_tensor print_out MakeDirs list_variables zeros time load_model latest_checkpoint tables_initializer print_out eval global_variables_initializer run safe_exp eval time print_time register add_argument add_hparam setattr hasattr join embed_prefix vocab_prefix src share_vocab residual tgt Exists metrics _add_argument num_decoder_layers check_vocab print_out getattr MakeDirs out_dir num_encoder_layers list setattr hasattr num_layers print_out getattr add_hparam maybe_parse_standard_hparams keys values metrics ensure_compatible_hparams extend_hparams getattr load_hparams print_hparams maybe_parse_standard_hparams save_hparams ckpt subword_option seed inference_input_file repr print_out hparams_path dirname list_devices inference_list latest_checkpoint inference_output_file MakeDirs random_seed inference_ref_file create_or_load_hparams join jobid train_fn inference_fn evaluate metrics num_workers out_dir inference train create_hparams run_main join get_temp_dir batch_size_placeholder src_placeholder iterator _sample_decode _internal_eval iterator iterator load_data _external_eval infer_batch_size run_external_eval avg_ckpts num_keep_ckpts avg_checkpoints run_avg_external_eval run_external_eval metrics _format_results run_internal_eval test_prefix run_sample_decode batch_size print_out add_summary safe_exp print_out time initializer batch_size epoch_step init_stats print_out run num_train_steps avg_ckpts get_model_creator save Session create_train_model steps_per_stats print_time GFile get_config_proto init_stats run_internal_eval update_stats create_infer_model print_out add_summary getattr print_step_info run_full_eval log_device_placement before_train close FileWriter process_stats steps_per_external_eval add_info_summaries run_avg_external_eval join time run_external_eval metrics graph run_sample_decode get_best_results load_data out_dir create_eval_model append metrics add_summary compute_perplexity initializer run decode initializer len print_out add_summary randint get_translation run join setattr initializer metrics print_out getattr save out_dir add_summary decode_and_evaluate save_hparams run tuple range Counter len _get_ngrams exp Counter zip float sum range len add set _split_into_words _lcs dict max range tuple _lcs intersection _get_word_ngrams len _len_lcs _split_into_words len _recon_lcs set _split_into_words union len _split_into_words len mean list map zip create_standard_hparams constant index_table_from_tensor from_tensor_slices index_to_string_table_from_tensor _accuracy _rouge _word_accuracy _bleu lstrip strip sub _clean zip append compute_bleu split rouge check_output search group call float constant make_initializable_iterator EOS_CHAR_ID map get_next lookup cast int32 batching_func constant make_initializable_iterator EOS_CHAR_ID prefetch shard group_by_window shuffle skip map apply get_next lookup filter cast int32 zip batching_func exp print flush decode isinstance print write encode flush sorted list print_out keys values join print_out Exists print_out join print_out name Summary ConfigProto encode append isinstance len time print_out evaluate format_spm_text format_text tolist format_bpe_text encode as_list uint8 decode_raw to_int32 concat reshape info fill join basename Exists load_vocab print_out len index_table_from_file dict print allocate_gpu reduce_mean softmax_cross_entropy_with_logits_v2 float32 reduce_mean cast argmax equal makedirs trainable_variables analyze_vars seed shuffle to_categorical load_data seed to_categorical shuffle load_data expand_dims seed to_categorical shuffle load_data expand_dims get_annotations_map str join seed print transpose to_categorical astype float32 shuffle zeros listdir array open read split splitlines open print startswith split startswith get download_cifar10 concatenate reshape astype int32 append unpickle download_mnist join asarray concatenate astype int32 fromfile zeros float open multivariate_normal eye append zeros array range multivariate_normal eye append zeros array range multivariate_normal eye append zeros array range exp convolve float64 reshape min astype mean shape abs _FSpecialGauss fftconvolve ones size prod _SSIMForMultiScale append array range MultiScaleSSIM placeholder string expand_dims decode_png as_list list get_deconv_dim dropout noise as_list reshape ones as_list while_loop reshape name assign append get_variable iBaseNumFilterD iBaseNumFilterD iBaseNumFilterD iBaseNumFilterG run uniform get_shape str name len shape range log run randn rand iDimsZ bSphereZ bUniformZ concatenate iBatchSize append range run concatenate iBatchSize len append sample_z range run get_preds transpose log mean append abs max range median norm asarray int reshape argmin append zeros abs max range len norm concatenate min append zeros range len exp entropy append FloatTensor print Variable mean DataLoader eval bar is_available zeros type get_pred range enumerate len print int format | # Adashift Reproducing of the [AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods](https://openreview.net/forum?id=HkgTkhRcKQ) as a part of ICLR Reproducibility Challange 2019. See our [report](https://github.com/MichaelKonobeev/adashift/tree/master/report/report.pdf). ## Experiments **Synthetic Experiment**   **Logistic Regression on MNIST**   **W-GAN**   | 723 |
Michaelwolf95/Hierarchical-ML-agents | ['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 | 724 |
MichalBusta/E2E-MLT | ['optical character recognition'] | ['E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text'] | ocr_gen.py train.py demo.py nms/__init__.py ocr_test_utils.py train_ocr.py eval.py ocr_utils.py models.py data_util.py net_utils.py data_gen.py generator get_images load_annoataion get_batch load_gt_annoataion point_dist_to_line generate_rbox2 random_perspective cut_image random_rotation generate_rbox draw_box_points GeneratorEnqueuer resize_image intersect area process_splits load_gt draw_detections evaluate_image union load_detections conv_dw_plain BasicBlockIn dice_loss iou_loss conv_bn CReLU conv_dw_res_in conv_dw_res conv_dw BasicBlockSepIn CReLU_IN ModelMLTRCTW ModelResNetSep2 BasicBlockSep conv_dw_in load_net np_to_variable generator get_images get_batch test ocr_image print_seq_ext intersect area process_boxes main union main do_nms get_boxes append format dirname sqrt line circle warpAffine cos copy pi getRotationMatrix2D uniform sin uniform warpPerspective float32 getPerspectiveTransform int uniform sum max cross norm blur fillPoly resize max ones waitKey imshow draw_box_points append asarray copy sqrt zip enumerate int min atan2 int32 argwhere zeros array len fillPoly resize ones waitKey imshow draw_box_points append asarray copy sqrt zip enumerate int point_dist_to_line atan2 int32 argwhere zeros array len load_annoataion arange random_rotation resize fromarray basename copyMakeBorder generate_rbox2 cut_image shape uniform generate_rbox dirname append imread RandomizedBlur format asarray replace Compose astype shuffle int get_images invert uint8 load_gt_annoataion print float32 random_perspective transform BORDER_CONSTANT array generator get is_running start sleep GeneratorEnqueuer int resize asarray reshape copy draw_box_points range min max min max intersect reshape area union eval lower boundingRect draw_box_points zeros float array range len max waitKey copy imshow draw_box_points float array range append len sum view gt min type cuda load items list print copy_ load_state_dict blur strip abs max len rotate getRotationMatrix2D range warpAffine ROTATE_90_COUNTERCLOCKWISE IMREAD_GRAYSCALE float reshape min extend randint split strip where object distance resize round max argmax DataFrame open sorted basename name title savetxt dirname permute append expand_dims imread range forward_ocr format asarray to_html close print_seq_ext eval lower swapaxes float forward_features enumerate int print write split zeros train numpy array len append range affine_grid cos argmax cuda max exp view FloatTensor Size shape sin forward_ocr asarray grid_sample size print_seq_ext mean sqrt swapaxes type forward_features int reshape atan2 unravel_index numpy affine_grid area cos unsqueeze boundingRect IntTensor round max cuda argmax view FloatTensor Size squeeze waitKey imshow sin draw_box_points append union range forward_ocr asarray format intersect grid_sample size shuffle print_seq_ext sqrt lower startswith swapaxes float type forward_features int print Variable boxPoints min atan2 argwhere randint numpy array circle len model zero_grad process_boxes numpy save IntTensor resize base_lr cuda exists ctc_loss squeeze waitKey Adam imshow permute next range forward_ocr format get_batch asarray save_path debug timeit size np_to_variable swapaxes ModelResNetSep2 net forward_features load_net int join backward print max_iters parameters argwhere train step CTCLoss loss argmax print_seq_ext savez nms_impl array fill zeros swapaxes do_nms | # E2E-MLT E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text code base for: https://arxiv.org/abs/1801.09919 ``` @@inproceedings{buvsta2018e2e, title={E2E-MLT-an unconstrained end-to-end method for multi-language scene text}, author={Bu{\v{s}}ta, Michal and Patel, Yash and Matas, Jiri}, booktitle={Asian Conference on Computer Vision}, pages={127--143}, year={2018}, | 725 |
MichiganCOG/Gaze-Attention | ['action recognition', 'egocentric activity recognition'] | ['Integrating Human Gaze into Attention for Egocentric Activity Recognition'] | pytorch_i3d.py dataset.py utils.py main.py model.py transform igazeDataset trainingSampler load_model test print_args load_weights adjust_lr main train load_weights_and_set_opt I3D_IGA_attn I3D_IGA_base I3D_IGA_gaze InceptionI3d MaxPool3dSamePadding InceptionModule Unit3D _pointwise_loss compute_cross_entropy compute_gradients_gaze get_accuracy sample_gumbel make_hard_decision div permute datasplit DataLoader DataParallel device datapath load_weights_and_set_opt seed load_model parse_args to manual_seed_all stride test manual_seed is_available trange crop join print print_args isfile igazeDataset train weight mode I3D_IGA_attn I3D_IGA_base I3D_IGA_gaze load join items print size copy_ isfile state_dict replace SGD load_weights append weight print datasplit wd lr b ngpu weight test_sparse mode param_groups clip_grad_norm_ zero_grad compute_gradients_gaze save kl_div tensor max exp view model_base sum range state_dict eps LongTensor log_softmax compute_cross_entropy mean zip max_pool2d model_gaze crop enumerate join time iters backward print makedirs adjust_lr zeros step make_hard_decision time print confusion_matrix eval get_accuracy append sum range len rand shape sample_gumbel view scatter_ model_attn lambd | # Gaze-Attention Integrating Human Gaze into Attention for Egocentric Activity Recognition (WACV 2021)\ [**paper**](https://openaccess.thecvf.com/content/WACV2021/html/Min_Integrating_Human_Gaze_Into_Attention_for_Egocentric_Activity_Recognition_WACV_2021_paper.html) | [**presentation**](https://youtu.be/k-VUi54GjXQ) ## Overview It is well known that human gaze carries significant information about visual attention. In this work, we introduce an effective probabilistic approach to integrate human gaze into spatiotemporal attention for egocentric activity recognition. Specifically, we propose to reformulate the discrete training objective so that it can be optimized using an unbiased gradient estimator. It is empirically shown that our gaze-combined attention mechanism leads to a significant improvement of activity recognition performance on egocentric videos by providing additional cues across space and time. | Method | Backbone network | Acc(%) | Acc\*(%) | |:----------------------|:------------------:|:-------:|:-------:| | [Li et al.](https://openaccess.thecvf.com/content_ECCV_2018/papers/Yin_Li_In_the_Eye_ECCV_2018_paper.pdf) | I3D | 53.30 | - | | [Sudhakaran et al.](http://bmvc2018.org/contents/papers/0756.pdf) | ResNet34+LSTM | - | 60.76 | | [LSTA](https://openaccess.thecvf.com/content_CVPR_2019/papers/Sudhakaran_LSTA_Long_Short-Term_Attention_for_Egocentric_Action_Recognition_CVPR_2019_paper.pdf) | ResNet34+LSTM | - | 61.86 | | 726 |
Microsoft/constrained-graph-variational-autoencoder | ['time series'] | ['Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders'] | data_augmentation.py data/get_qm9.py GGNN_core.py CGVAE.py evaluate.py utils.py data/get_zinc.py DenseGGNNChemModel genereate_incremental_adj construct_incremental_graph generate_mask update_one_step generate_label ChemModel is_planar get_idx_of_largest_frag check_uniqueness add_edge_mat count_atoms check_sascorer check_validity count_edge_type generate_empty_adj_matrix node_sequence_to_dense check_node_prob glorot_init check_edge_type_prob edge_type_labels_to_dense generate_std_normal novelty_metric dataset_info sssr_metric check_mean check_variance onehot to_graph need_kekulize overlapped_edge_features_to_dense check_cyclic distance_to_others_dense edge_type_masks_to_dense edge_masks_to_dense bfs_distance dump get_overlapped_edge_feature remove_extra_nodes MLP Graph get_initial_valence make_dir sample_node_symbol get_graph_length add_atoms check_edge_prob check_logp shape_count incre_adj_mat_to_dense check_qed load ThreadedIterator check_planar select_best visualize_mol graph_to_adj_mat check_adjacent_sparse edge_labels_to_dense preprocess get_validation_file_names read_xyz train_valid_split train_valid_split int min RemoveBond GetSymmSSSR AddBond append range len append range check_adjacent_sparse append deepcopy deepcopy get_overlapped_edge_feature generate_mask append bfs_distance generate_label MolFromSmiles sorted defaultdict genereate_incremental_adj int get_initial_valence popleft shuffle add_atoms update_one_step AddBond deque append float RWMol len print exit zeros add_edge_mat print zip items defaultdict set add append range len print zip print print print shape append MolFromSmiles qed MolFromSmiles append argmin len print mkdir arange choice append range enumerate len int RemoveBond GetSymmSSSR AddBond append range len popleft add set deque append AddAtom int SetFormalCharge strip Atom print MolToFile Compute2DCoords RemoveAtom get_idx_of_largest_frag range GetMolFrags len append defaultdict to_graph len GetBonds to_graph defaultdict to_graph append argmax len MolFromSmiles print GetTotalValence onehot GetFormalCharge GetAtoms index need_kekulize GetSymbol Kekulize GetBonds RemoveStereochemistry append len set len MolFromSmiles list to_graph GetSymmSSSR append range len sqrt isTree defaultdict Graph to_graph append len calculateScore MolFromSmiles append append MolFromSmiles MolLogP GetSymmSSSR MolFromSmiles range len sorted append zeros items append zeros append zeros append append zeros append zeros float len append append float len print join read_xyz print glob append get_validation_file_names enumerate dump print to_graph append enumerate strip float | # Constrained Graph Variational Autoencoders for Molecule Design
This repository contains our implementation of [Constrained Graph Variational Autoencoders for Molecule Design](https://arxiv.org/abs/1805.09076) (CGVAE).
```
@article{liu2018constrained,
title={Constrained Graph Variational Autoencoders for Molecule Design},
author={Liu, Qi and Allamanis, Miltiadis and Brockschmidt, Marc and Gaunt, Alexander L.},
journal={The Thirty-second Conference on Neural Information Processing Systems},
year={2018}
| 727 |
MikeWise2718/ml-agents-mod | ['unity'] | ['Unity: A General Platform for Intelligent Agents'] | ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_input_pb2.py ml-agents/mlagents/trainers/components/reward_signals/curiosity/model.py ml-agents-envs/mlagents/envs/communicator_objects/agent_action_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/unity_to_external_pb2.py gym-unity/gym_unity/envs/__init__.py ml-agents/mlagents/trainers/learn.py ml-agents-envs/mlagents/envs/communicator_objects/custom_observation_pb2.py ml-agents/mlagents/trainers/meta_curriculum.py ml-agents/mlagents/trainers/tests/test_barracuda_converter.py ml-agents/mlagents/trainers/ppo/models.py utils/validate_meta_files.py ml-agents-envs1/mlagents/envs/communicator_objects/custom_reset_parameters_pb2.py ml-agents/mlagents/trainers/lgger.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-envs/mlagents/envs/communicator.py ml-agents-envs/mlagents/envs/communicator_objects/custom_reset_parameters_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/engine_configuration_pb2.py ml-agents/mlagents/trainers/tests/test_ppo.py ml-agents-envs/mlagents/envs/tests/test_rpc_communicator.py ml-agents/mlagents/trainers/components/reward_signals/__init__.py ml-agents-envs/mlagents/envs/communicator_objects/agent_info_pb2.py ml-agents-envs/setup.py ml-agents/mlagents/trainers/tests/mock_brain.py ml-agents-envs1/mlagents/envs/communicator_objects/unity_rl_initialization_input_pb2.py ml-agents-envs/mlagents/envs/action_info.py ml-agents-envs/mlagents/envs/rpc_communicator.py ml-agents/mlagents/trainers/tests/test_bcmodule.py ml-agents/mlagents/trainers/tests/test_trainer_controller.py ml-agents/mlagents/trainers/components/reward_signals/reward_signal_factory.py ml-agents/setup.py ml-agents/mlagents/trainers/barracuda.py ml-agents-envs/mlagents/envs/tests/test_envs.py ml-agents/mlagents/trainers/sac/policy.py ml-agents-envs/mlagents/envs/env_manager.py ml-agents/mlagents/trainers/ppo/trainer.py ml-agents-envs1/mlagents/envs/communicator_objects/engine_configuration_pb2.py ml-agents-envs/mlagents/envs/tests/test_timers.py ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_output_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_initialization_output_pb2.py ml-agents-envs1/mlagents/envs/communicator_objects/header_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/unity_input_pb2.py ml-agents-envs1/mlagents/envs/communicator_objects/unity_input_pb2.py ml-agents-envs1/mlagents/envs/communicator_objects/command_pb2.py ml-agents/mlagents/trainers/tests/test_meta_curriculum.py ml-agents-envs1/mlagents/envs/communicator_objects/unity_rl_input_pb2.py ml-agents/mlagents/trainers/components/reward_signals/curiosity/signal.py va2_logger.py ml-agents-envs/mlagents/envs/subprocess_env_manager.py ml-agents/mlagents/trainers/bc/trainer.py ml-agents-envs/mlagents/envs/tests/test_brain.py ml-agents/mlagents/trainers/curriculum.py ml-agents/mlagents/trainers/tests/test_policy.py ml-agents/mlagents/trainers/ppo/policy.py ml-agents/mlagents/trainers/tests/test_learn.py ml-agents-envs1/mlagents/envs/communicator_objects/demonstration_meta_pb2.py ml-agents/mlagents/trainers/tests/test_demo_loader.py utils/validate_versions.py ml-agents/mlagents/trainers/models.py ml-agents-envs1/mlagents/envs/communicator_objects/brain_parameters_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/brain_parameters_pb2.py ml-agents/mlagents/trainers/tests/test_simple_rl.py ml-agents-envs/mlagents/envs/policy.py ml-agents/mlagents/trainers/exception.py gym-unity/gym_unity/tests/test_gym.py hello.py ml-agents/mlagents/trainers/buffer.py ml-agents/mlagents/trainers/tensorflow_to_barracuda.py ml-agents-envs/mlagents/envs/communicator_objects/unity_to_external_pb2_grpc.py ml-agents-envs/mlagents/envs/mock_communicator.py pg-pong.py ml-agents/mlagents/trainers/tests/test_rl_trainer.py ml-agents-envs1/mlagents/envs/communicator_objects/agent_action_pb2.py ml-agents-envs/mlagents/envs/timers.py gym-unity/setup.py ml-agents-envs/mlagents/envs/communicator_objects/unity_message_pb2.py ml-agents-envs/mlagents/envs/environment.py ml-agents-envs/mlagents/envs/communicator_objects/environment_statistics_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/custom_action_pb2.py ml-agents/mlagents/trainers/bc/policy.py ml-agents-envs/mlagents/envs/simple_env_manager.py ml-agents-envs/mlagents/envs/communicator_objects/environment_parameters_pb2.py ml-agents-envs1/mlagents/envs/communicator_objects/custom_observation_pb2.py ml-agents-envs1/mlagents/envs/communicator_objects/custom_action_pb2.py ml-agents-envs/mlagents/envs/base_unity_environment.py ml-agents-envs1/mlagents/envs/communicator_objects/__init__.py ml-agents-envs1/mlagents/envs/communicator_objects/agent_info_pb2.py ml-agents/mlagents/trainers/trainer_util.py ml-agents/mlagents/trainers/tests/test_trainer_util.py ml-agents-envs/mlagents/envs/communicator_objects/unity_output_pb2.py ml-agents/mlagents/trainers/components/reward_signals/extrinsic/signal.py ml-agents/mlagents/trainers/sac/trainer.py ml-agents-envs1/mlagents/envs/communicator_objects/environment_parameters_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/compressed_observation_pb2.py ml-agents-envs/mlagents/envs/sampler_class.py ml-agents/mlagents/trainers/tests/test_sac.py ml-agents-envs1/mlagents/envs/communicator_objects/unity_rl_initialization_output_pb2.py ml-agents-envs/mlagents/envs/exception.py ml-agents/mlagents/trainers/sac/models.py ml-agents-envs/mlagents/envs/communicator_objects/command_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/space_type_pb2.py ml-agents-envs1/mlagents/envs/communicator_objects/compressed_observation_pb2.py ml-agents/mlagents/trainers/components/reward_signals/gail/model.py ml-agents-envs/mlagents/envs/communicator_objects/header_pb2.py ml-agents/mlagents/trainers/rl_trainer.py ml-agents/mlagents/trainers/tests/test_reward_signals.py ml-agents-envs/mlagents/envs/brain.py ml-agents/mlagents/trainers/components/reward_signals/gail/signal.py ml-agents/mlagents/trainers/ppo/multi_gpu_policy.py ml-agents/mlagents/trainers/tests/test_multigpu.py ml-agents-envs/mlagents/envs/communicator_objects/__init__.py ml-agents/mlagents/trainers/demo_loader.py ml-agents/mlagents/trainers/components/bc/module.py ml-agents/mlagents/trainers/tests/test_trainer_metrics.py ml-agents-envs/mlagents/envs/tests/test_sampler_class.py ml-agents/mlagents/trainers/tests/test_buffer.py ml-agents/mlagents/trainers/trainer.py ml-agents-envs1/mlagents/envs/communicator_objects/space_type_pb2.py ml-agents-envs/mlagents/envs/tests/test_subprocess_env_manager.py ml-agents/mlagents/trainers/bc/models.py ml-agents/mlagents/trainers/bc/offline_trainer.py ml-agents/mlagents/trainers/tf_policy.py ml-agents/mlagents/trainers/tests/test_bc.py ml-agents-envs/mlagents/envs/communicator_objects/demonstration_meta_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_initialization_input_pb2.py ml-agents/mlagents/trainers/trainer_metrics.py policy_backward sigmoid policy_forward prepro discount_rewards getlgg createlgg lgger VerifyVersionCommand UnityGymException ActionFlattener UnityEnv create_mock_vector_braininfo test_gym_wrapper test_multi_agent test_branched_flatten setup_mock_unityenvironment test_gym_wrapper_visual create_mock_brainparams VerifyVersionCommand BarracudaWriter fuse print_known_operations compress Build sort lstm write fuse_batchnorm_weights trim mean gru Model summary Struct parse_args to_json rnn BufferException Buffer Curriculum make_demo_buffer load_demonstration demo_to_buffer CurriculumError MetaCurriculumError TrainerError CurriculumLoadingError CurriculumConfigError create_environment_factory create_sampler_manager CommandLineOptions parse_command_line imports run_training prepare_for_docker_run try_create_meta_curriculum main err error debug get_level warm set_level verbose deb Lgger info allways get_level_str MetaCurriculum EncoderType LearningModel LearningRateSchedule AllRewardsOutput RLTrainer 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 TFPolicy UnityPolicyException UnityTrainerException Trainer TrainerController TrainerMetrics TrainerFactory initialize_trainer load_config _load_config BehavioralCloningModel OfflineBCTrainer BCPolicy BCTrainer BCModel BCModule create_reward_signal RewardSignal CuriosityModel CuriosityRewardSignal ExtrinsicRewardSignal GAILModel GAILRewardSignal PPOModel get_devices MultiGpuPPOPolicy PPOPolicy PPOTrainer get_gae discount_rewards SACPolicyNetwork SACTargetNetwork SACNetwork SACModel SACPolicy SACTrainer create_mock_pushblock_brain create_buffer simulate_rollout create_mock_3dball_brain make_brain_parameters create_mock_banana_brain setup_mock_unityenvironment create_mock_braininfo create_mock_brainparams setup_mock_env_and_brains test_barracuda_converter test_bc_export bc_dummy_config test_bc_trainer_step test_bc_trainer_add_proc_experiences test_cc_bc_model test_dc_bc_model test_visual_cc_bc_model test_bc_trainer_end_episode test_bc_policy_evaluate dummy_config test_visual_dc_bc_model create_bc_trainer sac_dummy_config test_bcmodule_rnn_update test_bcmodule_update ppo_dummy_config create_policy_with_bc_mock test_bcmodule_dc_visual_update test_bcmodule_defaults test_bcmodule_rnn_dc_update test_buffer_sample construct_fake_buffer assert_array fakerandint test_buffer test_buffer_truncate test_curriculum_load_invalid_json location default_reset_parameters test_init_curriculum_bad_curriculum_raises_error test_curriculum_load_missing_file test_init_curriculum_happy_path test_increment_lesson test_curriculum_load_good test_get_config test_load_demo test_load_demo_dir basic_options test_docker_target_path test_run_training test_env_args test_commandline_args 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_create_model dummy_config test_average_gradients test_update basic_mock_brain test_take_action_returns_action_info_when_available basic_params test_take_action_returns_nones_on_missing_values test_take_action_returns_empty_with_no_agents test_trainer_increment_step test_trainer_update_policy test_rl_functions test_ppo_model_dc_vector_rnn test_ppo_model_cc_vector_rnn test_add_rewards_output test_ppo_policy_evaluate test_ppo_model_cc_visual dummy_config test_ppo_model_dc_vector test_ppo_model_dc_visual test_ppo_get_value_estimates test_ppo_model_cc_vector 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 create_policy_mock test_curiosity_dc curiosity_dummy_config test_curiosity_visual test_curiosity_rnn gail_dummy_config create_mock_all_brain_info create_rl_trainer test_clear_update_buffer dummy_config test_rl_trainer create_mock_brain create_mock_policy test_sac_update_reward_signals create_sac_policy_mock test_sac_model_dc_visual test_sac_cc_policy test_sac_visual_policy test_sac_model_cc_vector_rnn test_sac_model_dc_vector test_sac_model_cc_vector dummy_config test_sac_model_dc_vector_rnn test_sac_model_cc_visual test_sac_rnn_policy test_sac_save_load_buffer test_sac_dc_policy test_simple_sac clamp test_simple_ppo Simple1DEnvironment _check_environment_trains test_initialization_seed test_take_step_if_not_training test_start_learning_trains_until_max_steps_then_saves basic_trainer_controller test_take_step_adds_experiences_to_trainer_and_trains dummy_config trainer_controller_with_take_step_mocks trainer_controller_with_start_learning_mocks test_start_learning_trains_forever_if_no_train_model TestTrainerMetrics test_initialize_ppo_trainer dummy_offline_bc_config_with_override test_load_config_invalid_yaml dummy_offline_bc_config 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 VerifyVersionCommand ActionInfo BaseUnityEnvironment EnvStats safe_concat_lists safe_concat_np_ndarray CameraResolution BrainParameters BrainInfo Communicator UnityEnvironment EnvManager EnvironmentStep SamplerException UnityWorkerInUseException UnityException UnityCommunicationException UnityTimeOutException UnityEnvironmentException UnityActionException MockCommunicator Policy RpcCommunicator UnityToExternalServicerImplementation MultiRangeUniformSampler UniformSampler SamplerFactory SamplerManager GaussianSampler Sampler SimpleEnvManager worker EnvironmentResponse UnityEnvWorker StepResponse SubprocessEnvManager EnvironmentCommand TimerNode hierarchical_timer get_timer_root get_timer_tree reset_timers set_gauge timed GaugeNode TimerStack UnityToExternalProtoServicer add_UnityToExternalProtoServicer_to_server UnityToExternalProtoStub test_from_agent_proto_nan test_from_agent_proto_inf test_from_agent_proto_fast_path 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_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 mock_env_factory SubprocessEnvManagerTest MockEnvWorker test_timers decorated_func main extract_version_string check_versions list zeros_like size reversed range dot sigmoid dot T ravel outer lgger lgger create_mock_vector_braininfo sample UnityEnv setup_mock_unityenvironment step create_mock_brainparams create_mock_vector_braininfo UnityEnv setup_mock_unityenvironment step create_mock_brainparams setup_mock_unityenvironment create_mock_vector_braininfo create_mock_brainparams UnityEnv create_mock_vector_braininfo sample UnityEnv setup_mock_unityenvironment step create_mock_brainparams Mock list Mock array range join isdir print replaceFilenameExtension add_argument exit verbose source_file ArgumentParser target_file sqrt topologicalSort list hasattr layers addEdge Graph print inputs set len list hasattr layers print filter match trim_model compile data layers print tensors float16 replace layers dumps layers isinstance print tensors inputs zip 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 Buffer reset_local_buffers number_visual_observations append_update_buffer append range enumerate make_demo_buffer load_demonstration join read suffix isdir endswith BrainParametersProto from_agent_proto DemonstrationMetaProto ParseFromString AgentInfoProto isfile append from_proto listdir _DecodeVarint32 ModuleType list items isinstance parse_args add_argument ArgumentParser start_learning fast_simulation create_sampler_manager sampler_file_path put reset_parameters lesson load_config keep_checkpoints str docker_target_name load_model multi_gpu TrainerController save_freq trainer_config_path run_id num_envs format create_environment_factory no_graphics try_create_meta_curriculum TrainerFactory curriculum_folder base_port env_args SubprocessEnvManager train_model env_path pop SamplerManager load_config set_all_curriculums_to_lesson_num MetaCurriculum reset_parameters chmod format basename isdir glob copyfile copytree prepare_for_docker_run replace getLogger getEffectiveLevel setLevel seed Process append range format parse_command_line debug run_training start Queue info join print cpu randint num_runs warn verbosity 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 list 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 format SACTrainer OfflineBCTrainer PPOTrainer copy brain_name get check_config rcls list_local_devices append discount_rewards CameraResolution Mock list ones array range brain_name pop create_buffer brain sequence_length append range vector_action_space_size Buffer ones number_visual_observations append_update_buffer shape append sum range enumerate setup_mock_unityenvironment mock_env create_mock_braininfo create_mock_brainparams create_mock_brainparams create_mock_brainparams create_mock_brainparams join remove _get_candidate_names convert _get_default_tempdir dirname abspath isfile next create_bc_trainer export_model create_mock_pushblock_brain Mock BCTrainer simulate_rollout mock_env dirname abspath setup_mock_unityenvironment policy create_mock_braininfo create_mock_3dball_brain update_policy create_bc_trainer increment_step agents process_experiences step create_bc_trainer add_experiences end_episode agents process_experiences step create_bc_trainer add_experiences BCPolicy evaluate close reset MockCommunicator reset_default_graph UnityEnvironment reset_default_graph reset_default_graph reset_default_graph reset_default_graph mock_env dirname abspath setup_mock_unityenvironment create_mock_braininfo create_policy_with_bc_mock close ppo_dummy_config create_mock_3dball_brain update items list close create_policy_with_bc_mock create_mock_3dball_brain update items list close create_policy_with_bc_mock create_mock_3dball_brain update items list close create_mock_banana_brain create_policy_with_bc_mock update items list close create_mock_banana_brain create_policy_with_bc_mock flatten list range len append range Buffer get_batch construct_fake_buffer assert_array append_update_buffer make_mini_batch reset_agent array sample_mini_batch construct_fake_buffer append_update_buffer construct_fake_buffer truncate_update_buffer append_update_buffer Curriculum Curriculum Curriculum dumps StringIO StringIO make_demo_buffer load_demonstration dirname abspath make_demo_buffer load_demonstration dirname abspath MagicMock basic_options MagicMock parse_command_line parse_command_line 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 MultiGpuPPOPolicy create_mock_brainparams reset_default_graph create_mock_brainparams update Mock reset_default_graph MultiGpuPPOPolicy create_mock_brainparams MagicMock TFPolicy basic_mock_brain basic_params BrainInfo get_action MagicMock TFPolicy basic_mock_brain basic_params BrainInfo get_action MagicMock TFPolicy basic_mock_brain ActionInfo basic_params BrainInfo get_action evaluate close reset MockCommunicator PPOPolicy reset_default_graph UnityEnvironment get_value_estimates items list close reset MockCommunicator PPOPolicy reset_default_graph UnityEnvironment reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph assert_array_almost_equal array discount_rewards Mock increment_step BrainParameters assert_called_with PPOTrainer simulate_rollout update_policy policy PPOTrainer setup_mock_env_and_brains AllRewardsOutput BrainParameters PPOTrainer add_rewards_outputs update SACPolicy PPOPolicy setup_mock_env_and_brains reset evaluate model simulate_rollout _execute_model prepare_update update_dict make_mini_batch create_policy_mock reward_signal_update reward_signal_eval reward_signal_update reward_signal_eval create_policy_mock dirname abspath create_policy_mock reward_signal_update reward_signal_eval create_policy_mock reward_signal_update reward_signal_eval create_policy_mock reward_signal_update reward_signal_eval create_policy_mock reward_signal_update reward_signal_eval create_policy_mock reward_signal_update reward_signal_eval create_policy_mock reward_signal_update reward_signal_eval create_mock_brainparams RLTrainer dummy_config create_mock_brain Mock list create_mock_all_brain_info create_rl_trainer values end_episode construct_curr_info episode_steps create_mock_braininfo create_mock_policy add_experiences items list construct_fake_buffer create_rl_trainer clear_update_buffer append_update_buffer SACPolicy setup_mock_env_and_brains update evaluate create_sac_policy_mock simulate_rollout close update_buffer reset reset_default_graph create_sac_policy_mock simulate_rollout close update_reward_signals reset_default_graph update evaluate create_sac_policy_mock simulate_rollout close update_buffer reset reset_default_graph update evaluate create_sac_policy_mock simulate_rollout update_buffer reset reset_default_graph update evaluate create_sac_policy_mock simulate_rollout close update_buffer reset reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph str Mock SACTrainer save_model simulate_rollout len dummy_config policy setup_mock_env_and_brains Simple1DEnvironment _check_environment_trains Simple1DEnvironment _check_environment_trains TrainerController assert_called_with MagicMock basic_trainer_controller start_learning assert_called_once MagicMock assert_not_called trainer_controller_with_start_learning_mocks trainer_controller_with_start_learning_mocks start_learning MagicMock assert_called_once MagicMock basic_trainer_controller assert_called_once Mock MagicMock current_all_brain_info EnvironmentStep advance outputs assert_not_called trainer_controller_with_take_step_mocks assert_called_once_with previous_all_brain_info assert_called_once Mock MagicMock current_all_brain_info EnvironmentStep advance outputs assert_not_called trainer_controller_with_take_step_mocks assert_called_once_with previous_all_brain_info dummy_offline_bc_config dummy_offline_bc_config_with_override BrainParametersMock dummy_config BrainParametersMock dummy_bad_config _load_config StringIO extend copy items list value EnvironmentResponse external_brains payload text get_timer_root reset_timers put reset _send_response reset_parameters StepResponse env_factory step memory action perf_counter push reset method_handlers_generic_handler add_generic_rpc_handlers assert_called from_agent_proto AgentInfoProto extend assert_called from_agent_proto extend assert_not_called AgentInfoProto assert_not_called from_agent_proto AgentInfoProto extend UnityEnvironment close MockCommunicator UnityEnvironment close MockCommunicator reset str local_done print agents step close MockCommunicator UnityEnvironment len UnityEnvironment close MockCommunicator close RpcCommunicator close RpcCommunicator close RpcCommunicator SamplerManager sample_all sampler_config_1 sampler_config_2 SamplerManager SamplerManager sample_all incorrect_uniform_sampler incorrect_sampler_config set_gauge replace endswith add set walk join print extract_version_string set values | <img src="docs/images/unity-wide.png" align="middle" width="3000"/> <img src="docs/images/image-banner.png" align="middle" width="3000"/> # Unity ML-Agents Toolkit (Beta) [](docs/Readme.md) [](LICENSE) **The Unity Machine Learning Agents Toolkit** (ML-Agents) is an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. We also provide implementations (based on TensorFlow) | 728 |
MilesCranmer/bnn_chaos_model | ['time series'] | ['A Bayesian neural network predicts the dissolution of compact planetary systems'] | run_swag.py figures/spock/tseries_feature_functions.py figures/feature_importance.py find_minima.py figures/spock/featureclassifier.py figures/orbital_series.py figures/spock/training_data_functions.py figures/fit_trunc_dist.py figures/spock/regression.py filenames.py spock_reg_model.py figures/spock/__init__.py figures/spock/additional_feature_functions.py figures/main_figures.py figures/spock/nbodyregressor.py figures/spock/AMD_functions.py figures/oldsimsetup.py figures/custom_cmap.py figures/spock/version.py helper_functions.py figures/spock/simsetup.py figures/petit20_survival_time.py parse_swag_args.py figures/multiswag_5_planet.py figures/spock/modelfitting.py figures/comparison_figures.py figures/spock/feature_functions.py figures/multiswag_5_planet_plot.py figures/likelihood.py gen_dens get_accuracy_for_model load_data_normalized TorchWrapper reweighted_loss_func weight_init augment_time_series parse mlp safe_log_erf save_swag soft_clamp get_data SWAGModel load_swag CustomOneCycleLR VarModel is_random SimpleSKLearn make_colormap gradforward partforward fit_mu_sig fit_mu_sig_likelihood find_mu_sig_likelihood find_mu_sig likelihood fast_truncnorm sample_full_swag density_scatter fast_truncnorm get_features_for_sim data_setup_kernel get_xgb_prediction make_plot init_sim_parameters check_valid_sim check_hyperbolic set_integrator_and_timestep plotat make_transparent_color _exact_xiov nus_to_nu_eta Tsurv diffusion_slope integrate_gradeta _eventnu23 get_plsep_ov Tnorm nu_eta_to_nus get_Mfac diffusion_direction_curve distribution_exittimes nus_to_eta distance_eta_diffusion_direction gradeta_slope additional_get_tseries additional_features AMD relative_AMD_crit F AMD_crit FeatureClassifier get_tseries get_pairs find_strongest_MMR resonant_period_ratios populate_trio farey_sequence features unstable_error_fraction tnr_npv_curve stable_unstable_hist PR_curve calibration_plot train_test_split hasnull ROC_curve NbodyRegressor FeatureRegressorXGB FeatureRegressor data_setup_kernel init_sim_parameters check_valid_sim check_hyperbolic set_integrator_and_timestep AMD_stability_coefficients ressummaryfeaturesxgb restseriesv5 resparams findresv3 collision getpairs orbtseries critical_relative_AMD ressummaryfeaturesxgbv4 ressummaryfeaturesxgbv5 getpairsv5 F safe_run_func findres normressummaryfeaturesxgb resparamsv5 fillnanv5 orbsummaryfeaturesxgb compute_AMD AMD_stability_coefficient findres2 AMD_stable_Q ressummaryfeaturesxgb2 fillnan get_tseries get_pairs find_strongest_MMR get_extended_tseries resonant_period_ratios populate_extended_trio populate_trio farey_sequence features clf ravel histogram min ic concatenate concat transpose astype extend float32 tqdm nan_to_num cos tile sin append sum array read_csv enumerate len abs argmin int ic reshape fit loss_func KFold load_data split append numpy fit_transform predict len data xavier_uniform arccos multivariate_normal arctan2 reshape apply_augmentation_torch pi fmod uniform eye str no_eplusminus no_nan add_argument version ArgumentParser parse_args train_all load concatenate Variable reshape FloatTensor fit print copy PowerTransformer type DataLoader TensorDataset train_test_split StandardScaler BoolTensor open extend reversed Softplus ReLU range zeros_like where save load scale_ init_params StandardScaler array open append list isinstance enumerate compute_summary_stats cat fix_megno predict_instability summarize_megno fix_megno random_sample Variable zero_eplusminus zero_nan zero_megno augment zero_mmr sqrt exp array erf T exp prange average array append std x exp array inf T exp prange average array append std x _prior exp sum pdf len eval randint cuda forward_swag reshape range zeros_like randn T set_label subplots set_xlabel LogNorm tight_layout set_ylabel hist2d concatenate cos nan_to_num sin append range int list get_extended_tseries data_setup_kernel copy OrderedDict append array values enumerate copy normal index_update PRNGKey subplots query clip ax_joint use set_xlabel savefig legend gca plot set_xlim tight_layout annotate suptitle jointplot rc subplots_adjust dict set_ylabel set_style figure fill_between array set_ylim split calculate_orbits min particles array sqrt min array nan move_to_com set_integrator_and_timestep init_megno check_valid_sim m a integrate Simulation set_xlabel add set_visible set_ylabel gca OrbitPlotOneSlice spines tick_params ones minimum get_plsep_ov maximum sqrt get_Mfac sum distance_eta_diffusion_direction get_plsep_ov exact_xiov dawsn get_plsep_ov arange get_Mfac sqrt solve_ivp array concatenate solve_ivp array concatenate minimum diffusion_slope maximum ceil nus_to_eta get_pairs integrate P t AMD linspace nan populate_trio append zeros enumerate get_pairs abs log find_strongest_MMR OrderedDict log10 append a e AMD_crit mean nan zip enumerate additional_get_tseries particles m median std sqrt G particles m copy sqrt move_to_com a relative_AMD_crit brenth arctan min sqrt sin particles m copy sqrt move_to_com calculate_angular_momentum append int int list map floor ceil array range sorted particles a inf resonant_period_ratios pomega particles min cos e sqrt nan sin m a abs max n find_strongest_MMR particles sqrt calculate_megno m enumerate get_pairs integrate min t linspace nan populate_trio append zeros abs enumerate get_tseries get_pairs particles OrderedDict nan zip append median a std enumerate sum int read_csv values apply train_test_split roc_curve roc_auc_score precision_recall_curve train_test_split auc linspace train_test_split ravel enumerate auc train_test_split sqrt histogram append train_test_split sum range len sqrt log10 histogram append train_test_split sum range len brenth arctan min sqrt sin norm calculate_orbit vxyz len e cross sqrt m zeros a array enumerate xyz calculate_orbit critical_relative_AMD sqrt m compute_AMD a range len calculate_orbit critical_relative_AMD sqrt m compute_AMD a range zeros len calculate_orbit particles critical_relative_AMD sqrt m compute_AMD a calculate_orbits inc Omega integrate P e t linspace pomega zeros a enumerate M AMD_stability_coefficients integrate P polyfit linspace max inc OrderedDict a range e mean enumerate particles min t m zeros std len resonant_period_ratios pomega particles cos e isnan sqrt sin m abs n a resonant_period_ratios pomega particles min cos e isnan sqrt sin m n abs max M a resonant_period_ratios pomega particles min cos e isnan sqrt sin m n abs max M P Simulation move_to_com particles pi add sqrt m AMD_stability_coefficients median arange integrate P calculate_lyapunov cos polyfit linspace Z Zcom N abs max N_var findresv3 p from_Poincare OrderedDict init_megno sin a phiZcom Zstar from_Simulation phi e copy sqrt zip pomega enumerate int combinations poly1d sort particles dKprime calculate_megno zeros std AMD_stability_coefficients median arange integrate P calculate_lyapunov cos polyfit linspace Z Zcom N abs max N_var findresv3 p from_Poincare OrderedDict init_megno sin a phiZcom Zstar from_Simulation phi e copy sqrt zip pomega enumerate int combinations poly1d sort particles dKprime calculate_megno zeros std nan enumerate nan enumerate median calculate_lyapunov integrate P cos resparams linspace Z Zcom N abs max N_var getpairs from_Poincare init_megno sin sum phiZcom Zstar from_Simulation phi e sqrt pomega enumerate int particles min calculate_megno m zeros std fillnan median calculate_lyapunov integrate P cos resparams linspace Z Zcom N abs max N_var from_Poincare init_megno sin getpairsv5 sum phiZcom Zstar from_Simulation fillnanv5 phi e sqrt pomega enumerate int particles min calculate_megno m zeros std int N_var combinations arange particles N a zeros enumerate sorted particles a N N_var findresv3 getpairs from_Poincare OrderedDict a Zstar from_Simulation sqrt nan AMD_stability_coefficient enumerate int Zsep_inner sort particles min m array Zsep_outer N N_var findresv3 from_Poincare OrderedDict getpairsv5 a Zstar from_Simulation nan AMD_stability_coefficient enumerate int Zsep_inner sort particles min m array Zsep_outer integrate P cos linspace Z Zcom N abs N_var from_Poincare init_megno sin getpairsv5 Zstar phiZcom resparamsv5 from_Simulation phi e sqrt pomega enumerate particles t calculate_megno m zeros str find_strongest_MMR calculate_orbits inc Omega pomega particles e sqrt calculate_megno m a theta enumerate get_pairs integrate min t linspace nan populate_extended_trio append zeros abs enumerate | # [A Bayesian neural network predicts the dissolution of compact planetary systems](https://arxiv.org/abs/2101.04117) This repository is the official implementation of the paper https://arxiv.org/abs/2101.04117, containing training code, evaluation scripts, and figure generation. For an easy-to-use API implementing a trained version of this model, check out [spock](https://github.com/dtamayo/spock)!  # Running To train the model, download the data into the `/data` folder from this [Globus link](https://app.globus.org/file-manager?origin_id=ae09b8a8-5040-11eb-a4d1-0a53a3613b81&origin_path=%2F). Run the script `train.sh`, which will train | 729 |
MinaJf/FU-net | ['semantic segmentation'] | ['FU-net: Multi-class Image Segmentation Using Feedback Weighted U-net'] | train.py _weight.py image_loder.py main_unet2d.py unet_2d.py util.py predict.py ImageLoder Predictor Trainer div_dict save_images eval_to_str multiply_dict combine_images recale_array combine_3d_images add_dict combine_2d_images combine_and_save_images feedback_weight_map uint8 min astype array max array concatenate recale_array append argmax range concatenate recale_array append argmax range range save len save_images combine_images get mean constant exp log reduce_sum | # FU-net FU-net: Multi-class Image Segmentation using Feedback Weighted U-net | 730 |
Minerva-jiezhao/D-MEM | ['medical image segmentation', 'semantic segmentation'] | ['Automated Segmentation of Cervical Nuclei in Pap Smear Images using Deformable Multi-path Ensemble Model'] | datasets/camvid.py train.py models/layers.py utils/training.py datasets/joint_transforms.py test.py models/tiramisu.py models/deform_conv.py utils/imgs.py init_conv_offset _make_dataset LabelToLongTensor LabelTensorToPILImage CamVid JointScale JointRandomHorizontalFlip JointLambda JointRandomCrop JointPad JointRandomSizedCrop JointCenterCrop DeformConv2D TransitionDown center_crop Bottleneck DenseLayer DenseBlockUp DenseBlock TransitionUp DenseLayerUp FCDenseNet_36 FCDenseNet_test62 FCDenseNet_test72 FCDenseNet_37 FCDenseNet67 FCDenseNet FCDenseNet57 FCDenseNet103 view_annotated view_image decode_image view_sample_predictions_b view_sample_predictions error test save_weights load_weights adjust_learning_rate weights_init get_predictions train predict data zero_ zeros_like append join sorted walk size show copy imshow zeros numpy range transpose array show imshow clip decode_image join str print save round print format load load_state_dict data size max view float size sum str criterion model Variable backward zero_grad get_predictions step cuda enumerate Variable cuda get_predictions model param_groups kaiming_uniform_ isinstance Conv2d zero_ weight model Variable get_predictions append cuda model Variable size min range view_annotated iter get_predictions next cuda view_image asarray imwrite model Variable transpose get_predictions cuda enumerate | # Deformable Multi path Ensemble Model PyTorch1.0 Python3.7 This is my contribution to the "Automated Segmentation of Cervical Nuclein Pap Smear Images Using Deformable Multi-path Ensemble Model." (ISBI 2019 Oral Session Paper). Paper link: https://arxiv.org/abs/1812.00527 Please cite this paper if you find this project helpful for your research. # Dependencies python 3.7, CUDA 10.1, numpy 1.17, matplotlib 3.1.1, scikit-image (0.21), scipy (0.3.1), pyparsing (2.4.2), pytorch (1.0) (anaconda is recommended) other packages could be the latest version. # Training: 1.Install all dependencies python train.py | 731 |
MingSun-Tse/Caffe_IncReg | ['network pruning'] | ['Structured Pruning for Efficient ConvNets via Incremental Regularization'] | python/caffe/io.py python/caffe/test/test_python_layer.py scripts/download_model_binary.py python/caffe/net_spec.py python/caffe/coord_map.py python/caffe/test/test_net.py tools/extra/resize_and_crop_images.py python/draw_net.py python/caffe/test/test_net_spec.py src/caffe/test/test_data/generate_sample_data.py python/caffe/draw.py python/caffe/pycaffe.py tools/extra/extract_seconds.py scripts/cpp_lint.py python/classify.py python/caffe/proto/caffe_pb2.py examples/web_demo/exifutil.py examples/pycaffe/layers/pyloss.py python/caffe/test/test_solver.py python/caffe/classifier.py examples/pycaffe/layers/pascal_multilabel_datalayers.py examples/finetune_flickr_style/assemble_data.py python/caffe/test/test_io.py python/caffe/test/test_python_layer_with_param_str.py examples/pycaffe/tools.py tools/extra/parse_log.py python/caffe/__init__.py python/caffe/test/test_layer_type_list.py examples/web_demo/app.py scripts/copy_notebook.py python/caffe/detector.py python/detect.py examples/pycaffe/caffenet.py python/caffe/test/test_coord_map.py tools/extra/summarize.py download_image make_net max_pool caffenet conv_relu fc_relu CaffeSolver SimpleTransformer EuclideanLossLayer start_tornado start_from_terminal embed_image_html classify_upload index allowed_file ImagenetClassifier classify_url open_oriented_im apply_orientation main main main parse_args Classifier coord_map UndefinedMapException conv_params coord_map_from_to AxisMismatchException inverse crop_params compose crop Detector get_edge_label draw_net get_layer_label get_pydot_graph choose_color_by_layertype get_pooling_types_dict draw_net_to_file Transformer blobproto_to_array datum_to_array array_to_blobproto array_to_datum resize_image arraylist_to_blobprotovector_str blobprotovector_str_to_arraylist load_image oversample Layers Function Parameters Top NetSpec assign_proto param_name_dict to_proto _Net_blobs _Net_forward_all _Net_set_input_arrays _Net_backward _Net_params _Net_forward _Net_outputs _Net_forward_backward_all _Net_blob_loss_weights _Net_batch _Net_get_id_name _Net_inputs ReductionParameter HingeLossParameter BlobProto BlobProtoVector PruneParameter NetStateRule LayerParameter PowerParameter FillerParameter ArgMaxParameter V0LayerParameter InnerProductParameter ConvolutionParameter SolverState EltwiseParameter LossParameter SliceParameter BatchNormParameter WindowDataParameter DummyDataParameter HDF5OutputParameter TanHParameter TransformationParameter SoftmaxParameter ConcatParameter DataParameter SPPParameter ParamSpec EmbedParameter SolverParameter InputParameter MVNParameter ContrastiveLossParameter NetState NetParameter RecurrentParameter BiasParameter CropParameter DropoutParameter PoolingParameter Datum ParameterParameter SigmoidParameter BlobShape ExpParameter AccuracyParameter LogParameter ThresholdParameter TileParameter MemoryDataParameter LRNParameter ReLUParameter ImageDataParameter ELUParameter ReshapeParameter InfogainLossParameter ScaleParameter V1LayerParameter HDF5DataParameter PReLUParameter FlattenParameter PythonParameter TestCoordMap coord_net_spec TestBlobProtoToArray TestArrayToDatum TestLayerTypeList TestLevels TestStages simple_net_file TestNet TestAllInOne lenet TestNetSpec silent_net anon_lenet exception_net_file parameter_net_file SimpleLayer phase_net_file TestPythonLayer ParameterLayer PhaseLayer python_net_file ExceptionLayer SimpleParamLayer TestLayerWithParam python_param_net_file TestSolver ParseNolintSuppressions CheckVlogArguments CheckSectionSpacing FindNextMultiLineCommentEnd ReplaceAll CheckForFunctionLengths _SetOutputFormat _IsTestFilename _VerboseLevel CheckBraces RemoveMultiLineComments ResetNolintSuppressions CheckForNonStandardConstructs _SetVerboseLevel PrintUsage _NestingState CheckIncludeLine CheckAccess _CppLintState Search CheckInvalidIncrement RemoveMultiLineCommentsFromRange CleansedLines CheckForBadCharacters UpdateIncludeState FindPreviousMatchingAngleBracket CheckEmptyBlockBody FindNextMultiLineCommentStart Match _NamespaceInfo CheckMakePairUsesDeduction CheckCheck IsBlankLine _SetFilters ProcessLine _FunctionState CheckPosixThreading GetLineWidth GetHeaderGuardCPPVariable IsCppString _IncludeState CheckSpacing _ClassInfo CheckForCopyright IsErrorSuppressedByNolint ProcessFileData CheckForMultilineCommentsAndStrings CloseExpression _PreprocessorInfo _OutputFormat CheckForIncludeWhatYouUse CheckSpacingForFunctionCall FindEndOfExpressionInLine FindNextMatchingAngleBracket _SetCountingStyle ProcessFile _IncludeError CleanseRawStrings CheckAltTokens CheckForNewlineAtEOF ParseArguments CheckForNonConstReference PrintCategories _Filters main FilesBelongToSameModule CheckCStyleCast FileInfo _BlockInfo CheckForHeaderGuard CheckCaffeDataLayerSetUp ReverseCloseExpression CleanseComments _DropCommonSuffixes _ClassifyInclude CheckStyle CheckCaffeAlternatives FindStartOfExpressionInLine _ShouldPrintError CheckComment Error _GetTextInside CheckLanguage CheckCaffeRandom GetPreviousNonBlankLine reporthook parse_readme_frontmatter model_checks_out valid_dirname get_start_time extract_seconds extract_datetime_from_line get_log_created_year imread urlretrieve Convolution InnerProduct Data SoftmaxWithLoss LRN Accuracy max_pool InnerProduct conv_relu fc_relu Dropout get read info load_image classify_image StringIO join replace info secure_filename save filename open_oriented_im classify_image fromarray replace astype save resize StringIO iteritems listen HTTPServer format print start WSGIContainer update start_tornado add_option OptionParser debug port parse_args ImagenetClassifier forward run hasattr _getexif astype float32 tile apply_orientation open transpose model_def endswith ArgumentParser save mean_file channel_swap output_file dirname expanduser parse_args input_file predict Classifier set_mode_cpu load time isdir print add_argument set_mode_gpu pretrained_model gpu len DataFrame Detector format to_hdf detect_selective_search mean set_index to_csv detect_windows read_csv add_argument ArgumentParser read NetParameter output_image_file rankdir Merge TRAIN draw_net_to_file TEST get params array get params array crop_params conv_params pop collect_bottoms add fn coord_map compose coord_map_from_to items DESCRIPTOR batch_size str num_output get_pooling_types_dict add_edge get_edge_label Dot exclude get_layer_label add_node values choose_color_by_layertype Edge Node bottom append type layer include top data array diff shape BlobProto extend flat extend BlobProtoVector ParseFromString BlobProtoVector extend tostring shape Datum flat data len astype float32 tile zoom tuple resize fill empty array concatenate shape tile empty array LayerParameter NetParameter _to_proto extend Counter OrderedDict values iteritems hasattr isinstance extend add getattr setattr OrderedDict _blobs _blob_names zip OrderedDict _blob_loss_weights _blob_names zip OrderedDict list keys list keys iteritems layers index set outputs _forward len iteritems _backward layers inputs index set len iteritems asarray extend copy next _batch itervalues forward len iteritems izip_longest asarray backward extend copy next _batch itervalues zip forward len ascontiguousarray concatenate itervalues zeros next range len GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType GeneratedProtocolMessageType data Pooling pool Convolution NetSpec Deconvolution conv Input NamedTemporaryFile str close write data Pooling pool1 conv2 pool2 ip1 relu1 SoftmaxWithLoss Convolution NetSpec DummyData ip2 ReLU InnerProduct label conv1 Pooling SoftmaxWithLoss Convolution DummyData ReLU InnerProduct data NetSpec DummyData Silence data2 error search add group clear compile compile compile SetOutputFormat SetCountingStyle SetFilters _Filters startswith IsErrorSuppressedByNolint _ShouldPrintError write IncrementErrorCount replace append Match group find startswith endswith range error FindNextMultiLineCommentEnd RemoveMultiLineCommentsFromRange FindNextMultiLineCommentStart rstrip find xrange len FindEndOfExpressionInLine xrange len FindStartOfExpressionInLine error min search I xrange len FileInfo RepositoryName sep sub ParseNolintSuppressions error startswith split GetHeaderGuardCPPVariable enumerate error enumerate error len error replace count error find error find error find error find error Search error match InnermostClass replace error escape Match Search error group Search Check error lines Count End group Begin xrange NumLines Match raw_lines Search error match group error Match group pop group append Search pop group append Search elided replace CheckSpacingForFunctionCall rfind error len group min CloseExpression NumLines sub xrange find CheckComment Match Search lines_without_raw_strings error group starting_linenum Match range Search error rfind len group ReverseCloseExpression Search Match CloseExpression find error Match CloseExpression find elided error strip group FindEndOfExpressionInLine xrange find Match CloseExpression len error Match finditer normalize isinstance GetLineWidth int InnermostClass CheckCheck error CheckAltTokens CheckBraces CheckSpacing CheckSectionSpacing CheckEmptyBlockBody CheckAccess GetHeaderGuardCPPVariable lines_without_raw_strings _DropCommonSuffixes RepositoryName match split CheckNextIncludeOrder CanonicalizeAlphabeticalOrder FileInfo error search group SetLastHeader match _ClassifyInclude Match pop end search set itervalues append M rstrip replace CheckCStyleCast error _GetTextInside CheckIncludeLine search group lstrip startswith Match ResetSection Search split rfind error group ReverseCloseExpression lstrip xrange findall Match Search ReplaceAll error Match Search endswith replace setdefault group search CleanseComments open FilesBelongToSameModule error search copy sub xrange NumLines FullName keys error search CheckPosixThreading ParseNolintSuppressions CheckVlogArguments CheckMakePairUsesDeduction CheckCaffeDataLayerSetUp CheckLanguage CheckInvalidIncrement CheckCaffeRandom CheckForNonConstReference check_fn Update CheckForNonStandardConstructs CheckStyle raw_lines CheckForMultilineCommentsAndStrings CheckCaffeAlternatives CheckForFunctionLengths CleansedLines _NestingState CheckForBadCharacters CheckForNewlineAtEOF _IncludeState NumLines RemoveMultiLineComments CheckForCopyright ResetNolintSuppressions CheckForHeaderGuard xrange CheckCompletedBlocks CheckForIncludeWhatYouUse ProcessLine _FunctionState Error rstrip endswith len write ProcessFileData _SetVerboseLevel range split write exit join write exit _VerboseLevel int getopt _SetOutputFormat set _SetVerboseLevel PrintCategories _SetFilters _OutputFormat PrintUsage _SetCountingStyle split getreader ParseArguments ResetErrorCounts stderr exit verbose_level PrintErrorCounts StreamReaderWriter ProcessFile getwriter int time write flush load join index int rfind datetime split getctime year strip extract_datetime_from_line get_start_time total_seconds strip write get_log_created_year close extract_datetime_from_line open | ## Pruned models | Model | Baseline accuracy (%) | Speedup ratio | Pruned accuracy(%) | | :-: | :-: | :-: | :-: | | vgg16 | [70.62/89.56](http://www.robots.ox.ac.uk/~vgg/research/very_deep/) | 5x | [67.62/88.04](https://drive.google.com/open?id=112OyHziceXoww6rZ_aIjTUzpKElg0sFd) | | resnet50 | [72.92/91.18](https://github.com/KaimingHe/deep-residual-networks) | 2x | [72.47/91.05](https://drive.google.com/open?id=112OyHziceXoww6rZ_aIjTUzpKElg0sFd) | Note: - Speedup ratio is the theoretical value measured by FLOPs reduction in *only conv* layers. - The baseline accuracies are obtained by evaluating the downloaded model *without* finetuning them on our produced ImageNet dataset. - The provided pruned caffemodels are only zero-masked, without taking out the zero weight filters or columns. So they are literally of the same size as their baseline counterparts. ## Environment | 732 |
MiriamHu/ActiveBoundary | ['active learning'] | ['Active Decision Boundary Annotation with Deep Generative Models'] | objectives.py train.py callbacks.py joint_optimisation.py plot_utils.py generative_model.py interface.py dataset.py options.py labeler.py utils.py query_strategy.py model.py LR_scheduler_factory Dataset GenerativeALIModel VerticalScrolledFrame LabelInterface PointLabelInterface WeightsEarlyStopping do_optimization make_Xj_bigger create_joint_model create_single_model make_Xi_bigger WeightSharing NoisyLineLabeler Labeler LineLabeler IdealLabeler HumanPointLabeler PointLabeler HumanLineLabeler JointOptimisationSVM Model SimpleSVM gmse_factory my_accuracy set plot_after_query plot_after_label plot_estimated_decision_boundary plot_groundtruth_decision_boundary plot_base plot_intersection_points plot_line_query_segment plot_data_points LineQueryStrategy UncertaintyDenseSamplingLine QueryStrategy RandomSamplingLine UncertaintySamplingLine ClusterCentroidsLine ClusterCentroids RandomSampling UncertaintySampling UncertaintyDenseSampling ActiveBoundary project_point_on_decision_boundary compute_intersection_line_decision_boundary load_unlabeled_indices_from_hdf5 load_decision_boundary_from_hdf5 make_line_segment compute_radius_sphere project_point_on_line to_vector compile Adam Dense Model Input shared_layer Sequential Adam add Dense compile set_weights time print make_Xj_bigger l2 create_joint_model create_single_model history make_Xi_bigger fit sign get_file join hdf5_dataset_encoded hdf5_dataset enable_gpu main_loop_path getcwd add_argument mkdir ArgumentParser parse_args db_points subplot get_position unlabeled_class show suptitle set_position plot_estimated_decision_boundary plot_groundtruth_decision_boundary b0 w plot_intersection_points figure legend b plot_data_points mu mu show plot_line_query_segment plot_base db_points _old_mu show subplot plot_groundtruth_decision_boundary legend plot_data_points mu get_position set_position b0 w plot_intersection_points b unlabeled_class suptitle plot_estimated_decision_boundary figure plot_line_query_segment plot ylim unique xlim range len T linspace plot T linspace plot plot plot data_sources H5PYDataset data_sources H5PYDataset dot float astype float32 print astype float32 astype float32 dot astype float32 norm max | ## Active Decision Boundary Annotation with Deep Generative Models Miriam W. Huijser and Jan C. van Gemert International Conference on Computer Vision 2017 (ICCV): spotlight presentation. Paper: https://jvgemert.github.io/pub/huijserICCV17ActiveBoundAnnoGAN.pdf arXiv preprint: https://arxiv.org/abs/1703.06971 <p align="center"><img src="https://user-images.githubusercontent.com/9445724/31823446-849b2b78-b5ac-11e7-9329-a7c56a6333ff.png" width="500" height="500"/></p> We provide the code for the decision boundary annotation active learning algorithm. If you find my code useful for your research, please cite: ``` @article{huijser2017active, | 733 |
MiriamJo/ML-Agents | ['unity'] | ['Unity: A General Platform for Intelligent Agents'] | ml-agents-envs/mlagents_envs/communicator_objects/capabilities_pb2.py ml-agents/mlagents/trainers/components/reward_signals/curiosity/model.py ml-agents-envs/mlagents_envs/communicator_objects/command_pb2.py ml-agents/mlagents/trainers/cli_utils.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/tests/yamato/setup_venv.py ml-agents/mlagents/trainers/barracuda.py ml-agents/mlagents/trainers/optimizer/tf_optimizer.py utils/run_markdown_link_check.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/tests/yamato/scripts/run_gym.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/tests/yamato/check_coverage_percent.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-envs/mlagents_envs/side_channel/environment_parameters_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-envs/mlagents_envs/tests/test_env_utils.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/env_utils.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-envs/mlagents_envs/side_channel/side_channel_manager.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-envs/mlagents_envs/side_channel/stats_side_channel.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/ghost/controller.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-envs/mlagents_envs/tests/test_steps.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/tests/yamato/scripts/run_llapi.py ml-agents/mlagents/trainers/stats.py ml-agents/mlagents/trainers/tests/test_trajectory.py ml-agents/mlagents/trainers/optimizer/optimizer.py VerifyVersionCommand UnityGymException ActionFlattener UnityToGymWrapper create_mock_vector_steps test_gym_wrapper create_mock_group_spec test_branched_flatten setup_mock_unityenvironment test_gym_wrapper_visual 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 create_name_behavior_id BrainParameters CameraResolution get_global_agent_id behavior_spec_to_brain_parameters BufferException AgentBuffer StoreConfigFile DetectDefault DetectDefaultStoreTrue Curriculum make_demo_buffer write_demo get_demo_files load_demonstration write_delimited demo_to_buffer 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 write_run_options run_training try_create_meta_curriculum run_cli main _create_parser get_version_string MetaCurriculum EncoderType NormalizerTensors ScheduleType ModelUtils main parse_command_line MultiRangeUniformSampler UniformSampler SamplerFactory SamplerManager GaussianSampler Sampler SimpleEnvManager StatsWriter StatsSummary ConsoleWriter StatsReporter GaugeWriter TensorboardWriter StatsPropertyType CSVWriter worker EnvironmentResponse EnvironmentRequest 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 handle_existing_directories initialize_trainer _load_config assemble_curriculum_config TrainerFactory load_config AgentExperience Trajectory SplitObservations BCModel BCModule create_reward_signal RewardSignal CuriosityModel CuriosityRewardSignal ExtrinsicRewardSignal GAILModel GAILRewardSignal GhostController GhostTrainer Optimizer TFOptimizer NNPolicy Policy TFPolicy UnityPolicyException PPOOptimizer PPOTrainer get_gae discount_rewards SACPolicyNetwork SACTargetNetwork SACNetwork SACOptimizer SACTrainer create_mock_pushblock_brain create_steps_from_brainparams simulate_rollout make_brain_parameters setup_mock_brain make_fake_trajectory create_mock_banana_brain create_mock_steps create_mock_brainparams create_mock_3dball_brain RecordEnvironment clamp SimpleEnvironment MemoryEnvironment test_end_episode test_agent_deletion test_agent_manager_queue test_agentprocessor test_agent_manager test_agent_manager_stats 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_unsupported_version_raises_error test_load_demo test_demo_mismatch test_edge_cases 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_run_training test_yaml_args test_sampler_configs 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 test_load_save create_policy_mock test_normalization dummy_config test_policy_evaluate _compare_two_policies 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_advance 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_simple_ghost_fails test_gail test_visual_advanced_sac _check_environment_trains test_visual_sac test_2d_ppo generate_config test_simple_sac default_reward_processor test_simple_ghost test_simple_asymm_ghost test_gail_visual_ppo test_simple_ppo test_gail_visual_sac test_recurrent_ppo DebugWriter test_recurrent_sac test_simple_asymm_ghost_fails test_visual_advanced_ppo test_visual_ppo test_2d_sac simple_record test_tensorboard_writer test_stat_reporter_add_summary_write test_tensorboard_writer_clear test_gauge_stat_writer_sanitize ConsoleWriterTest test_csv_writer test_stat_reporter_property MockEnvWorker mock_env_factory SubprocessEnvManagerTest test_subprocess_env_raises_errors create_worker_mock test_subprocess_env_endtoend 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_assemble_curriculum_config 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_existing_directories 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 check_coverage main clean_previous_results TestResults parse_results main main main run_training override_config_file init_venv get_unity_executable_path override_legacy_config_file get_base_path run_standalone_build checkout_csharp_version undo_git_checkout get_base_output_path test_closing test_run_environment test_closing test_run_environment VerifyVersionCommand ActionType BehaviorMapping TerminalStep DecisionSteps BehaviorSpec TerminalSteps BaseEnv DecisionStep Communicator UnityEnvironment validate_environment_path launch_executable get_platform UnityCommunicatorStoppedException UnityObservationException UnityWorkerInUseException UnityException UnityCommunicationException UnityTimeOutException UnitySideChannelException UnityEnvironmentException UnityActionException get_logger set_log_level MockCommunicator RpcCommunicator UnityToExternalServicerImplementation _generate_split_indices process_pixels behavior_spec_from_proto _raise_on_nan_and_inf observation_to_np_array steps_from_proto _process_vector_observation _process_visual_observation _get_thread_timer TimerNode merge_gauges hierarchical_timer add_metadata get_timer_tree get_timer_root reset_timers get_timer_stack_for_thread set_gauge timed GaugeNode TimerStack UnityToExternalProtoServicer add_UnityToExternalProtoServicer_to_server UnityToExternalProtoStub EngineConfigurationChannel EngineConfig EnvironmentParametersChannel FloatPropertiesChannel IncomingMessage OutgoingMessage RawBytesChannel SideChannel SideChannelManager StatsAggregationMethod StatsSideChannel test_initialization test_reset test_returncode_to_signal_name test_log_file_path_is_set test_close test_step test_port_defaults test_handles_bad_filename test_check_communication_compatibility test_set_logging_level test_validate_path mock_glob_method test_launch_executable test_validate_path_empty test_rpc_communicator_checks_port_on_create test_rpc_communicator_create_multiple_workers test_rpc_communicator_close test_batched_step_result_from_proto_raises_on_nan test_process_pixels test_process_visual_observation_bad_shape test_agent_behavior_spec_from_proto proto_from_steps_and_action test_batched_step_result_from_proto test_action_masking_continuous test_action_masking_discrete_1 generate_list_agent_proto generate_uncompressed_proto_obs test_batched_step_result_from_proto_raises_on_infinite generate_compressed_proto_obs test_vector_observation proto_from_steps test_action_masking_discrete generate_compressed_data test_action_masking_discrete_2 test_process_pixels_gray test_process_visual_observation test_raw_bytes test_int_channel test_message_float_list IntChannel test_engine_configuration test_message_bool test_message_string test_float_properties test_environment_parameters test_message_int32 test_stats_channel test_message_float32 test_decision_steps test_specs test_terminal_steps test_empty_terminal_steps test_empty_decision_steps test_timers decorated_func table_line ReleaseInfo validate_packages main NonTrivialPEP420PackageFinder main set_academy_version_string _escape_non_none extract_version_string check_versions set_package_version set_version MagicMock create_mock_vector_steps UnityToGymWrapper sample create_mock_group_spec setup_mock_unityenvironment step MagicMock create_mock_vector_steps UnityToGymWrapper create_mock_group_spec setup_mock_unityenvironment MagicMock create_mock_vector_steps UnityToGymWrapper sample create_mock_group_spec setup_mock_unityenvironment step tuple CONTINUOUS range DISCRETE list array range BehaviorMapping convert_to_barracuda convert convert_to_onnx _make_frozen_graph _enforce_onnx_conversion convert_frozen_to_onnx info model_path items list 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 layers isinstance print tensors inputs zip 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 resequence_and_append obs from_observations steps_from_proto vector_actions AgentBuffer append reset_agent vector_observations array visual_observations enumerate make_demo_buffer camera_resolutions load_demonstration behavior_spec_to_brain_parameters zip enumerate isdir isfile get_demo_files write SerializeToString _EncodeVarint len add_argument_group add_argument ArgumentParser parse_args start_learning join join pop SamplerManager MetaCurriculum set_all_curricula_to_lesson_num set_warnings_enabled warning __version__ DEBUG seed load_model _asdict set_log_level debug run_training add_timer_metadata INFO get_version_string train_model API_VERSION print dumps cpu randint parse_command_line run_cli add_argument ArgumentParser RunOptions experiment_config_path load_config get_timer_root reset_timers put _send_response StepResponse env_factory list _generate_all_results set_log_level get_and_reset_stats set_actions StatsSideChannel action set_configuration EngineConfigurationChannel external_brains payload set_float_parameter STEP EnvironmentParametersChannel items EnvironmentResponse EXTERNAL_BRAINS reset 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 list 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 join min_lesson_length format SACTrainer GhostTrainer copy warning PPOTrainer items list isdir get check_config rcls list zeros_like size reversed range append discount_rewards Mock CameraResolution arange ones BehaviorSpec 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 ActionInfo publish_trajectory_queue range create_mock_steps AgentProcessor empty create_mock_policy add_experiences Mock assert_has_calls ActionInfo publish_trajectory_queue range call create_mock_steps append AgentProcessor empty create_mock_policy add_experiences Mock assert_has_calls ActionInfo end_episode publish_trajectory_queue range call create_mock_steps append AgentProcessor empty create_mock_policy add_experiences AgentManager create_mock_policy Mock get_nowait AgentManagerQueue put Mock assert_any_call remove record_environment_stats AgentManager add_writer StatsReporter write_stats join remove _get_candidate_names convert _get_default_tempdir dirname abspath isfile next join export_policy_model sess save_model 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 list ppo_dummy_config create_bc_module create_mock_3dball_brain update items list ppo_dummy_config current_lr create_bc_module create_mock_3dball_brain update items list ppo_dummy_config create_bc_module create_mock_3dball_brain update items list ppo_dummy_config create_mock_banana_brain create_bc_module update items list 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 list 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 dirname abspath mock_open BytesIO DemonstrationMetaProto write_delimited create_tf_graph brain_name setup_mock_brain load_weights init_load_weights zip assert_array_equal get_weights PPOTrainer create_policy GhostController GhostTrainer PPOTrainer subscribe_trajectory_queue advance put make_fake_trajectory BrainParameters from_name_behavior_id AgentManagerQueue add_policy brain_name create_policy GhostController GhostTrainer PPOTrainer simulate_rollout get_nowait advance _swap_snapshots setup_mock_brain publish_policy_queue BrainParameters from_name_behavior_id AgentManagerQueue add_policy brain_name create_policy clear safe_load MagicMock parse_command_line clear parse_command_line 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 SimpleEnvironment safe_load loads MetaCurriculum _check_environment_trains setup_mock_brain NNPolicy join save_model _set_step initialize_or_load create_policy_mock _compare_two_policies list create_steps_from_brainparams evaluate brain agent_id assert_array_equal list create_steps_from_brainparams evaluate brain agent_id create_policy_mock reset_default_graph NNPolicy update_normalization to_agentbuffer make_fake_trajectory BrainParameters zeros range run MagicMock basic_mock_brain basic_params BehaviorSpec get_action empty FakePolicy MagicMock basic_mock_brain DecisionSteps basic_params get_action array FakePolicy MagicMock basic_mock_brain ActionInfo DecisionSteps 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 list 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 list 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 update create_optimizer_mock reward_signal_eval reward_signal_update update create_optimizer_mock reward_signal_eval reward_signal_update 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 create_optimizer_mock reward_signal_eval reward_signal_update set_is_policy_updating FakeTrainer dummy_config create_mock_brain end_episode list create_rl_trainer values items list construct_fake_buffer create_rl_trainer _clear_update_buffer create_rl_trainer set_is_policy_updating subscribe_trajectory_queue advance put make_fake_trajectory publish_policy_queue AgentManagerQueue get_nowait 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 list SACTrainer values make_brain_parameters add_policy brain_name create_policy SamplerManager sample_all sampler_config_1 sampler_config_2 SamplerManager SamplerManager sample_all incorrect_uniform_sampler incorrect_sampler_config update safe_load print SimpleEnvironment _check_environment_trains generate_config SimpleEnvironment _check_environment_trains generate_config SimpleEnvironment _check_environment_trains generate_config SimpleEnvironment _check_environment_trains generate_config MemoryEnvironment _check_environment_trains generate_config SimpleEnvironment _check_environment_trains generate_config SimpleEnvironment _check_environment_trains generate_config SimpleEnvironment _check_environment_trains generate_config SimpleEnvironment _check_environment_trains generate_config MemoryEnvironment _check_environment_trains generate_config SimpleEnvironment _check_environment_trains generate_config SimpleEnvironment _check_environment_trains generate_config SimpleEnvironment _check_environment_trains generate_config SimpleEnvironment _check_environment_trains generate_config SimpleEnvironment _check_environment_trains simple_record generate_config SimpleEnvironment _check_environment_trains simple_record generate_config SimpleEnvironment _check_environment_trains simple_record generate_config clear assert_called_once_with Mock get_stats_summaries add_stat add_writer StatsReporter float range write_stats clear Mock add_property add_writer StatsReporter assert_called_once_with sleep TensorboardWriter StatsSummary write_stats generate_config close SubprocessEnvManager simple_env_factory _check_environment_trains default_config default_config close SubprocessEnvManager GhostController MagicMock GhostController TrainerController MagicMock 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 _load_config assemble_curriculum_config mkdir join handle_existing_directories append from_observations range ones items list 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 format print exit walk float check_coverage 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 init_venv add_argument ArgumentParser parse_args split strip run_standalone_build int time join init_venv override_config_file print exit override_legacy_config_file get_base_path rename run_standalone_build run checkout_csharp_version exists get_base_output_path python run_training csharp exists join move get_unity_executable_path print makedirs dirname get_base_output_path run check_call check_call check_call update list values update list values check_call str format UnityToGymWrapper print step reset sample UnityEnvironment range reset UnityEnvironment close UnityToGymWrapper get_steps is_action_discrete EngineConfigurationChannel randn set_configuration_parameters discrete_action_branches len action_size any set_actions is_action_continuous column_stack join format basename replace glob debug getcwd normpath validate_environment_path debug format 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 get_ident TimerStack perf_counter push items list merge reset method_handlers_generic_handler add_generic_rpc_handlers UnityEnvironment close MockCommunicator UnityEnvironment MockCommunicator _executable_args UnityEnvironment MockCommunicator index get_steps obs close reset MockCommunicator zip UnityEnvironment observation_shapes len get_steps obs zip ones step close MockCommunicator set_actions zeros UnityEnvironment observation_shapes len UnityEnvironment close MockCommunicator validate_environment_path validate_environment_path launch_executable PermissionError set_log_level 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 obs concatenate action_mask agent_id ObservationProto AgentInfoProto append generate_uncompressed_proto_obs proto_from_steps 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 list sort CONTINUOUS agent_id BehaviorSpec steps_from_proto generate_list_agent_proto range BehaviorSpec steps_from_proto DISCRETE generate_list_agent_proto action_mask BehaviorSpec steps_from_proto DISCRETE generate_list_agent_proto action_mask BehaviorSpec steps_from_proto DISCRETE generate_list_agent_proto action_mask CONTINUOUS BehaviorSpec steps_from_proto generate_list_agent_proto action_mask BrainParametersProto behavior_spec_from_proto extend CONTINUOUS generate_list_agent_proto BehaviorSpec CONTINUOUS generate_list_agent_proto BehaviorSpec generate_side_channel_messages process_side_channel_message send_int IntChannel FloatPropertiesChannel process_side_channel_message generate_side_channel_messages get_property set_property uuid4 process_side_channel_message generate_side_channel_messages 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_configuration channel_id EngineConfigurationChannel generate_side_channel_messages process_side_channel_message set_configuration_parameters RawBytesChannel read_float32 read_int32 IncomingMessage get_and_clear_received_messages default_config channel_id generate_side_channel_messages process_side_channel_message read_string set_float_parameter RawBytesChannel read_float32 read_int32 IncomingMessage EnvironmentParametersChannel IncomingMessage write_float32 write_string buffer write_int32 get_and_reset_stats on_message_received StatsSideChannel OutgoingMessage DecisionSteps action_mask empty BehaviorSpec TerminalSteps empty BehaviorSpec BehaviorSpec set_gauge TimerStack print find_packages find validate_packages replace endswith add set walk join print extract_version_string set values join format set_academy_version_string print set_package_version enumerate split | MiriamJo/ML-Agents | 734 |
MiuLab/TaylorGAN | ['text generation'] | ['TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation', 'TaylorGAN: Neighbor-Augmented Policy Update for Sample-Efficient Natural Language Generation'] | src/scripts/train/GAN.py src/factories/__init__.py src/core/preprocess/preprocessors.py src/core/objectives/__init__.py src/core/models/sequence_modeling.py src/library/utils/collections.py src/core/train/callbacks/channels.py src/core/preprocess/adaptors.py src/core/train/pubsub_base.py src/library/tf_keras_zoo/layers/masking/apply_mask.py src/core/evaluate/generator_executor.py src/core/objectives/regularizers/__init__.py src/library/tf_keras_zoo/layers/masking/tests/test_mask_conv.py src/library/tf_keras_zoo/optimizers/radam.py src/library/tf_keras_zoo/layers/masking/mask_conv.py src/core/evaluate/bleu.py src/library/utils/iterator.py src/factories/modules/discriminator.py src/scripts/train/train.py src/library/utils/tests/test_logging.py src/core/train/callbacks/train_profiler.py src/library/tf_keras_zoo/layers/tests/test_resnet.py src/library/tf_keras_zoo/optimizers/tests/test_gradient_clipping.py src/core/preprocess/tests/test_preprocessors.py src/core/train/updaters.py src/library/tf_keras_zoo/layers/tests/test_recurrent.py src/core/preprocess/config_objects.py src/library/utils/tests/test_array_utils.py src/library/tf_keras_zoo/networks/tests/test_sequential.py src/scripts/evaluate/generate_text.py src/factories/trainer_factory/MLE.py src/core/objectives/regularizers/variable_regularizers.py src/library/tf_keras_zoo/layers/resnet.py src/scripts/tests/test_integration.py src/library/tf_keras_zoo/optimizers/__init__.py src/library/tf_keras_zoo/optimizers/tests/test_radam.py src/library/utils/tests/test_collections.py src/library/tf_keras_zoo/layers/recurrent.py src/factories/callback_factory/__init__.py src/library/tf_keras_zoo/networks/sequential.py src/core/train/callbacks/callback_list.py src/core/objectives/collections.py src/library/tf_keras_zoo/optimizers/tests/test_weight_decay.py src/library/utils/logging.py src/core/objectives/GAN/discrete.py src/scripts/parsers/__init__.py src/core/objectives/GAN/__init__.py src/factories/trainer_factory/GAN.py src/factories/trainer_factory/optimizers.py src/core/preprocess/tests/test_config_objects.py conftest.py src/library/utils/cache_utils.py src/library/tf_keras_zoo/layers/masking/tests/test_utils.py src/library/tf_keras_zoo/layers/masking/utils.py src/core/objectives/regularizers/base.py src/library/utils/tests/test_cache_utils.py src/factories/modules/__init__.py src/core/evaluate/__init__.py src/core/models/discriminators.py src/scripts/evaluate/perplexity.py src/core/objectives/regularizers/regularizers.py src/core/evaluate/tests/test_bleu.py src/factories/utils.py src/library/utils/random.py src/core/evaluate/fed.py src/scripts/snippets.py src/core/train/callbacks/tensorboardx_writer.py src/library/utils/format_utils.py src/library/tf_keras_zoo/optimizers/look_ahead.py src/factories/trainer_factory/trainer_factory.py src/scripts/train/MLE.py src/core/objectives/GAN/continuous.py src/library/tf_keras_zoo/layers/masking/tests/test_apply_mask.py src/core/train/callbacks/__init__.py src/scripts/parsers/parsers.py src/library/utils/func_utils.py src/core/train/trainers.py src/core/preprocess/tests/conftest.py src/library/tf_keras_zoo/networks/tests/test_model.py src/library/tf_keras_zoo/layers/masking/__init__.py src/library/utils/tests/test_func_utils.py src/core/cache.py src/taylorgan.py src/factories/callback_factory/callback_factory.py src/factories/callback_factory/evaluator_creator.py src/library/tf_keras_zoo/layers/tests/test_embeddings.py src/library/tf_keras_zoo/optimizers/gradient_clipping.py src/library/utils/tests/test_iterator.py src/core/train/optimizer.py src/library/tf_keras_zoo/networks/model.py src/core/evaluate/tests/test_fed.py src/core/train/callbacks/progbar_logger.py src/library/utils/file_helper.py src/scripts/train/restore_from_checkpoint.py src/core/preprocess/__init__.py src/library/tf_keras_zoo/layers/conv1d_transpose.py src/library/utils/array_utils.py src/library/tf_keras_zoo/layers/embeddings.py src/library/utils/tests/test_format_utils.py src/scripts/evaluate/evaluate_text.py src/library/tf_keras_zoo/optimizers/weight_decay.py src/factories/trainer_factory/__init__.py src/core/models/interfaces.py src/core/models/__init__.py src/library/tf_keras_zoo/functions.py src/library/tf_keras_zoo/networks/__init__.py src/library/tf_keras_zoo/optimizers/tests/test_look_ahead.py src/core/preprocess/tests/test_tokenizers.py src/factories/data_factory.py src/library/tf_keras_zoo/layers/masking/pooling.py src/core/train/callbacks/base.py src/library/tf_keras_zoo/tests/test_functions.py src/core/train/callbacks/evaluator.py src/core/train/__init__.py src/factories/modules/generator.py src/core/models/generators.py src/library/utils/__init__.py src/core/train/fit_loop.py src/core/objectives/GAN/base.py src/library/tf_keras_zoo/layers/__init__.py src/library/utils/tests/test_file_helper.py src/core/objectives/MLE.py src/core/preprocess/record_objects.py src/core/preprocess/tokenizers.py src/core/preprocess/tests/test_adaptors.py src/core/train/callbacks/model_checkpoint.py src/library/tf_keras_zoo/layers/masking/tests/test_pooling.py src/library/tf_keras_zoo/layers/tests/test_conv1d_transpose.py src/core/train/callbacks/model_saver.py data_dir sess pytest_addoption pytest_collection_modifyitems CacheCenter get_closest_values SmoothingFunction get_brevity_penalty_table hashable_ngrams NGramCounter BLEUCalculator cum_geomean FEDCalculator FEDModel TextGenerator PerplexityCalculator test_hashable_ngrams test_long test_with_brevity_penalty test_get_closest_values test_smoothing test_trivial test_with_clipped test_cache Discriminator Generator AutoRegressiveGenerator ModuleInterface SampledTokenSequence TokenSequence LossCollection MLEObjective BCE GANObjective D_BCE GANEstimator GANLossTuple _compute_loss_of_probability GumbelSoftmaxEstimator SoftmaxEstimator TaylorEstimator StraightThroughEstimator ReinforceEstimator Regularizer GradientPenaltyRegularizer WordVectorRegularizer EntropyRegularizer VariableRegularizer EmbeddingRegularizer SpectralRegularizer WordEmbeddingCollection UttutPipeline SpecialTokenConfig CorpusConfig LanguageConfig Namespace Preprocessor UttutPreprocessor TextDataset DataCollection MetaData UttutTokenizer get_freq_and_maxlen Tokenizer corpus_config language_config TestWordEmbeddingCollection TestUttutPipeline test_is_valid test_isnot_valid test_uttut_preprocessor TokenizerTestTemplate TestUttutTokenizer DataLoader OptimizerWrapper Subscriber Subject GANTrainer NonParametrizedTrainer Trainer ModuleUpdater GeneratorUpdater DiscriminatorUpdater Callback CallbackList MessageChannel register_channel TextEvaluator EvaluateCommander ModelCheckpoint ModelSaver ProgbarLogger ModuleBar MetricsBar MetricsWriter ModuleLossWriter TensorBoardXWritter TrainProfiler preprocess load_corpus_table parse_config create_factory_action parent_parser create CallbackCreator EvaluatorCreator print_samples create resnet cnn create gru_cell GANCreator MLECreator _wrap_optimizer create_action_of sgd rmsprop adam radam create_parser create TrainerCreator takewhile_mask suffix_sum gaussian suffix_sum_with_gradients compute_advantage _get_discount_matrix pairwise_euclidean random_choice_by_logits masked_reduce Conv1DTranspose Embedding PositionalEmbedding OutputEmbedding _tolist GRUCell SkipConnectCells ZoneoutWrapper ResBlock ApplyMask MaskConv1D MaskMaxPooling1D MaskGlobalAveragePooling1D MaskGlobalPooling1D MaskAveragePooling1D MaskGlobalMaxPooling1D apply_mask ComputeOutputMaskMixin1D test_apply_mask_layer_propagate_mask test_mask_conv test_mask_max_pool1d TestMaskGlobalAveragePooling1D test_mask_avg_pool1d test_mask_global_max_pool1d test_apply_mask_just_do_once test_apply_mask test_output_value inputs test_configs test_output_shape test_raise_invalid_input_channel_after_built test_raise_invalid_input_rank test_extend_partially_trainable test_dropout test_tie_embedding inputs mask create_session_from_graphdef test_mask_index test_output_shape test_construct_from_invalid_weights_raise test_construct_from_weights test_init_from_invalid_mask_index_raise test_freeze_success test_zoneout_wrapper test_zoneout_multi_rate test_initial_state test_skip_connect_cells test_output_shape layer test_output_mask Model Sequential ModelSupportMask test_support_input_mask_propagate network test_support_mask_args_used test_mask_computed_by_layer_propagate test_mask_given_in_inputs_propagate test_build_sublayers_when_first_called test_masked_inputs_propagate float_inputs sequential int_inputs test_context_manager_work_when_first_called test_empty_sequential GradientClipping LookAhead RAdamOptimizer WeightDecay test_clip_global_norm test_clip_norm test_clip_value test_look_ahead test_radam test_sparse_weight_decay test_weight_decay_with_filter test_weight_decay test_random_choice_by_logits test_suffix_sum_with_gradients test_suffix_sum test_takewhile_mask unpad get_seqlens safe_divide random_sample FileCache NumpyCache reuse_method_call PickleCache JSONCache cached_property JSONSerializableMixin dict_of_unique counter_ior counter_or ExponentialMovingAverageMeter count_lines format_list FormatableMixin format_object format_highlight2 format_highlight left_aligned join_arg_string format_id format_path get_args log_args_when_error extract_wrapped ObjectWrapper match_abbrev tqdm_open batch_generator logging_indent _IndentPrinter TqdmRedirector random_sample test_get_seqlens test_cache_static A B TestCacheProperty test_cache_format test_cache_callable_path test_reuse_method_call test_deserialize_subclass_and_non_subclass equal test_dict_of_unique test_exponential_average_meter test_counter_or test_count_lines test_left_aligned test_object_wrapper test_abbrev_kwargs test_log_args_when_error TestBatchGenerator test_tqdm_open TestLoggingIndent TestTqdmRedirector get_subdir_if_unique_base_tag_exists get_tf_config_proto load_serving_signature set_package_verbosity set_global_random_seed BLEUMetrics main FEDMetrics parse_args main parse_args main parse_args evaluate_parser load_parser develop_parser train_parser backend_parser logging_parser save_parser checkpoint_root redirect_cache_root transact serving_root TestSaveLoad TestTrain cache_root_dir TestEvaluate main main main parse_args main parse_args addoption skip add_marker partialmethod int32 minimum exp get_closest_values arange safe_divide argmin abs unique tobytes itemsize assert_array_equal get_closest_values array arange array bleu assert_array_almost_equal BLEUCalculator bleu assert_array_almost_equal _modified_precision BLEUCalculator assert_array_almost_equal _modified_precision BLEUCalculator bleu assert_array_almost_equal BLEUCalculator bleu fuzz_smoothing assert_array_almost_equal BLEUCalculator assert_has_calls BCE generator_loss embedding_matrix reduce_mean score_word_vector tensordot float32 namedtuple idx SpecialTokenConfig INT_DTYPE int32 max Counter len preprocess partialmethod print UttutPreprocessor PathNamespace isinstance LookUpCall add_argument_group ArgumentParser _add_action CallbackCreator CallbackList summary print enumerate print dict OutputEmbedding from_weights Dense vocab_size load_pretrained_embeddings LookAhead WeightDecay append optimizer_cls GradientClipping build_graph TokenSequence create_trainer GeneratorUpdater placeholder int32 creator_cls add_argument_group set_defaults ArgumentParser cumprod cast int8 dtype cast reduce_sum average reduce_mean ExponentialMovingAverage apply shape argmax epsilon random_uniform matmul _get_discount_matrix gradients matmul _get_discount_matrix power constant arange tril matmul square reduce_sum isinstance dtype name tuple match cast ndims ApplyMask layer ones assert_array_equal variables_initializer MaskConv1D _keras_mask sequence_mask ones assert_array_almost_equal variables layer run assert_array_equal _keras_mask sequence_mask assert_array_almost_equal MaskAveragePooling1D layer run MaskMaxPooling1D _keras_mask sequence_mask assert_array_almost_equal assert_array_equal layer run sequence_mask assert_array_almost_equal MaskGlobalMaxPooling1D array layer run assert_array_equal apply_mask constant run ones apply_mask Conv1DTranspose Conv1DTranspose dconv1d variables_initializer dconv1d Conv1DTranspose assert_array_almost_equal sum run Conv1DTranspose Conv1DTranspose placeholder dconv1d Embedding embed_layer value seq variables_initializer _keras_mask value isinstance Embedding Sequential logical_and vocab_size randint isin run variables_initializer value Embedding embed_layer placeholder vocab_size randint run variables_initializer from_weights embed_layer embeddings assert_array_almost_equal array run variables_initializer value minimize from_weights embed_layer astype float32 reduce_sum total_embeddings assert_array_almost_equal run variables_initializer value convert_variables_to_constants name from_weights embed_layer astype float32 create_session_from_graphdef choice vocab_size assert_array_almost_equal get_tensor_by_name run Session dense zeros embedder OutputEmbedding zeros GRUCell cell get_initial_state zeros get_initial_state skip_cells SkipConnectCells variables_initializer ones get_initial_state placeholder ZoneoutWrapper zip variables bool wrapped_cell run ones get_initial_state ZoneoutWrapper LSTMCell wrapped_cell zeros layer ones zeros layer constant network assert_array_almost_equal array run constant network Sequential sequential Graph variables Sequential sequential_with_masking ones Sequential sequential_supports_mask Sequential sequential_supports_mask minimum variables_initializer minimize Variable GradientDescentOptimizer assert_almost_equal GradientClipping run minimum variables_initializer norm minimize Variable GradientDescentOptimizer assert_array_almost_equal array GradientClipping l2_loss run minimum variables_initializer minimize Variable GradientDescentOptimizer sqrt assert_array_almost_equal sum GradientClipping l2_loss run variables_initializer LookAhead get_collection GLOBAL_VARIABLES GradientDescentOptimizer assert_almost_equal range run variables_initializer RAdamOptimizer get_collection GLOBAL_VARIABLES assert_almost_equal range run variables_initializer minimize Variable GradientDescentOptimizer pow WeightDecay assert_almost_equal run variables_initializer minimize Variable GradientDescentOptimizer pow WeightDecay assert_almost_equal run embedding_lookup constant initializer minimize Variable GradientDescentOptimizer pow WeightDecay assert_array_almost_equal array run takewhile_mask eval constant assert_array_almost_equal constant print Counter eval random_choice_by_logits assert_array_equal eval constant model suffix_sum_with_gradients choice assert_array_almost_equal global_variables_initializer random_normal run asarray equal len ndarray isinstance choice object ObjectWrapper setattr getattr cache_clear items list list max map len get_args args hasattr count_lines permutation range len print_body print_footer partial warn print_header assert_array_equal get_seqlens Mock Mock Mock D Mock ExponentialMovingAverageMeter write A B write listdir ERROR set_verbosity print seed set_random_seed ConfigProto ON_1 print load update fed FEDMetrics calculate print debug tokenizer set_package_verbosity preprocess append bleu BLEUMetrics add_argument ArgumentParser add_argument_group add_argument ArgumentParser add_argument_group set_defaults add_argument ArgumentParser add_argument_group add_argument ArgumentParser add_argument_group add_argument ArgumentParser add_argument_group add_argument ArgumentParser add_argument_group add_argument ArgumentParser add_argument_group add_argument ArgumentParser str reset_default_graph collect get_subdir_if_unique_base_tag_exists __dict__ path parse_args | # TaylorGAN Source code of our NeurIPS 2020 poster paper *TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation* [Paper](https://neurips.cc/virtual/2020/public/poster_e1fc9c082df6cfff8cbcfff2b5a722ef.html) | [arXiv (including appendix)](https://arxiv.org/abs/2011.13527) ## Setup ### Environment ```bash cp .env.sample .env ``` modify the `CHECKPOINT_DIR`, `DISK_CACHE_DIR`, `TENSORBOARD_PORT`, `TENSORBOARD_LOGDIR` as you need. ### Datasets | 735 |
MnMaxon/VR-Gesture-Detection | ['unity'] | ['Unity: A General Platform for Intelligent Agents'] | QuickDraw-master/MyQD.py QuickDraw-master/QuickDrawApp.py gym-unity/tests/test_gym.py gym-unity/gym_unity/__init__.py QuickDraw-master/LoadData.py gym-unity/gym_unity/envs/__init__.py gym-unity/setup.py gym-unity/gym_unity/envs/unity_env.py QuickDraw-master/QD_trainer.py UnityGymException UnityEnv test_gym_wrapper test_multi_agent load_data keras_predict top_predict keras_process_image stars_to_ar main keras_model augmentData prepress_labels loadFromPickle main blend_transparent keras_predict get_QD_emojis overlay keras_process_image main sample step MockCommunicator UnityEnv step MockCommunicator UnityEnv load print reshape astype append range listen accept stars_to_ar send Session str socket set_session load_model keras_predict getcwd AF_INET sleep encode SOCK_STREAM bind close ConfigProto recv print top_predict array split append str print index keras_process_image shape max range len str list print sort index keras_process_image shape append max reshape array compile Sequential add Dense MaxPooling2D Conv2D ModelCheckpoint Flatten Dropout append to_categorical keras_model reshape prepress_labels shuffle save loadFromPickle train_test_split print_summary fit VideoCapture bitwise_and COLOR_BGR2HSV boundingRect max medianBlur get_QD_emojis ones overlay morphologyEx minEnclosingCircle circle waitKey imshow range appendleft COLOR_BGR2GRAY dilate deque cvtColor GaussianBlur flip moments int read uint8 isOpened line erode contourArea zeros inRange MORPH_OPEN len resize str print append imread listdir range len blend_transparent resize COLOR_GRAY2BGR cvtColor | <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 | 736 |
ModarTensai/gaussian-regularizer | ['data augmentation'] | ['Analytical Moment Regularizer for Gaussian Robust Networks'] | main.py vis.py cli.py Config show dump all file gpu_args main run VGG16GNM GNM AlexNetGNM Trainer subparser LeNetGNM main maybe_number get_experiments run results name plot_dataframe aug_vs_exp print_results print debug option print print print print print device_count product experiment isdecimal experiment experiment multiple filter enumerate startswith summary gpu get_experiments run add_argument add_parser show isinstance plot name print test accuracies legend sigmas robustness_plot config list log_dir product isinstance OrderedDict filter any print_results append type show plot table set_edgecolor legend values extract sorted namedtuple all_results plot_dataframe accuracy set robustness res append | # Gaussian Regularizer Despite the impressive performance of deep neural networks (DNNs) on numerous vision tasks, they still exhibit yet-to-understand uncouth behaviours. One puzzling behaviour is the subtle sensitive reaction of DNNs to various noise attacks. Such a nuisance has strengthened the line of research around developing and training noise-robust networks. In this work, we propose a new training regularizer that aims to minimize the probabilistic expected training loss of a DNN subject to a generic Gaussian input. We provide an efficient and simple approach to approximate such a regularizer for arbitrary deep networks. This is done by leveraging the analytic expression of the output mean of a shallow neural network; avoiding the need for the memory and computationally expensive data augmentation. We conduct extensive experiments on LeNet and AlexNet on various datasets including MNIST, CIFAR10, and CIFAR100 demonstrating the effectiveness of our proposed regularizer. In particular, we show that networks that are trained with the proposed regularizer benefit from a boost in robustness equivalent to performing 3-21 folds of data augmentation. ### Installation All the requirements are listed in [requirements.txt](./requirements.txt). ```shell pip install -r requirements.txt python main.py --help ``` The training logs for the experiments reported in the paper are all published under releases as a single zip file. ### Cite | 737 |
ModelBunker/MORAN-PyTorch | ['scene text recognition'] | ['A Multi-Object Rectified Attention Network for Scene Text Recognition'] | tools/dataset.py demo.py models/moran.py models/morn.py test.py models/asrn_res.py inference.py tools/utils.py main.py models/fracPickup.py Recognizer val trainBatch ResNet BidirectionalLSTM ASRN AttentionCell Attention Residual_block fracPickup MORAN MORN lmdbDataset randomSequentialSampler resizeNormalize averager loadData strLabelConverterForAttention get_torch_version data decode DataLoader max view add iter append encode next range cat BidirDecoder averager loadData size MORAN zip float criterion print min len criterion backward loadData MORAN zero_grad step encode next cat BidirDecoder copy_ get_torch_version split | # MORAN: A Multi-Object Rectified Attention Network for Scene Text Recognition  | <center>Python 2.7</center> | <center>Python 3.6</center> | | :---: | :---: | | <center>[](https://travis-ci.org/Canjie-Luo/MORAN_v2)</center> | <center>[](https://travis-ci.org/Canjie-Luo/MORAN_v2)</center> | MORAN is a network with rectification mechanism for general scene text recognition. The paper (accepted to appear in Pattern Recognition, 2019) in [arXiv](https://arxiv.org/abs/1901.03003), [final](https://www.sciencedirect.com/science/article/pii/S0031320319300263) version is available now. [Here is a brief introduction in Chinese.](https://mp.weixin.qq.com/s/XbT_t_9C__KdyCCw8CGDVA)  ## Recent Update - 2019.03.21 Fix a bug about Fractional Pickup. | 738 |
MohdDanish/FastPhotoStyle1 | ['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)   ## FastPhotoStyle ### License Copyright (C) 2018 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). <img src="https://raw.githubusercontent.com/NVIDIA/FastPhotoStyle/master/teaser.png" width="800" title="Teaser results"> ### What's new | 739 |
MohdElgaar/DialogWAE | ['response generation'] | ['DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder'] | metrics.py dataset.py hparams.py utils.py main.py model.py MyDataset Metrics PriorNet DialogWAE RecognitionNet mean to_string | # DialogWAE Implementation of Xiaodong Gu, Kyunghyun Cho, Jung-Woo Ha, Sunghun Kim, _DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder_, Published as a conference paper at ICLR 2019 https://arxiv.org/pdf/1805.12352.pdf ### Data http://yanran.li/files/ijcnlp_dailydialog.zip ### Embedding Weights http://nlp.stanford.edu/data/glove.twitter.27B.zip ## Implementation Notes In the IPython notebook: | 740 |
MonashTS/UCR-Monash | ['time series analysis', 'time series', 'dynamic time warping'] | ['Early Abandoning PrunedDTW and its application to similarity search'] | main.py print_info read_all sum_tuple get_file_paths slower_than list tuple append range len int join items setdefault name print get_file_paths float keys split str print min append float max print | # UCR-Monash Monash UCR suite implementation showcasing EAPrunedDTW. This page support our paper **Early Abandoning PrunedDTW and its application to similarity search** available on arxiv https://arxiv.org/abs/2010.05371 (submitted to SDM2021). We developed EAPruneDTW, a version of PrunedDTW suited to early abandon. You can find the source code of PruneDTW on its website http://sites.labic.icmc.usp.br/prunedDTW/ . PrunedDTW was later use in the UCR suite ( **Searching and Mining Trillions ofTime SeriesSubsequencesunder Dynamic Time Warping** https://www.cs.ucr.edu/~eamonn/UCRsuite.html ), leading to the UCR USP suite (**Speeding Up Similarity Search Under Dynamic Time Warping by Pruning Unpromising Alignments** https://sites.google.com/view/ucruspsuite ). We implemented EAPrunedDTW in the UCR suite, leading to our own UCR MON suite. To be clear, the UCR USP and UCR MON suites are directly derived from UCR suite: the code is the same except for the implementation of the dtw function. | 741 |
MoonBlvd/Detection-of-Traffic-Anomaly | ['anomaly detection'] | ['When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos'] | dataset/dota.py dataset/video2frames.py dataset/download_DoTA.py dataloader_example.py config/config.py parse_args visualize_config DoTADataset main download_videos extract_clips videos_to_frames extract_frames_from_videos print items sorted format config_file add_argument parse_known_args ArgumentParser visualize_config download_dir print makedirs readlines cookiefile download YoutubeDL download_videos sorted join glob download_dir print add_argument ArgumentParser parse_args len join str read print out_dir encode fps makedirs load join sorted format zip move glob print len makedirs zfill rmtree anno_dir out_dir append enumerate open extract_clips videos_to_frames | # When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos Yu Yao, Xizi Wang, Mingze Xu, Zelin Pu, Ella Atkins, David Crandall <p align="center"><img width="100%" src="img/DoTA.png"/></p> :boom: This repo contains the **Detection of Traffic Anomaly (DoTA) dataset** and the **code** of our [paper](https://arxiv.org/pdf/2004.03044.pdf). ## DoTA dataset **UPDATE 11/13/2021:** Due to high ratio of lost video URL, we have connected with the original channel authors to get the authority to share all the video clips we downloaded and extracted. Please find the download link [here](https://drive.google.com/file/d/1RQp4hOP9X7TW6S3_vbqZvFSkrbbFzrRj/view?usp=sharing). After downloading frames from the above link, users can skip the datadownloading and frame extraction steps below. Install `ffmpeg`. Install python dependencies by `pip install -r requirements.txt`. Get your youtube cookies to be able to download age restricted videos. Please find how to generate cookie based on this [github issue](https://github.com/blackjack4494/yt-dlc/issues/7). Chrome users can use [this app](https://chrome.google.com/webstore/detail/get-cookiestxt/bgaddhkoddajcdgocldbbfleckgcbcid) to generate cookie file. First, download the orginal videos from YouTube: | 742 |
Morpheus3000/intrinseg | ['intrinsic image decomposition', 'semantic segmentation'] | ['Joint Learning of Intrinsic Images and Semantic Segmentation'] | direct_intrinsics_sn.py infer.py utils.py DirectIntrinsicsSN main set_experiment create_folder write_image TrimBot ExperimentType Cuda Garden network create_image combined print ExperimentType segmentation intrinsics split data resize color_labels cuda max open write_image transpose from_numpy load_state_dict expand_dims enabled glob astype eval Cuda net DirectIntrinsicsSN join load time isdir ANTIALIAS print Variable float32 tqdm numpy array len makedirs fromarray uint8 transpose squeeze astype join create_folder dirname save create_image | ## Joint Learning of Intrinsic Images and Semantic Segmentation We provide the inference code for our paper titled Joint Learning of Intrinsic Images and Semantic Segmentation, ECCV 2018. The model is based on the architecture of 'Learning Non-Lambertian Object Intrinsics across ShapeNet Categories' by Shi et. CVPR17. The scripts have been tested on PyTorch 1.0. ## Semantic Labels ``` 0. void 1. ground 2. grass 3. dirt | 743 |
MortenHannemose/pytorch-vfi-cft | ['camera calibration'] | ['Superaccurate Camera Calibration via Inverse Rendering'] | simple_example.py loss_functions.py network.py slow_movie.py continuous_fine_tune.py utils.py CFT Cyclic SMLoss VGG Network SlowMovie FrameHandler clear_dir get_device load_model VideoCapture CAP_PROP_FRAME_HEIGHT put resize CAP_PROP_FPS CAP_PROP_FRAME_COUNT max list FrameHandler range get format finetune_4 Queue mkdir compute_inbetween_frames CAP_PROP_FRAME_WIDTH int read print system tqdm split clear_dir write_frame load CFT endswith get_device abspath device load_state_dict to Network join remove listdir isfile | MortenHannemose/pytorch-vfi-cft | 744 |
MotohisaFukuda/CROP | ['time series', 'semantic segmentation'] | ['Central object segmentation by deep learning for fruits and other roundish objects'] | source.py Up UNetConvDeep7 ProcessingSinglePic crop_image_raw Down open_image find_pics ProcessingMultiplePics SegmentingImage RotatingImage ProcessingImage ConvDoubled AnalyzingImage crop_image show imshow urlopen convert show resized_crop imshow show imshow crop axis print sorted enumerate | # Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images **CROP(Central Roundish Object Painter)**, which is a deepr version of **U-Net**, identifies and paints the central roundish objects in RGB images in the varied light conditions. **CROP** was trained solely by 172 images of fruits but gained somewhat general ability to work for the other types of objects. Applying **CROP** to time series fruit images captured by fixed cameras will give the growth curves of fruits. Read our paper: [Sensors 2021, 21(21), 6999](https://doi.org/10.3390/s21216999) ## Works for various fruits. <img src="images/murayama35a.png" width="33%" /><img src="images/murayama21a.png" width="33%" /><img src="images/murayama45.png" width="33%"/> <i>The photos with no masks (left, 512×512 pixels) are the inputs and the ones with masks (right, 512×512 pixels) are the outputs. **CROP** can identify and process the central roundish fruit of various kinds and colors. These examples are independent of the training process. Photo credit: Hideki Murayama.</i> ## Achieved generality. <img src="images/coffee10.png" width="25%" /><img src="images/bread2.png" width="25%" /><img src="images/okuno_ball1556.png" width="25%"/><img src="images/stone_br6.png" width="25%"/> <i>It can process organic or non-organic roundish objects. The photos with no masks (left, 512×512 pixels) were the inputs and the ones with masks (right, 512×512 pixels) were the outputs. These examples are independent of the training process.</i> | 745 |
MrGranddy/EfficientSeg | ['semantic segmentation'] | ['EfficientSeg: An Efficient Semantic Segmentation Network'] | train_statistics.py helpers/helpers.py Minicity_train.py helpers/minicity.py evaluate_flip.py helpers/csHelpers.py helpers/model.py helpers/labels.py baseline.py model.py train_trans validate_epoch train_epoch new_gen_train_trans train_trans_alt weight_init adjust_learning_rate test_trans main vislbl visim predict validate_epoch test_trans MiniCity_train conv_1x1_bn h_swish conv_3x3_bn h_sigmoid EfficientSeg InvertedResidual _make_divisible SELayer train_trans getCoreImageFileName printError getCsFileInfo getColorEntry getDirectory ensurePath writeDict2JSON writeDict2Txt colors printError AverageMeter ProgressMeter getScoreAverage iouCalc assureSingleInstanceName MiniCity outconv up double_conv UNet down inconv param_groups lr_init epochs kaiming_uniform weight EfficientSeg arange subplots mask_colors tuple grid warn dataset_path DataLoader save cuda test_size seed str set_xlabel train_size MiniCity_train Adam apply device_count validClasses twinx load_state_dict legend savefig append parse_args sum CrossEntropyLoss predict range format plot validate_epoch tight_layout manual_seed is_available classLabels get_device_name load time print weights train_epoch parameters set_ylabel epochs MiniCity makedirs create_an_epoch time param_groups AverageMeter DataLoader ProgressMeter train len AverageMeter eval ProgressMeter iouCalc len len eval AverageMeter ProgressMeter test_size uint8 dataset_std dataset_mean from_numpy array resize normalize to_tensor hflip resize to_pil_image train_size dataset_mean crop_size from_numpy uniform array normalize colorjitter_factor to_tensor adjust_saturation dataset_std uint8 affine adjust_hue adjust_brightness randint adjust_contrast scale_factor uint8 normalize adjust_saturation adjust_hue train_size hflip dataset_std dataset_mean choice from_numpy uniform adjust_contrast adjust_brightness array resize randint to_tensor to_pil_image img_transforms Sequential resize fromarray train_size dataset_mean from_numpy shape JpegCompression normalize to_tensor MultiplyHue dataset_std choice flip uint8 randint array MultiplyBrightness cpu transpose astype cpu numpy array cuda list id classes unique append zeros array int max print str exit format basename printError CsFile split len getCsFileInfo dirname makedirs list id classes unique append zeros array | EfficientSeg: An Efficient Semantic Segmentation Network EfficientSeg is a segmentation network using Mobilev3 blocks inside a U-shaped network structure. Using this network and the training procedure we have obtained 58.1% mIoU on Minicity test (A subset of Cityscapes) set where the baseline U-Net score was 40%. How to run: - Insert Minicty train and val images under Minicity directory. To use, train+val in training and test set for testing, you can add both sets under train. - You can obtain the weights of the best model from : https://drive.google.com/file/d/1pZrv-LjPg3VhsU5w5MygyLHz6lCm2uvP/view - Using evaluate_flip.py, you can evaluate the model. - If you have any questions feel free to ask us via [email protected] - The paper can be found here: https://arxiv.org/abs/2009.06469 | 746 |
Mraksu/Lottery-Ticket | ['network pruning'] | ['The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks'] | load_MNIST.py used_func.py model.py load_MNIST pruned_nn pruning_rounds encode_save_json parameter_count prune_network define_pruning_params decode_json list format print reshape to_categorical astype float32 set shape load_data len Sequential add prune_low_magnitude Dense compile print format numpy trainable_weights format print copy append numpy range int layers sort reshape astype where flatten shape zeros abs set_weights items print tolist keys exists dict items keys array | # Implementation of the Master Thesis: Identifying Winning Lottery Tickets in Small Neural Network Architectures by Murat Aksu Experiments notebook can be used to reproduce the results (using the same random seed). Also you can modify parameters and make your own experiments as well. In figures notebook you can find the code the produce the figures in the paper. ### Requirements - Python 3.7 - Tensorflow 2.1 - Keras 2.3 - Tensorflow Model Optimization API ### Acknowledgement | 747 |
Mufabo/ICASSP2020_tutorial_6 | ['person identification'] | ['Robust M-Estimation Based Bayesian Cluster Enumeration for Real Elliptically Symmetric Distributions'] | icassp20_T6/functions/data_31.py icassp20_T6/functions/psi_tukey.py icassp20_T6/SI_breakdown.py icassp20_T6/functions/plot_scatter.py icassp20_T6/functions/.ipynb_checkpoints/data_31-checkpoint.py icassp20_T6/functions/psi_huber2.py icassp20_T6/functions/BIC_S.py icassp20_T6/functions/g_gaus.py icassp20_T6/functions/mahalanobisDistance.py icassp20_T6/functions/rho_tukey.py icassp20_T6/functions/kmedians.py icassp20_T6/functions/rho_t.py icassp20_T6/functions/eta_gaus.py icassp20_T6/SI_BIC.py icassp20_T6/functions/BIC_F.py icassp20_T6/functions/eta_t.py icassp20_T6/functions/rho_huber2.py icassp20_T6/functions/matlab_helpers.py icassp20_T6/SI_sensitivity.py icassp20_T6/functions/psi_gaus.py icassp20_T6/functions/nearestSPD.py icassp20_T6/functions/g_huber2.py icassp20_T6/functions/psi_t.py icassp20_T6/functions/duplicationMatrix.py icassp20_T6/functions/rho_gaus.py icassp20_T6/functions/eta_tukey.py icassp20_T6/functions/BIC_A.py icassp20_T6/functions/EM_RES.py icassp20_T6/SI_EM.py icassp20_T6/__init__.py icassp20_T6/SI_simple_example.py setup.py icassp20_T6/functions/untitled1.py icassp20_T6/functions/eta_huber2.py icassp20_T6/functions/FIM_RES.py icassp20_T6/functions/g_t.py read fun fun BIC_A BIC_F BIC_S data_31 duplicationMatrix EM_RES eta_huber2 eta_tukey FIM_RES g_gaus g_huber2 g_t kmedians mahalanobisDistance nearestSPD plot_scatter psi_huber2 psi_t psi_tukey rho_gaus rho_huber2 rho_t rho_tukey data_31 EM_RES BIC_F BIC_S prange BIC_A zeros range eta det range shape psi array rho zeros sum max log det abs warn FIM_RES range shape pi rho duplicationMatrix zeros sum log det range shape rho zeros sum log int hstack rand zeros round array range zeros range int reshape g kmeans warn psi get_clusters log initialize all get_total_wce shape process sum range MANHATTAN distance_metric cond det T mahalanobisDistance eye zeros get_centers array diag len ppf sqrt cdf zeros len zeros len eta T hstack mldivide solve kron nearestSPD psi vstack eye zeros sum range len exp pi ppf exp igamma pi sqrt cdf zeros gamma len sum initialize zeros abs range svd T min shape eigvals diag show xlabel set_xlabel add_subplot ylabel scatter set_ylabel figure set_zlabel zeros sum range ppf sqrt cdf zeros len zeros len ppf exp igamma pi sqrt cdf zeros gamma log len zeros log pi len vstack | This package provides the Python Code for the paper "Robust M-Estimation based Bayesian Cluster Enumeration for Real Elliptically Symmetric Distributions" by Christian A. Schroth and Michael Muma. The theory can be found in http://arxiv.org/abs/2005.01404. # Installation Run the following in a terminal. ```pip install git+https://github.com/Mufabo/ICASSP2020_tutorial_6.git``` # Examples A jupyter notebook with examples can be found on google colab: https://colab.research.google.com/drive/18FRP5Xn5ILBOHQFFLHzXUs2gorDukrMR#scrollTo=aXLylfruAlWK | 748 |
MuhamedKamil/Neural-Style-Transfer- | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | NeuralStyleMain.py generate_noise_image ComputeTotalLoss Preprocessing_input_Vgg19 deprocess_img clip_0_1 View_Images Training_Neural_Style_Transfer compute_grads Compute_Cost_ContentImg gram_matrix ReadImage Compute_Cost_StyleImg_Per_one_Layer get_model ResizeImage VGG19 squeeze astype copy randn as_list as_list reshape transpose matmul as_list gram_matrix float zip model show add_subplot imshow title figure expand_dims preprocess_input img_to_array format layers deprocess_img Variable print compute_grads AdamOptimizer assign apply_gradients clip_by_value get_model numpy array range | # Neural-Style-Transfer-Deep Learning using TensorFlow Neural style transfer (NST) is an optimization technique used to take images, a content image, a style reference image (such as an artwork by a famous painter), and the input image you want to style — and blend them together such that the input image is transformed to look like the content image, but “painted” in the style of the style image. # Output Aftar Training Process # First Example  # Second Example  # Training Process   | 749 |
Mushfequr-Rahman/Omniglot_baselines | ['one shot learning'] | ['Deep Triplet Ranking Networks for One-Shot Recognition'] | make_imagenet_dataset.py model.py make_omniglot_dataset.py makeTriplet read_folder split_list readModelCSV main main readModelCSV split_list makeTriplet Triplet Siamese glob join append print len print append len print len str join isfile COLOR_BGR2RGB print makeTriplet split_list imshow readModelCSV resize append imread range cvtColor len int listdir | **A journey into few shot learning** **By:** Mohammad Mushfequr Rahman **Email:** [email protected] The fewshot learning problems arises when the data you have is not enough to train a network. One such dataset that we have is the Omniglot dataset. it has nearly 964 characters from 50 different languages with roughly only 20 images for each class. As you can see, this isn't nearly enough datapoints to train a traditional feedforward network. So we must explore other alternatives that will give us acceptable accuracy. Two ways we can do this is: 1. Augment the data to increase datapoints i.e Siamese and Triplet Networks 2. Meta Learning i.e Pretrain the Network in such a way that we can get convergence on the new task by training the network on a few new datapoints. Initially we explored Siamese and Triplet Networks on the Omniglot dataset to see how would they do while training over a few iterations. **Siamese Networks:** This type of architecture works by taking in a pair input images and outputs a binary prediction value on whether the two images belong to the same class or not. The image below can help you visulaize how the network works:  | 750 |
Muzammal-Naseer/SAT | ['adversarial defense'] | ['Stylized Adversarial Defense'] | test_roa.py test_common_corruptions.py networks/wideresnet.py test.py attack.py ROA/ROA.py local_attack CWLoss BasicBlock NetworkBlock WideResNet ROA data criterion backward model min grad sign mean clamp_ zero_ uniform_ abs max range detach | # SAT: Stylized Adversarial Training [Muzammal Naseer](https://scholar.google.ch/citations?user=tM9xKA8AAAAJ&hl=en), [Salman Khan](https://scholar.google.com/citations?user=M59O9lkAAAAJ&hl=en), [Munawar Hayat](https://scholar.google.ch/citations?user=Mx8MbWYAAAAJ&hl=en&oi=ao), [Fahad Shahbaz Khan](https://scholar.google.ch/citations?user=zvaeYnUAAAAJ&hl=en&oi=ao), and [Fatih Porikli](https://scholar.google.com/citations?user=VpB8NZ8AAAAJ&hl=en) **Paper**: https://arxiv.org/abs/2007.14672 > **Abstract:** *Deep Convolution Neural Networks (CNNs) can easily be fooled by subtle, imperceptible changes to the input images. To address this vulnerability, adversarial training creates perturbation patterns and includes them in the training set to robustify the model. In contrast to existing adversarial training methods that only use class-boundary information (e.g., using a cross entropy loss), we propose to exploit additional information from the feature space to craft stronger adversaries that are in turn used to learn a robust model. Specifically, we use the style and content information of the target sample from another class, alongside its class boundary information to create adversarial perturbations. We apply our proposed multi-task objective in a deeply supervised manner, extracting multi-scale feature knowledge to create maximally separating adversaries. Subsequently, we propose a max-margin adversarial training approach that minimizes the distance between source image and its adversary and maximizes the distance between the adversary and the target image. Our adversarial training approach demonstrates strong robustness compared to state of the art defenses, generalizes well to naturally occurring corruptions and data distributional shifts, and retains the model accuracy on clean examples.* > ## Citation If you find our work, this repository and pretrained model useful. Please consider giving a star :star: and citation. ```bibtex @InProceedings{naseer2020stylized, title={Stylized Adversarial Defense}, | 751 |
MyChocer/KGTN | ['few shot learning'] | ['Knowledge Graph Transfer Network for Few-Shot Recognition'] | FeatureExtractor/save_features.py FeatureExtractor/main.py FeatureExtractor/losses.py FeatureExtractor/myMetaDataset.py KGTN/main_KGTN.py FeatureExtractor/ResNetBasic.py KGTN/util.py FeatureExtractor/additional_transforms.py parse_results.py FeatureExtractor/data.py FeatureExtractor/ResNetFeat.py KGTN/KGTN.py parse_args ImageJitter get_data_loader parse_transform SGMLoss get_one_hot BatchSGMLoss GenericLoss l2_loss main_training_loop accuracy adjust_learning_rate isfile parse_args get_model get_resume_file MetaDataset ResNet SimpleBlock ResNet18 BottleneckBlock ResNet34 ResNet50 init_layer ResNet10 ResNet101 ResNetFeat ResNet18 ResNet34 ResNet50 ResNet10 ResNet101 save_features get_model parse_args KGTM KGTN SimpleHDF5Dataset perelement_accuracy get_test_loader eval_loop_step LowShotDataset training_loop parse_args process_adjacent_matrix add_argument ArgumentParser getattr ImageJitter DataLoader Compose float size cuda expand sum topk numpy step_size param_groups lr_decay warmup_lr lr checkpoint_dir zero_grad SGD adjust_learning_rate save cuda range format eval lr item float enumerate join time backward Variable print makedirs parameters loss_fn train step len dict join checkpoint_dir max format glob isfile array fill_ isinstance out_channels Conv2d normal_ sqrt BatchNorm2d float format batch_size print Variable model File size close create_dataset numpy cuda enumerate len SimpleHDF5Dataset DataLoader model zero_grad featdim SGD cuda range CrossEntropyLoss get_sample format lr item backward l2_reg print parameters filter loss_function train step sum topk numpy data format model print perelement_accuracy mean eval softmax in1d cuda enumerate load int ones reshape rand min eye len | # Knowledge Graph Transfer Network for Few-Shot Recognition This is the implementation code of [KGTN paper](https://arxiv.org/pdf/1911.09579.pdf): ```bash @inproceedings{chen2020knowledge, title={Knowledge Graph Transfer Network for Few-Shot Recognition.}, author={Chen, Riquan and Chen, Tianshui and Hui, Xiaolu and Wu, Hefeng and Li, Guanbin and Lin, Liang}, booktitle={AAAI}, pages={10575--10582}, year={2020} | 752 |
N0vel/weighted-hausdorff-distance-tensorflow-keras-loss | ['object localization'] | ['Locating Objects Without Bounding Boxes'] | weighted_hausdorff_loss.py Weighted_Hausdorff_loss tf_repeat where tf_repeat clip_by_value squeeze transpose reduce_sum matmul cast append range sqrt stack reshape float32 maximum reduce_mean bool reduce_min Print | # Weighted Hausdorff Distance Tensorflow/Keras loss Weighted Hausdorff Distance Loss: use it as a point cloud similarity metric based loss for keras and tf. Useful in keypoint detection. ### Attention This loss requires a huge tensor with dimensions (number_of_pixels * number_of_keypoints if I remember correctly) of float values. So high res picture with thousands of keypoints will consume A LOT of GPU memory (at least 1 GB for 512 pixels x 512 pixels x 1000 keypoints with float32 type). 1024x1024x2000 will eat 8 GB. It doesn't matter if you want to detect only several points in an image. https://arxiv.org/pdf/1806.07564.pdf | 753 |
N950/edge-segnet | ['semantic segmentation'] | ['EdgeSegNet: A Compact Network for Semantic Segmentation'] | NetworkModules.py CamSeqDataset.py EdgeSegNet.py train.py CamSeqDataset Util EdgeSegNet BilinearResizeModule ResidualBottleneckModule RefineModule BottleneckReductionModule train_model array device data criterion model backward step zero_grad tqdm set_description long set_postfix item append train round max range | # EdgeSegNet - Pytorch
An implementation of [EdgeSegNet](https://arxiv.org/abs/1905.04222) in Pytorch,
[CamSeg01](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamSeq01/) dataset is used for training
Repo containes :
* [EdgeSegNet.ipynb](https://github.com/N950/edge-segnet/blob/master/EdgeSegNet.py) Notebook taking care of downloading the dataset, training, plotting and prediction examples.
* [train.py](https://github.com/N950/edge-segnet/blob/master/train.py) entry point script which does all the downloading and setting up of the dataset, training and plotting graphs and prediction example
| 754 |
NIC-VICOROB/cnn-ms-lesion-segmentation | ['lesion segmentation'] | ['Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach'] | data_utils.py metrics.py build_model_nolearn.py base.py config.py train_leave_one_out.py DSC rtpf ftpf vol_dif logical_and bool sum astype bool astype | # Multiple Sclerosis (MS) lesion segmentation of MRI images using a cascade of two 3D convolutional neural networks This repository implements the method proposed in the [NeuroImage publication](https://doi.org/10.1016/j.neuroimage.2017.04.034), but an electronic preprint is available from [Arxiv](http://arxiv.org/abs/1702.04869): ``` Valverde, S., Cabezas, M., Roura, E., González-Villà, S., Pareto, D., Vilanova, J. C., … Lladó, X. (2017). Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. NeuroImage, 155, 159–168. https://doi.org/10.1016/j.neuroimage.2017.04.034 ``` # Overview: Convolutional neural networks (CNN) are being increasingly used in brain MRI image analysis for segmentation of brain tumors, tissue or pathological lesions. We have developed a novel Multiple Sclerosis (MS) white matter (WM) lesion segmentation method based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is trained to be more sensitive revealing possible candidate lesion voxels while the second network is trained to reduce the number of misclassified voxels coming from the first network. This cascaded CNN architecture tends to learn well from small sets of training data, which can be very interesting in practice, given the difficulty to obtain manual label annotations and the large amount of available unlabeled MRI data. The method accepts a variable number of MRI image sequences for training (T1-w, FLAIR, PD-w, T2-w, ...), which are stacked as channels into the model. However, so far, the same number of sequences have to be used for testing. In contrast to other proposed methods, the model is trained using two cascaded networks: for the first network, a balanced training dataset is generated using all positive examples (lesion voxels) and the same number of negative samples (non-lesion voxels), randomly sampled from the entire training voxel distribution. The first network is then used to find the most challenging examples of the entire training distribution, ie. non-lesion voxels which have being classified as lesion with a high probability. From this set of challenging voxels, the second CNN is trained using a new balanced dataset composed by again all positive examples and the same number of randomly sampled voxels from the set of challenging examples. | 755 |
NIC-VICOROB/tissue_segmentation_comparison | ['brain segmentation'] | ['Quantitative analysis of patch-based fully convolutional neural networks for tissue segmentation on brain magnetic resonance imaging'] | workflow/evaluate.py architectures/Kamnitsas.py utils/general_utils.py utils/reconstruction.py architectures/Guerrero.py utils/extraction.py utils/training_testing_utils.py architectures/Cicek.py architectures/Dolz.py configuration.py architectures/arch_creator.py utils/callbacks.py main.py utils/ioutils.py generate_model generate_unet_model get_conv_fc __generate_unet_model get_deconv_layer get_max_pooling_layer get_conv_core organise_output get_conv_fc generate_dolz_multi_model get_cropping_layer get_conv_core __generate_dolz_multi_model organise_output get_res_conv_core generate_uresnet_model get_conv_fc get_deconv_layer get_max_pooling_layer __generate_uresnet_model organise_output get_conv_fc get_cropping_layer generate_kamnitsas_model get_deconv_layer get_conv_core get_low_res_layer __generate_kamnitsas_model organise_output generate_callbacks generate_output_filename extract_patches pad_both_sides __save_volume read_dataset read_volume_data read_IBSR18_dataset read_volume save_volume_MICCAI2012 read_MICCAI2012_dataset save_volume read_iSeg2017_dataset reconstruct_volume generate_indexes perform_voting determine_label_selector build_training_set split_train_val build_testing_set __generate_unet_model compile int get_conv_fc concatenate get_deconv_layer get_max_pooling_layer get_conv_core Input organise_output __generate_dolz_multi_model compile get_conv_fc get_cropping_layer concatenate get_conv_core Input organise_output compile __generate_uresnet_model get_res_conv_core get_conv_fc add get_deconv_layer get_max_pooling_layer Input organise_output add __generate_kamnitsas_model compile get_conv_fc get_cropping_layer concatenate get_deconv_layer get_conv_core get_low_res_layer Input organise_output str EarlyStopping CSVLogger ModelCheckpoint generate_output_filename shape sk_extract_patches prod len str format read_volume zeros keys range zeros format range format multiply read_volume zeros enumerate str __save_volume format multiply read_volume_data str format __save_volume multiply read_volume_data shape zeros keys affine AnalyzeImage astype Nifti1Image save perform_voting zeros generate_indexes enumerate len ceil int32 len extract_patches to_categorical where flatten pad_both_sides vstack zeros determine_label_selector prod range len slice len | # Quantitative analysis of patch-based fully convolutional neural networks for tissue segmentation on brain magnetic resonance imaging This repository implements the evaluation framework proposed in one of our research papers: ``` Bernal, J., Kushibar, K., Cabezas, M., Valverde, S., Oliver, A., Lladó, X. (2017). "Quantitative Analysis of Patch-Based Fully Convolutional Neural Networks for Tissue Segmentation on Brain Magnetic Resonance Imaging." IEEE Access 7 (2019): 89986-90002. ``` ## Requirements ### Libraries The code has been tested with the following configuration - h5py == 2.7.0 - ipython == 5.3.0 | 756 |
NIRVANALAN/magnifiernet_reid | ['person re identification'] | ['A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification', 'MagnifierNet: Towards Semantic Adversary and Fusion for Person Re-identification'] | data/datasets/dataset_loader.py data/collate_batch.py utils/logger.py modeling/modules/MaskModule.py utils/re_ranking.py data/build.py data/datasets/veri.py layers/seg_loss.py modeling/modules/SegAttrModule.py modeling/backbones/__init__.py modeling/Tools.py data/datasets/__init__.py data/datasets/import_Market1501.py tests/lr_scheduler_test.py layers/__init__.py engine/mt_trainer.py utils/iotools.py tools/test.py modeling/__init__.py engine/trainer.py config/defaults.py layers/div_loss.py data/__init__.py data/datasets/import_DukeMTMC.py engine/inference.py layers/attr_loss.py modeling/backbones/resnet_ibn_a.py modeling/MT_Net.py data/datasets/import_DukeMTMCAttribute.py tests/__init__.py data/datasets/cuhk03-ori.py data/datasets/cuhk03_d.py tools/__init__.py layers/center_loss.py tools/test-mt.py data/datasets/market1501.py layers/triplet_loss.py tools/train.py solver/lr_scheduler.py utils/__init__.py data/transforms/build.py data/datasets/cuhk03_l.py solver/build.py data/datasets/bases.py solver/__init__.py data/datasets/cuhk03.py modeling/MT_Model.py tools/train-mt.py modeling/backbones/senet.py data/samplers/__init__.py data/datasets/import_Market1501Attribute.py data/transforms/transforms.py config/__init__.py modeling/modules/PartModule.py modeling/baseline.py modeling/backbones/resnet.py data/samplers/triplet_sampler.py utils/reid_metric.py data/transforms/__init__.py data/datasets/mt_dataset_loader.py modeling/modules/__init__.py data/datasets/msmt17.py data/datasets/dukemtmcreid.py data/datasets/eval_reid.py make_mt_data_loader make_data_loader val_collate_fn train_collate_fn mt_collate_fn BaseDataset BaseImageDataset BaseVideoDataset CUHK03 CUHK03 CUHK03_D CUHK03_L ImageDataset read_image DukeMTMCreID eval_func import_DukeMTMC import_DukeMTMCAttribute_binary import_DukeMTMCAttribute import_Market1501 import_Market1501Attribute_binary import_Market1501Attribute Market1501 MSMT17 MTImageDataset read_image VeRi get_names init_dataset RandomIdentitySampler RandomIdentitySampler_alignedreid build_transforms build_mt_transforms RandomErasing RandomErasing2 inference create_supervised_evaluator create_mt_supervised_trainer do_mt_train create_mt_supervised_evaluator create_supervised_trainer_with_center create_supervised_trainer do_train create_supervised_evaluator do_train_with_center CenterLoss Diverse_Loss PSLoss hard_example_mining euclidean_dist CrossEntropyLabelSmooth TripletLoss normalize make_loss_with_center make_loss make_mt_loss weights_init_classifier weights_init_kaiming Baseline MTModel MTNet weights_init_classifier PCBSplitter WeightedAvgPooling AttrClassBlockFc ClassBlock Pyramidal_Block_V2 AttrAttnBlockFc weights_init_classifier Pyramid_V2 weights_init_kaiming build_mt_model build_model ResNet resnet50 Bottleneck conv3x3 resnet34 resnet101 BasicBlock resnet152_ibn_a resnet50_ibn_a Bottleneck_IBN ResNet_IBN IBN resnet101_ibn_a SENet SEResNetBottleneck SEBottleneck SEResNeXtBottleneck Bottleneck SEModule MaskHead SegmentBatchDrop DoubleBatchDrop SegmentAttrPart PartAblationHead SegmentAllPart PartDistanceHead SegPred PartSegModule make_optimizer make_optimizer_with_center WarmupMultiStepLR MyTestCase main main main train main train check_isfile read_json write_json mkdir_if_missing setup_logger R1_mAP R1_mAP_reranking re_ranking make_mt_data_loader make_data_loader val_collate_fn train_collate_fn mt_collate_fn BaseDataset BaseImageDataset BaseVideoDataset CUHK03 CUHK03_D CUHK03_L ImageDataset read_image DukeMTMCreID eval_func import_DukeMTMC import_DukeMTMCAttribute_binary import_DukeMTMCAttribute import_Market1501 import_Market1501Attribute_binary import_Market1501Attribute Market1501 MSMT17 MTImageDataset read_image VeRi get_names init_dataset RandomIdentitySampler RandomIdentitySampler_alignedreid build_transforms build_mt_transforms RandomErasing RandomErasing2 inference create_supervised_evaluator create_mt_supervised_trainer do_mt_train create_mt_supervised_evaluator create_supervised_trainer_with_center create_supervised_trainer do_train create_supervised_evaluator do_train_with_center CenterLoss Diverse_Loss PSLoss hard_example_mining euclidean_dist CrossEntropyLabelSmooth TripletLoss normalize make_loss_with_center make_loss make_mt_loss weights_init_classifier weights_init_kaiming Baseline MTModel MTNet weights_init_classifier PCBSplitter WeightedAvgPooling AttrClassBlockFc ClassBlock Pyramidal_Block_V2 AttrAttnBlockFc Pyramid_V2 weights_init_kaiming build_mt_model build_model ResNet resnet50 Bottleneck conv3x3 resnet34 resnet101 BasicBlock resnet152_ibn_a resnet50_ibn_a Bottleneck_IBN ResNet_IBN IBN resnet101_ibn_a SENet SEResNetBottleneck SEBottleneck SEResNeXtBottleneck Bottleneck SEModule MaskHead SegmentBatchDrop DoubleBatchDrop SegmentAttrPart PartAblationHead SegmentAllPart PartDistanceHead SegPred PartSegModule make_optimizer make_optimizer_with_center WarmupMultiStepLR MyTestCase main train check_isfile read_json write_json mkdir_if_missing setup_logger R1_mAP R1_mAP_reranking re_ranking gallery NAMES init_dataset NUM_WORKERS query ImageDataset DataLoader num_train_pids build_transforms train gallery NAMES init_dataset build_mt_transforms NUM_WORKERS query ImageDataset DataLoader num_train_pids build_transforms train MTImageDataset tensor zip stack LongTensor zip zip convert invert format asarray print cumsum astype float32 argsort shape mean int32 append sum range join int print append listdir pop join list items ndarray isinstance print sort insert import_DukeMTMC append loadmat range len import_DukeMTMCAttribute join int print append listdir join remove items ndarray isinstance print sort import_Market1501 append loadmat range len pop print import_Market1501Attribute insert Compose Normalize Compose Normalize items Engine DataParallel to attach DEVICE format getLogger print RE_RANKING info create_supervised_evaluator run to DataParallel items Engine DataParallel to attach CHECKPOINT_PERIOD DEVICE Timer create_mt_supervised_evaluator getLogger print add_event_handler MAX_EPOCHS EVAL_PERIOD create_mt_supervised_trainer NAME attach info LOG_PERIOD ModelCheckpoint OUTPUT_DIR EPOCH_COMPLETED run to DataParallel to DataParallel CHECKPOINT_PERIOD DEVICE Timer getLogger add_event_handler MAX_EPOCHS EVAL_PERIOD NAME create_supervised_trainer attach info LOG_PERIOD ModelCheckpoint create_supervised_evaluator OUTPUT_DIR EPOCH_COMPLETED run CHECKPOINT_PERIOD DEVICE Timer create_supervised_trainer_with_center CENTER_LOSS_WEIGHT getLogger add_event_handler MAX_EPOCHS EVAL_PERIOD NAME attach info LOG_PERIOD ModelCheckpoint create_supervised_evaluator OUTPUT_DIR EPOCH_COMPLETED run expand_as t sqrt addmm_ expand data ne view size min squeeze expand t eq gather max METRIC_LOSS_TYPE format SAMPLER MARGIN print CrossEntropyLabelSmooth TripletLoss METRIC_LOSS_TYPE format MARGIN print CrossEntropyLabelSmooth TripletLoss CenterLoss format MARGIN print PSLoss CrossEntropyLabelSmooth TripletLoss IF_LABELSMOOTH append AttributeLoss cuda range Diverse_Loss affine bias kaiming_normal_ weight __name__ constant_ bias normal_ weight __name__ constant_ data fill_ normal_ NECK_FEAT LAST_STRIDE NECK Baseline NAME PRETRAIN_CHOICE PRETRAIN_PATH ResNet ResNet ResNet ResNet_IBN load_url load_state_dict ResNet_IBN load_url load_state_dict ResNet_IBN load_url load_state_dict WEIGHT_DECAY_BIAS named_parameters BASE_LR BIAS_LR_FACTOR WEIGHT_DECAY WEIGHT_DECAY_BIAS SGD named_parameters parameters BASE_LR BIAS_LR_FACTOR WEIGHT_DECAY ArgumentParser make_data_loader opts load_param OUTPUT_DIR DEVICE_ID build_mt_model freeze parse_args inference merge_from_file format config_file setup_logger WEIGHT merge_from_list mkdir info print add_argument build_model STEPS WARMUP_METHOD GAMMA weight PRETRAIN_PATH make_optimizer do_mt_train make_mt_data_loader num_classes_seg load_state_dict PRETRAIN_CHOICE build_mt_model to DEVICE METRIC_LOSS_TYPE format make_loss_with_center replace IF_WITH_CENTER eval do_train_with_center WARMUP_FACTOR WARMUP_ITERS load make_mt_loss triplet_margin print make_optimizer_with_center WarmupMultiStepLR normalize_size train makedirs make_loss do_train make_data_loader build_model makedirs print format isfile dirname mkdir_if_missing setFormatter join getLogger addHandler StreamHandler Formatter DEBUG setLevel FileHandler zeros_like float16 max exp transpose expand append sum range cat size astype mean unique addmm_ minimum print t int32 zeros numpy len | # Magnifier: Towards Semantic Adversary and Fusion for Person Re-identification <!-- # LIMITATIONS: HARD-CODED MASK POLICY / DISABLED 3PART POLICY / DISABLED ATTR BY HARDCOPDED 0 / SEG SIZE IS RESTRICTED TO 28-28 OR 16-32 DYNAMICALLY / DYNAMIC LOSS IS HARDEDCODED IN / FORCED NORM FEATURE / FORCED SOFTMAX LOSS FOR PART BRANCH, EVEN IF YOU CHOOSE TRIPLET IT WONT WORK FOR PART --> <!-- ## Doubts: Label Smoothing seems to not really work when sampler is not softmax-triplet?? --> ## This repo is expanded on [Bag of Tricks and A Strong ReID Baseline](https://github.com/michuanhaohao/reid-strong-baseline) ## Results (rank1/mAP) | Model | CUHK03-L | DukeMTMC-reID | | ----------------- | ---------- | ------------- | | Standard baseline | 69.8(67.4) | 82.7(70.8) | | + Mask Branch | 71.3(69.0) | 83.9(72.9) | | + SAB | 76.6(74.6) | 87.1(76.7) | | 757 |
NIRVANALAN/reid_baseline | ['person retrieval', 'person re identification', 'data augmentation'] | ['Camera Style Adaptation for Person Re-identification', 'Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)'] | datafolder/reid_dataset/import_VIPeR.py model/PCB/model.py model/ft_net_dense/model.py model/ft_ResNet50/train.py model/PCB/train.py model/ft_ResNet50/model.py net/ResNet34_nFC.py datafolder/reid_dataset/import_Market1501Attribute.py datafolder/reid_dataset/cuhk03_to_image.py random_erasing.py datafolder/reid_dataset/gdrive_downloader.py datafolder/reid_dataset/__init__.py net/ResNet50_nFC.py prepare.py train.py datafolder/reid_dataset/import_Market1501.py net/DenseNet121_nFC.py datafolder/reid_dataset/import_DukeMTMC.py datafolder/reid_dataset/import_CUHK03.py datafolder/reid_dataset/import_MarketDuke_nodistractors.py datafolder/reid_dataset/reiddataset_downloader.py evaluate.py net/ResNet50_nFC_softmax.py datafolder/reid_dataset/pytorch_prepare.py net/ResNet18_nFC.py prepare_static.py test.py evaluate_rerank.py re_ranking.py datafolder/reid_dataset/import_MarketDuke.py net/__init__.py datafolder/folder.py model/fp16/train.py model.py datafolder/reid_dataset/marketduke_to_hdf5.py demo.py datafolder/reid_dataset/import_DukeMTMCAttribute.py model/ft_net_dense/train.py datafolder/reid_dataset/import_CUHK01.py evaluate_gpu.py model/fp16/model.py imshow sort_img compute_mAP evaluate compute_mAP evaluate compute_mAP evaluate ft_net_dense ClassBlock ft_net PCB_test weights_init_classifier ft_net_middle PCB weights_init_kaiming prepare_model RandomErasing k_reciprocal_neigh re_ranking load_network get_id extract_feature fliplr train_model save_network draw_curve Train_Dataset Test_Dataset cuhk03_to_image get_confirm_token save_response_content gdrive_downloader import_CUHK01 cuhk03_test import_CUHK03 import_DukeMTMC import_DukeMTMCAttribute_binary import_DukeMTMCAttribute import_Market1501 import_Market1501Attribute_binary import_Market1501Attribute import_MarketDuke import_MarketDuke_nodistractors import_VIPeR marketduke_to_hdf5 pytorch_prepare_all pytorch_prepare reiddataset_downloader reiddataset_downloader_all ft_net_dense ClassBlock ft_net PCB_test weights_init_classifier ft_net_middle PCB weights_init_kaiming train_model save_network draw_curve ft_net_dense ClassBlock ft_net PCB_test weights_init_classifier ft_net_middle PCB weights_init_kaiming train_model save_network draw_curve ft_net_dense ClassBlock ft_net PCB_test weights_init_classifier ft_net_middle PCB weights_init_kaiming train_model save_network draw_curve ft_net_dense ClassBlock ft_net PCB_test weights_init_classifier ft_net_middle PCB weights_init_kaiming train_model save_network draw_curve ClassBlock weights_init_classifier DenseNet121_nFC weights_init_kaiming ResNet18_nFC ClassBlock weights_init_classifier weights_init_kaiming ClassBlock weights_init_classifier ResNet34_nFC weights_init_kaiming ClassBlock weights_init_classifier ResNet50_nFC weights_init_kaiming ResNet50_nFC_softmax weights_init_classifier weights_init_kaiming title imread pause view argsort intersect1d numpy argwhere in1d cpu mm append setdiff1d compute_mAP dot argsort intersect1d argwhere append flatten argwhere in1d zero_ range len view numpy cpu mm data normal_ kaiming_normal_ __name__ constant_ data normal_ __name__ constant_ time format print shape zeros range zeros_like around max exp transpose append sum range concatenate astype mean unique minimum int float32 argpartition k_reciprocal_neigh zeros len load join which_epoch load_state_dict index_select long norm view FloatTensor print Variable size model div sqrt cuda zero_ expand_as float PCB fliplr range cat append int basename data draw_curve Softmax model zero_grad max sm shape load_state_dict append range detach state_dict format save_network time criterion backward print Variable t train step join plot savefig legend append join save is_available cuda state_dict fromarray join print transpose File len save array range makedirs get get_confirm_token save_response_content Session items startswith print join listdir append append join listdir Print join File tolist append range join int print append listdir pop join list items ndarray isinstance print sort insert import_DukeMTMC append loadmat range len import_DukeMTMCAttribute join int print append listdir join remove items ndarray isinstance print sort import_Market1501 append loadmat range len pop import_Market1501Attribute insert join sorted int print append listdir join sorted int print append listdir append join orint listdir join special_dtype import_MarketDuke File close create_dataset range create_group len join print copyfile mkdir walk split pytorch_prepare join gdrive_downloader print extractall close cuhk03_to_image rmtree ZipFile makedirs items reiddataset_downloader half | <h1 align="center"> Person_reID_baseline_pytorch </h1> [](https://lgtm.com/projects/g/layumi/Person_reID_baseline_pytorch/context:python) [](https://travis-ci.org/layumi/Person_reID_baseline_pytorch) [](https://lgtm.com/projects/g/layumi/Person_reID_baseline_pytorch/alerts/) [](https://opensource.org/licenses/MIT) A tiny, friendly, strong baseline code for Person-reID (based on [pytorch](https://pytorch.org)). - **Strong.** It is consistent with the new baseline result in several top-conference works, e.g., [Beyond Part Models: Person Retrieval with Refined Part Pooling(ECCV18)](https://arxiv.org/abs/1711.09349) and [Camera Style Adaptation for Person Re-identification(CVPR18)](https://arxiv.org/abs/1711.10295). We arrived Rank@1=88.24%, mAP=70.68% only with softmax loss. - **Small.** With fp16, our baseline could be trained with only 2GB GPU memory. - **Friendly.** You may use the off-the-shelf options to apply many state-of-the-art tricks in one line. Besides, if you are new to person re-ID, you may check out our **[Tutorial](https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/tutorial)** first (8 min read) :+1: . | 758 |
NJUNLP/TOWE | ['sentiment analysis', 'aspect based sentiment analysis'] | ['Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling'] | code/main.py code/models.py code/networks.py code/train.py code/data_helper.py code/evaluate.py code/build_vocab_embed.py filter_vocab_embed load_text_target_label load_w2v generate_sentence_label load_data process_files line_to_index score_aspect score_BIO main test ToweDataset NeuralTagger numericalize numericalize_label WordRep IOG eval train category_from_output join list dump insert print len set add sqrt uniform save open zeros append split dump print open load_word2vec_format range len asarray print exit from_numpy append long enumerate len int print readlines dict zeros range split append split open asarray print tolist zip append range len append sum range len float range len load join NeuralTagger load_text_target_label print ds float32 train_from_data open load join str format load_w2v tolist write NeuralTagger_elmo load_data zip predict open append lower print split append enumerate model zero_grad SGD save cuda str view Adam EPOCHS device_count append freeze sum use_crf range nll_loss mean lr item label enumerate deepcopy time int Adadelta _neg_log_likelihood print backward parameters split step len topk tolist update _viterbi_decode category_from_output model tolist score_BIO extend numpy use_crf | NJUNLP/TOWE | 759 |
NLPbox/feng-hirst-service | ['discourse segmentation'] | ['Two-pass Discourse Segmentation with Pairing and Global Features'] | feng_hug_api.py test_api.py process_response process_data call_parser test_api_short start_api post_file test_api_long set_header unlink name join Command print close spawn decode post_file decode post_file | # feng-hirst-service [](https://travis-ci.org/NLPbox/feng-hirst-service) [](https://hub.docker.com/r/nlpbox/feng-hirst-service) This docker container allows you to build, install and run the [Feng/Hirst discourse parser](http://www.cs.toronto.edu/~weifeng/software.html) (Feng and Hirst 2014) in a docker container with an added REST API. Instead of the original code, this service uses [my fork](https://github.com/arne-cl/feng-hirst-rst-parser) which applies some patches, is easier to dockerize and produces an output format that is easier to parse (e.g. by [discoursegraphs](https://github.com/arne-cl/discoursegraphs) or the [rst-converter-service](https://[email protected]/NLPbox/rst-converter-service)). | 760 |
NOEPG/pytorch-kaldi | ['speech recognition', 'noisy speech recognition', 'distant speech recognition'] | ['The PyTorch-Kaldi Speech Recognition Toolkit'] | kaldi_decoding_scripts/utils/nnet/make_lstm_proto.py cfg/TIMIT_baselines/TIMIT_SincNet_raw3.py data_io.py core.py complexmodels/__init__.py kaldi_decoding_scripts/utils/nnet/gen_hamm_mat.py kaldi_decoding_scripts/utils/nnet/make_nnet_proto.py kaldi_decoding_scripts/utils/filt.py complexmodels/complexops.py kaldi_decoding_scripts/utils/nnet/make_blstm_proto.py save_raw_fea.py utils.py kaldi_decoding_scripts/utils/nnet/gen_dct_mat.py tune_hyperparameters.py neural_networks.py complexmodels/bn.py kaldi_decoding_scripts/utils/reverse_arpa.py complexmodels/complex_models.py run_exp.py plot_acc_and_loss.py kaldi_decoding_scripts/utils/nnet/gen_splice.py kaldi_decoding_scripts/utils/nnet/make_cnn_proto.py kaldi_decoding_scripts/utils/nnet/make_cnn2d_proto.py complexmodels/realops.py extract_data_from_shared_list convert_numpy_to_torch run_nn run_nn_refac01 read_next_chunk_into_shared_list_with_subprocess load_counts UnknownMatrixHeader _read_vec_flt_binary open_or_fd _read_mat_ascii read_vec_int_ark context_window_old read_vec_flt_scp UnknownVectorHeader read_cntime_ark load_chunk read_vec_flt_ark write_mat read_cntime UnsupportedDataType write_vec_int BadInputFormat read_post_ark SubprocessFailed write_vec_flt read_vec_int read_mat load_dataset BadSampleSize read_vec_flt read_post_rxspec read_post_scp read_ali_ark read_lab_fea_refac01 _read_vec_flt_riff _read_mat_binary read_key read_cnet_ark _read_compressed_mat read_segments_as_bool_vec read_lab_fea context_window popen read_mat_scp read_post read_mat_ark CNN RNN liGRU SincNet LayerNorm LSTM_cudnn GRU_cudnn GaborRealConv GaborConv channel_averaging SincConv_fast GRU minimalGRU act_fun MLP PASE GaborNet flip SincConv SRU ComplexMLP GaborRealNet LSTM RNN_cudnn logMelFb _max_nr_of_parallel_forwarding_processes _is_first_validation _run_forwarding_in_subprocesses nth_replace_string run_command compute_cw_max create_curves run_shell read_args_command_line check_cfg parse_model_field create_lists get_chunks_after_which_to_validate optimizer_init create_block_diagram run_shell_display dump_epoch_results is_sequential export_loss_acc_to_txt split_chunks write_cfg_chunk expand_section_proto get_val_cfg_file_path expand_str_ep create_block_connection get_val_lst_file_path compute_avg_performance check_consistency_with_proto parse_fea_field change_lr_cfg check_field expand_section get_all_archs get_val_info_file_path forward_model terminal_node_detection model_init create_configs parse_lab_field dict_fea_lab_arch is_sequential_dict do_validation_after_chunk _get_val_file_name_base shift check_cfg_fields cfg_item2sec list_fea_lab_arch forward_model_refac01 compute_n_chunks progress ComplexBatchNormalization complexbn complex_standardization multi_mean ComplexLayerNormalization apply_complex_mask complex_conv check_input affect_init get_imag get_normalized apply_complex_dropout complex_init affect_conv_init get_conjugate unitary_init get_real complex_product complex_max_pool1d istftloss get_modulus cosloss check_conv_input independent_complex_filters_init get_kernel_and_weight_shape complex_linear _istuple ComplexLinear STFT2d STFT1d ComplexConv _mktuple2d _mktuple1d real_init affect_init affect_conv_init independent_filters_init extract_data_from_shared_list convert_numpy_to_torch run_nn run_nn_refac01 read_next_chunk_into_shared_list_with_subprocess load_counts UnknownMatrixHeader _read_vec_flt_binary open_or_fd _read_mat_ascii read_vec_int_ark context_window_old read_vec_flt_scp UnknownVectorHeader read_cntime_ark load_chunk read_vec_flt_ark write_mat read_cntime UnsupportedDataType write_vec_int BadInputFormat read_post_ark SubprocessFailed write_vec_flt read_vec_int read_mat load_dataset BadSampleSize read_vec_flt read_post_rxspec read_post_scp read_ali_ark read_lab_fea_refac01 _read_vec_flt_riff _read_mat_binary read_key read_cnet_ark _read_compressed_mat read_segments_as_bool_vec read_lab_fea context_window popen read_mat_scp read_post read_mat_ark CNN RNN liGRU SincNet LayerNorm LSTM_cudnn GRU_cudnn GaborRealConv GaborConv channel_averaging SincConv_fast GRU minimalGRU act_fun MLP PASE GaborNet flip SincConv SRU ComplexMLP GaborRealNet LSTM RNN_cudnn logMelFb _max_nr_of_parallel_forwarding_processes _is_first_validation _run_forwarding_in_subprocesses nth_replace_string run_command compute_cw_max create_curves run_shell read_args_command_line check_cfg parse_model_field create_lists get_chunks_after_which_to_validate optimizer_init create_block_diagram run_shell_display dump_epoch_results is_sequential export_loss_acc_to_txt split_chunks write_cfg_chunk expand_section_proto get_val_cfg_file_path expand_str_ep create_block_connection get_val_lst_file_path compute_avg_performance check_consistency_with_proto parse_fea_field change_lr_cfg check_field expand_section get_all_archs get_val_info_file_path forward_model terminal_node_detection model_init create_configs parse_lab_field dict_fea_lab_arch is_sequential_dict do_validation_after_chunk _get_val_file_name_base shift check_cfg_fields cfg_item2sec list_fea_lab_arch forward_model_refac01 compute_n_chunks progress ComplexBatchNormalization complexbn complex_standardization multi_mean ComplexLayerNormalization apply_complex_mask complex_conv check_input affect_init get_imag get_normalized apply_complex_dropout complex_init affect_conv_init get_conjugate unitary_init get_real complex_product complex_max_pool1d istftloss get_modulus cosloss check_conv_input independent_complex_filters_init get_kernel_and_weight_shape complex_linear _istuple ComplexLinear STFT2d STFT1d ComplexConv _mktuple2d _mktuple1d real_init affect_init affect_conv_init independent_filters_init start Thread join float cuda view _optimization_step load_counts strtobool convert_numpy_to_torch write_mat _prepare_input log _write_info_file list _get_dim_from_data_set len map _read_chunk_specific_config sum range detach extract_data_from_shared_list close _get_batch_size_from_config _get_batch_config read_next_chunk_into_shared_list_with_subprocess forward_model join time _save_model is_sequential_dict _initialize_random_seed shift _update_progress_bar _open_forward_output_files_and_get_file_handles _load_model_and_optimizer numpy split load_counts strtobool open_or_fd zero_grad DataParallel numpy save write_mat round cuda max log seed str list optimizer_init len exit map load_state_dict sum range detach state_dict Thread replace ConfigParser close start manual_seed item float keys model_init forward_model load int read join time backward is_sequential_dict contiguous shift write randint step progress split _read_features_and_labels_with_kaldi _match_feature_and_label_sequence_lengths _chunk_features_and_labels _concatenate_features_and_labels _input_is_wav_file flatten empty range concatenate empty range roll min mean context_window load_dataset std column_stack _reorder_data_set _append_to_shared_list _read_features_and_labels _read_from_config _read_chunk_specific_config update int read dict_fea_lab_arch ConfigParser is_sequential_dict write exit shuffle load_chunk compute_cw_max keys append column_stack rsplit int seek search popen split open start Popen open decode strip read_vec_int open_or_fd read_key decode remove read open_or_fd close frombuffer array split pack char write open_or_fd encode range len read_vec_flt open_or_fd split read_vec_flt open_or_fd read_key decode remove open_or_fd close array split read frombuffer unpack decode read frombuffer pack char write open_or_fd encode tobytes read_mat open_or_fd split read_mat open_or_fd read_key decode _read_mat_ascii _read_mat_binary open_or_fd decode read reshape startswith frombuffer decode vstack append array split dtype read reshape zeros frombuffer array pack char write open_or_fd encode tobytes print exit startswith open_or_fd read_post split open_or_fd read_post read_key decode read tolist open_or_fd close append frombuffer range read_cntime open_or_fd read_key decode read tolist open_or_fd close frombuffer loadtxt repeat astype size view contiguous strtobool get_chunks_after_which_to_validate _get_nr_of_valid_per_epoch_from_config decode readline print append Popen decode write Popen flush communicate wait Popen int str findall group write exit nth_replace_string split append range compile len read ConfigParser mean append float sum int list write exit map float split append sections read list add_section ConfigParser remove_section set sections append keys range values len ConfigParser read list set list write exit any sections keys read ConfigParser exit check_cfg_fields expand_section open rstrip strtobool values open run_shell str list sorted parse_model_field len exit create_block_diagram append sum range replace check_consistency_with_proto parse_fea_field sections join items parse_lab_field int read write split findall makedirs write sections exit append range len list _partition_chunks append get_chunks_after_which_to_validate _get_nr_of_valid_per_epoch_from_config format _get_val_lst_file_name _get_val_info_file_name _get_val_cfg_file_name strtobool max open str check_cfg exit log10 ceil append range write_cfg_chunk expand_str_ep get_val_cfg_file_path format get_val_lst_file_path replace close get_all_archs float get_val_info_file_path keys int items do_validation_after_chunk write split compute_n_chunks len __add__ max open seed str sorted list map log10 reverse writelines ceil append split_chunks range format get_val_lst_file_path parse_fea_field close shuffle _get_validation_data_for_chunks _shuffle_forward_data int do_validation_after_chunk cfg_item2sec split len add_section str sorted remove_section append range replace check_consistency_with_proto ConfigParser glob remove_option sections keys int read join items cfg_item2sec findall len sorted write exit sub append split write exit sub append split glob int sorted format read list replace ConfigParser len write exit filter split findall float range append open list str list index append range len run_shell str read list remove replace create_block_connection ConfigParser filter findall append split list replace strtobool len map cfg_item2sec findall range append split list replace strtobool len map cfg_item2sec findall range append split strtobool keys strtobool append int keys max NLLLoss list out_dim strtobool nn_class set eval import_module getattr train cuda list strtobool map SGD Adam RMSprop parameters float keys split list _get_network_output _get_labels_from_input mean _add_input_features_to_outs_dict shape _compute_layer_values float cat len list view float mean shape long bool keys cat len str int print write close keys log10 ceil max open int write float round flush str asarray ndarray readlines makedirs savetxt split append float range len arange axis str use exit ylabel title savefig legend append export_loss_acc_to_txt range plot readlines clear print loadtxt xlabel write amax len find str read ConfigParser set keys int write extend exit append float split view size sqrt dim cat view size complex_standardization dim cat mean sorted reversed pow gather print str size check_conv_input size check_input check_conv_input size check_input get_real get_imag check_conv_input get_imag get_real check_input check_conv_input check_input repeat expand_as get_modulus get_real get_imag cat norm get_conjugate clamp complex_product sum view stft_module get_real get_imag cat squeeze cat check_input conv_transpose3d conv_transpose2d check_conv_input conv3d conv2d conv1d conv_transpose1d cat svd int T var dot sqrt uniform eye real imag rayleigh tuple cos uniform sin prod svd int T var reshape dot sqrt uniform eye real prod imag data init_func size type_as data init_func type_as tuple get_real get_imag tuple size create_dropout_mask append type dim range from_numpy from_numpy svd int T var reshape tuple dot sqrt uniform eye prod normal tuple prod | This is a fork from [mravanelli/pytorch-kaldi](https://github.com/mravanelli/pytorch-kaldi) where I added the models proposed in the work *"Complex Gabor Convolutional Neural Network on raw speech", [arXiv](https://arxiv.org/abs/2002.04569)*. In order to try it : 1. Follows the steps described in the “TIMIT tutorial”. 2. Save the raw waveform into the Kaldi ark format. To do it, you can use the save_raw_fea.py utility in our repository. The script saves the input signals into a binary Kaldi archive, keeping the alignments with the pre-computed labels. You have to run it for all the data chunks (e.g., train, dev, test). It can also specify the length of the speech chunk (*sig_wlen=200 # ms*) composing each frame. 3. Edit the GaborNet config file in *cfg/*, change the paths, and run: ``` python ./run_exp.py cfg/TIMIT_baselines/TIMIT_GaborNet_raw_pg.cfg ``` # The PyTorch-Kaldi Speech Recognition Toolkit <img src="pytorch-kaldi_logo.png" width="220" img align="left"> | 761 |
NPCai/Nopie | ['machine translation'] | ['Sequence Level Training with Recurrent Neural Networks'] | src/dataLoader.py src/customLoss.py src/main.py src/train.py src/model.py src/timey.py src/utils.py src/load.py TupleCritic getTopics pairs loadModel RNNDecoder RNNEncoder RNNAttentionDecoder timeSince asMinutes EncoderDecoder sentence2nums num2word onehot string2gloves word2glove word2num getDevice getVocabSize print append getTopics load EncoderDecoder floor time float append word2glove lower_ nlp append word2num lower_ nlp float | # Nopie Neural OPen infomation extractor, based on our [Squadie](https://github.com/NPCai/Squadie) dataset and bootstrapped from the [Graphene OpenIE system](https://github.com/Lambda-3/Graphene). ## Overview *Model*: Attention is All You Need (Transformer) *General Training procedure*: Initially train the transformer to mimic the Graphene OpenIE system (i.e. “bootstrapping”) until convergence. Then alternate with REINFORCE on our Squadie and News QA IE datasets to improve. Rewards are given for (1) NER’s being identified (as provided by an external NER system) (2) tuples being identified, (3) correct causal and temporal tags, (4) negative rewards for remaining pronouns. (See: MIXER training in https://arxiv.org/pdf/1511.06732.pdf) ## Milestones - [x] Make Squadie Dataset - [x] Make News QA Dataset - [x] Create a seq2seq with attention baseline on the datasets using OpenNMT (just for fun) - [x] Construct a database using the Graphene parser | 762 |
NUAA-AL/ALiPy | ['active learning'] | ['ALiPy: Active Learning in Python'] | test/test_split.py examples/bonus/label_ranking.py alipy/toolbox.py alipy/oracle/oracle.py examples/tools/Al_Eeperiment_usage.py test/test_stateio.py alipy/query_strategy/noisy_oracles.py examples/bonus/poss.py test/test_exa_feature_querying.py alipy/query_strategy/query_labels.py alipy/index/__init__.py test/test_exa_cost_sensitive.py test/test_exa_query_type.py examples/tools/toolbox_usage.py alipy/query_strategy/cost_sensitive.py test/test_exa_Al_Eeperiment_usage.py test/test_exa_poss.py test/test_exa_query_instance.py alipy/__init__.py test/test_exa_label_ranking.py alipy/query_strategy/multi_label.py test/test_exa_labeling_real_data.py alipy/oracle/__init__.py alipy/query_strategy/query_type.py alipy/data_manipulate/al_split.py examples/tools/StateIO_usage.py examples/AL_settings/labeling_real_data.py alipy/utils/interface.py examples/AL_settings/multi label.py test/test_repository.py alipy/query_strategy/query_features.py examples/AL_settings/query_type.py examples/tools/aceThreading_usage.py alipy/experiment/__init__.py examples/tools/split.py alipy/data_manipulate/datapreprocess.py examples/AL_settings/query_instance.py alipy/utils/misc.py alipy/experiment/stopping_criteria.py alipy/index/multi_label_tools.py alipy/data_manipulate/__init__.py alipy/metrics/performance.py examples/AL_settings/feature_querying.py examples/tools/index_manager_usage.py examples/tools/Oracle_usage.py examples/tools/Stopping_criteria_usage.py test/test_indexcollection.py test/test_exa_matrix_completion_usage.py alipy/utils/ace_warnings.py alipy/experiment/experiment_analyser.py test/test_analyser.py examples/bonus/matrix_completion_usage.py alipy/query_strategy/base.py test/test_oracle.py test/test_recover_multithreads.py alipy/index/index_collections.py examples/AL_settings/noisy_oracles.py alipy/experiment/state_io.py alipy/utils/multi_thread.py examples/tools/Experiment_analyser_usage.py test/test_multi_threading.py test/test_tools.py alipy/experiment/al_experiment.py alipy/experiment/state.py test/test_exa_noisy_oracles.py setup.py examples/AL_settings/cost_sensitive.py test/test_stop.py test/test_exa_multi label.py alipy/oracle/knowledge_repository.py alipy/query_strategy/__init__.py alipy/metrics/__init__.py ToolBox split_features split_load split_save __split_data_matrix split_multi_label split StandardScale _handle_zeros_in_scale minmax_scale AlExperiment StateIOContainer _CostEffectiveAnalyser _NumOfQueryAnalyser _ContentSummary _type_of_data ExperimentAnalyser State StateIO StoppingCriteria FeatureIndexCollection IndexCollection map_whole_index_to_train MultiLabelIndexCollection flattern_multilabel_index check_index_multilabel get_labelmatrix_in_multilabel integrate_multilabel_index get_Xy_in_multilabel infer_label_size_multilabel type_of_target label_ranking_loss label_ranking_average_precision_score get_fps_tps_thresholds coverage_error accuracy_score roc_auc_score hamming_loss zero_one_loss precision_recall_curve _auc findmax findIndex micro_auc_score check_consistent_length sort roc_curve average_precision_score _num_samples one_error _check_targets find MatrixRepository ElementRepository OracleQueryInstance Oracles OracleQueryFeatures OracleQueryMultiLabel Oracle BaseFeatureQuery BaseIndexQuery BaseMultiLabelQuery BaseNoisyOracleQuery QueryCostSensitiveRandom QueryCostSensitiveHALC select_POSS hierarchical_multilabel_mark QueryCostSensitivePerformance select_Knapsack_01 QueryMultiLabelQUIRE QueryMultiLabelMMC QueryMultiLabelAdaptive _LabelRankingModel_MatlabVer _BinaryRelevance DummyClf QueryMultiLabelAUDI LabelRankingModel QueryMultiLabelRandom seed_random_state majority_vote QueryNoisyOraclesRandom QueryNoisyOraclesSelectInstanceUncertainty QueryNoisyOraclesCEAL QueryNoisyOraclesAll QueryNoisyOraclesIEthresh get_query_results get_majority_vote QueryFeatureRandom QueryFeatureAFASMC AFASMC_mc AFASMC_mask_mc QueryFeatureStability _svd_threshold IterativeSVD_mc _get_proba_pred QueryInstanceUncertainty QueryInstanceSPAL QueryExpectedErrorReduction QueryInstanceBMDR QueryInstanceGraphDensity QueryInstanceLAL QueryInstanceQBC QueryInstanceCoresetGreedy QueryInstanceDensityWeighted QueryRandom QueryInstanceQUIRE QueryInstanceRandom QueryTypeAURO check_query_type ValidityWarning FunctionWarning InexistentElementWarning UnexpectedParameterWarning RepeatElementWarning BaseAnalyser BaseRepository BaseQueryStrategy BaseCollection BaseVirtualOracle get_gaussian_kernel_mat nlargestarg check_matrix _is_arraylike check_one_to_one_correspondence randperm unpack calc_kernel_matrix nsmallestarg aceThreading main_loop main_loop al_loop main_loop target_func test_list_of_performance2 test_stateio_container1 test_list_of_stateio_object2 test_stateio_container2 test_list_of_stateio_object1 test_list_of_performance1 main_loop main_loop al_loop main_loop test_raise_multiind test_raise_ind1 test_warn_ind1 test_basic_ind1 test_warn_multiind test_basic_multiind run_thread test_Oracles test_OracleQueryMultiLabel test_Oracle recover test_mat_basic_example test_ele_raise_example test_ele_raise_no_example test_ele_basic_no_example test_cost test_ele_basic_example test_mat_raise_example test_split1 test_split1_allclass_assert test_split3 test_split3_all_features test_split2 test_split2_allclass test_split1_allclass test_stateio_recover test_stateio_input_file test_stateio_output_file test_stateio_basic test_stateio_validity_checking test_stop3 test_stop4 test_stop2 test_stop1 test_nlargestarg_nsmallestarg test_check_one_to_one_correspondence test_check_index_multilabel int sum split_save arange check_array randperm unique append round array range len int sum split_save arange check_matrix set randperm shape append difference_update round range len load join loadtxt abspath append exists join isdir savetxt abspath save copy ndarray isscalar isinstance nanmax nanmin issparse asarray _handle_zeros_in_scale mean var issparse asarray isinstance Iterable append isinstance Counter isinstance all check_index_multilabel any array max isinstance check_index_multilabel Iterable append range infer_label_size_multilabel len update items check_index_multilabel set append range infer_label_size_multilabel isinstance check_matrix check_index_multilabel index copy Iterable append zeros len check_matrix get_labelmatrix_in_multilabel asarray hasattr unique asarray all isinstance lexsort trapz any memmap type check_consistent_length diff pop type_of_target csr_matrix union1d set startswith ravel check_consistent_length indptr startswith _check_targets check_consistent_length diff _num_samples accuracy_score sum append unique check_consistent_length count slice get_fps_tps_thresholds searchsorted get_fps_tps_thresholds repeat nan shape roc_curve asarray shape startswith sum _check_targets check_consistent_length shape type_of_target check_consistent_length type_of_target reshape masked_array sum filled check_consistent_length type_of_target csr_matrix cumsum indptr dot shape unique zip bincount zeros diff check_consistent_length enumerate rankdata type_of_target csr_matrix indptr mean shape zip check_consistent_length enumerate append range range append float array range range len type_of_target append range check_consistent_length len findIndex type_of_target sort append zeros sum range check_consistent_length find arange zeros max range len sum arange infty argmin power e where choice any row_stack zeros abs array len update deepcopy isinstance MultiLabelIndexCollection construct_by_1d_array _label_size range RandomState isinstance most_common Counter query_from_s isinstance append sum query_by_index get_query_results majority_vote T asarray eig copy dot sqrt argsort fmax shape zeros sum diag isinstance MultiLabelIndexCollection shape construct_by_1d_array get_matrix_mask pop norm asarray reshape hstack copy dot shape flatten sqrt unique zeros sum _svd_threshold range shape predict_proba asarray reshape check_array reshape asarray check_array arange isinstance shuffle generic item argsort argsort polynomial_kernel hasattr linear_kernel rbf_kernel kernel isinstance len append isinstance update update_information add_state arange reshape hstack get_stateio select reset hierarchical_multilabel_mark get_matrix_mask get_split difference_update State calc_performance_metric predict fit f1_score isinstance save get_Xy_in_multilabel len update get_repository get_training_data add_state update_information update_query get_stateio select reset get_split difference_update State calc_performance_metric get_majority_vote predict fit set_initial_point update add_state len select LogisticRegression QueryInstanceQBC save difference_update State sum predict fit print plot_learning_curves add_method get_experiment_analyser StateIOContainer plot_learning_curves add_method get_experiment_analyser plot_learning_curves add_method get_experiment_analyser print plot_learning_curves add_method get_experiment_analyser StateIOContainer plot_learning_curves add_method get_experiment_analyser plot_learning_curves add_method get_experiment_analyser update difference_update discard add add IndexCollection update discard update add_state fit select save set_initial_point difference_update State sum predict len add_knowledge randrange range query_by_index randrange _add_one_entry zip range query_by_index query_from_s query_from add_oracle print full_history cost_inall print load_iris LogisticRegression start_all_threads add_method ExperimentAnalyser QueryInstanceQBC plot_learning_curves retrieve_by_indexes discard update_query print full_history add get_training_data retrieve_by_indexes retrieve_by_examples add retrieve_by_indexes discard update_query print full_history add retrieve_by_examples get_training_data update_query discard add set union range split set union range split check_index_multilabel set union range split_multi_label check_index_multilabel set union range split_multi_label split_features union range set split_features union range set refresh_info add_state set_initial_point get_state pop add_state get_workspace recover_workspace save load join get_workspace StateIO update_information deepcopy update_information add_state State deepcopy update_information add_state State deepcopy update_information add_state State print check_index_multilabel array array | # ALiPy: Active Learning in Python Authors: Ying-Peng Tang, Guo-Xiang Li, [Sheng-Jun Huang](http://parnec.nuaa.edu.cn/huangsj) Online document: [http://parnec.nuaa.edu.cn/huangsj/alipy/](http://parnec.nuaa.edu.cn/huangsj/alipy/) ## Introduction ALiPy是一个基于Python实现的主动学习工具包，内置20余种主动学习算法，并提供包括数据处理、结果可视化等工具。ALiPy根据主动学习框架的不同部件提供了若干独立的工具类，这样一方面可以方便地支持不同主动学习场景，另一方面可以使用户自由地组织自己的项目，用户可以不必继承任何接口来实现自己的算法与替换项目中的部件。此外，ALiPy不仅支持多种不同的主动学习场景，如标注代价敏感，噪声标注者，多标记查询等。详细的介绍与文档请参考工具包的[官方网站](http://parnec.nuaa.edu.cn/huangsj/alipy/)。 ALiPy provides a module based implementation of active learning framework, which allows users to conveniently evaluate, compare and analyze the performance of active learning methods. It implementations more than 20 algorithms and also supports users to easily implement their own approaches under different settings. Features of alipy include: * Model independent - There is no limitation to the classification model. One can use SVM in sklearn or deep model in tensorflow as you need. | 763 |
NVIDIA/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 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 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)   ## FastPhotoStyle ### License Copyright (C) 2018 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). <img src="https://raw.githubusercontent.com/NVIDIA/FastPhotoStyle/master/teaser.png" width="800" title="Teaser results"> ### What's new | 764 |
NVIDIA/tacotron2 | ['speech synthesis'] | ['Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions'] | logger.py text/numbers.py text/cleaners.py plotting_utils.py train.py text/symbols.py layers.py text/cmudict.py utils.py audio_processing.py data_utils.py loss_scaler.py distributed.py multiproc.py hparams.py model.py text/__init__.py loss_function.py stft.py griffin_lim window_sumsquare dynamic_range_decompression dynamic_range_compression TextMelCollate TextMelLoader DistributedDataParallel _unflatten_dense_tensors _flatten_dense_tensors apply_gradient_allreduce create_hparams LinearNorm ConvNorm TacotronSTFT Tacotron2Logger Tacotron2Loss LossScaler DynamicLossScaler Tacotron2 Decoder Postnet Prenet Encoder LocationLayer Attention plot_gate_outputs_to_numpy plot_spectrogram_to_numpy save_figure_to_numpy plot_alignment_to_numpy STFT validate load_model load_checkpoint save_checkpoint init_distributed prepare_dataloaders prepare_directories_and_logger warm_start_model train reduce_tensor get_mask_from_lengths load_wav_to_torch to_gpu load_filepaths_and_text lowercase english_cleaners expand_abbreviations collapse_whitespace basic_cleaners convert_to_ascii transliteration_cleaners expand_numbers _parse_cmudict _get_pronunciation CMUDict normalize_numbers _expand_dollars _expand_ordinal _expand_decimal_point _expand_number _remove_commas text_to_sequence _clean_text _symbols_to_sequence _should_keep_symbol sequence_to_text _arpabet_to_sequence get_window normalize pad_center zeros range exp angle Variable squeeze rand astype float32 pi from_numpy transform range cat append view_as numel list requires_grad register_forward_hook register_hook parameters values broadcast HParams parse values info reshape tostring_rgb fromstring subplots xlabel draw close ylabel colorbar tight_layout imshow save_figure_to_numpy subplots xlabel draw close ylabel colorbar tight_layout imshow save_figure_to_numpy subplots xlabel draw close ylabel tight_layout scatter save_figure_to_numpy range len all_reduce clone print device_count set_device init_process_group validation_files DistributedSampler distributed_run n_frames_per_step DataLoader training_files TextMelCollate TextMelLoader join chmod Tacotron2Logger makedirs apply_gradient_allreduce min distributed_run fp16_run cuda update load format print load_state_dict state_dict print format load load_state_dict print format save format print log_validation eval train validate model batch_size clip_grad_norm_ zero_grad save_checkpoint fp16_run prepare_dataloaders prepare_directories_and_logger max seed initialize load_model ignore_layers Adam epochs grad_clip_thresh parse_batch warm_start_model master_params range format use_saved_learning_rate param_groups perf_counter distributed_run manual_seed item log_training enumerate int join learning_rate criterion apply_gradient_allreduce print load_checkpoint backward Tacotron2Loss parameters isnan init_distributed step len bool arange item read is_available contiguous cuda sub lowercase collapse_whitespace lowercase convert_to_ascii collapse_whitespace lowercase expand_abbreviations collapse_whitespace convert_to_ascii expand_numbers append _get_pronunciation sub split split group split int group sub match group len cleaner getattr | # Tacotron 2 (without wavenet) PyTorch implementation of [Natural TTS Synthesis By Conditioning Wavenet On Mel Spectrogram Predictions](https://arxiv.org/pdf/1712.05884.pdf). This implementation includes **distributed** and **automatic mixed precision** support and uses the [LJSpeech dataset](https://keithito.com/LJ-Speech-Dataset/). Distributed and Automatic Mixed Precision support relies on NVIDIA's [Apex] and [AMP]. Visit our [website] for audio samples using our published [Tacotron 2] and [WaveGlow] models.  ## Pre-requisites | 765 |
NVIDIA/waveglow | ['speech synthesis'] | ['WaveGlow: A Flow-based Generative Network for Speech Synthesis'] | train.py mel2samp.py glow.py distributed.py inference.py denoiser.py glow_old.py convert_model.py _check_model_old_version _update_model_res_skip _update_model_cond update_model Denoiser _flatten_dense_tensors apply_gradient_allreduce _unflatten_dense_tensors init_distributed main reduce_tensor remove fused_add_tanh_sigmoid_multiply WaveGlow Invertible1x1Conv WaveGlowLoss WN fused_add_tanh_sigmoid_multiply WaveGlow WN main load_wav_to_torch Mel2Samp files_to_list load_checkpoint train save_checkpoint Parameter n_layers WN len remove_weight_norm bias ModuleList Conv1d n_channels append weight range cat weight_norm Parameter n_layers WN len remove_weight_norm bias Conv1d n_channels append weight range cat weight_norm deepcopy _update_model_res_skip hasattr _update_model_cond modules setattr all_reduce clone print device_count set_device init_process_group cat append view_as numel requires_grad list register_forward_hook dir register_hook parameters values broadcast chmod format print wait strftime device_count append range Popen makedirs sigmoid tanh ModuleList append remove_weight_norm load join initialize remove_weightnorm Variable squeeze astype write eval unsqueeze files_to_list numpy cuda enumerate read load format print load_state_dict state_dict format print load_state_dict save cuda state_dict chmod model zero_grad DataLoader save_checkpoint max cuda initialize len Adam range SummaryWriter format WaveGlowLoss Mel2Samp manual_seed item enumerate join int criterion apply_gradient_allreduce print load_checkpoint Variable backward add_scalar parameters init_distributed step makedirs |  ## WaveGlow: a Flow-based Generative Network for Speech Synthesis ### Ryan Prenger, Rafael Valle, and Bryan Catanzaro In our recent [paper], we propose WaveGlow: a flow-based network capable of generating high quality speech from mel-spectrograms. WaveGlow combines insights from [Glow] and [WaveNet] in order to provide fast, efficient and high-quality audio synthesis, without the need for auto-regression. WaveGlow is implemented using only a single network, trained using only a single cost function: maximizing the likelihood of the training data, which makes the training procedure simple and stable. | 766 |
NVlabs/STEP | ['action detection', 'action recognition'] | ['STEP: Spatio-Temporal Progressive Learning for Video Action Detection'] | utils/utils.py external/ActivityNet/Evaluation/ava/np_box_list_ops.py external/ActivityNet/Evaluation/ava/np_box_mask_list.py data/data_utils.py external/ActivityNet/Evaluation/ava/metrics.py external/ActivityNet/Evaluation/ava/np_mask_ops.py external/ActivityNet/Evaluation/ava/np_box_list.py external/ActivityNet/Evaluation/ava/standard_fields.py train.py data/__init__.py train_cls.py models/two_branch.py external/ActivityNet/Evaluation/ava/np_box_ops.py models/__init__.py scripts/generate_label.py data/ava.py data/customize.py utils/solver.py external/ActivityNet/Evaluation/ava/np_box_mask_list_ops.py utils/__init__.py utils/tube_utils.py data/augmentations.py external/ActivityNet/Evaluation/ava/label_map_util.py external/maskrcnn_benchmark/roi_layers/nms.py test.py external/maskrcnn_benchmark/roi_layers/__init__.py external/ActivityNet/Evaluation/ava/object_detection_evaluation.py external/maskrcnn_benchmark/roi_layers/roi_pool.py utils/eval_utils.py scripts/extract_clips.py external/ActivityNet/Evaluation/get_ava_performance.py demo.py external/maskrcnn_benchmark/roi_layers/roi_align.py data/ava_cls.py utils/vis_utils.py models/networks.py setup.py models/i3dpt.py config.py external/ActivityNet/Evaluation/ava/per_image_evaluation.py parse_config str2bool str2none main get_extensions main main train validate main train validate SwapChannels base_transform ToAbsoluteCoords RandomErase RandomBrightness PhotometricDistort RandomSaturation Resize BaseTransform RandomSampleCrop ToPercentCoords intersect DivideStds Compose ConvertColor TubeAugmentation Expand SubtractMeans jaccard_numpy RandomHue ConvertFromInts RandomMirror RandomContrast RandomLightingNoise _load_images AVADataset detection_collate read_images get_target_tubes make_list get_proposals _load_images AVADataset detection_collate read_images sample_anchors get_target_tubes make_list CustomizedDataset detection_collate readsplitfile generate_anchors print_time read_exclusions run_evaluation parse_arguments make_image_key read_labelmap main read_csv create_category_index_from_labelmap create_category_index create_class_agnostic_category_index get_max_label_map_index _validate_label_map get_label_map_dict convert_label_map_to_categories load_labelmap compute_average_precision compute_cor_loc compute_precision_recall BoxList multi_class_non_max_suppression sort_by_field iou clip_to_window _update_valid_indices_by_removing_high_iou_boxes concatenate filter_scores_greater_than _copy_extra_fields area SortOrder ioa change_coordinate_frame prune_non_overlapping_boxes prune_outside_window scale intersection gather non_max_suppression BoxMaskList multi_class_non_max_suppression sort_by_field iou concatenate filter_scores_greater_than area ioa box_list_to_box_mask_list intersection gather prune_non_overlapping_masks non_max_suppression iou area ioa intersection iou area ioa intersection OpenImagesDetectionEvaluator WeightedPascalDetectionEvaluator DetectionEvaluator ObjectDetectionEvaluator ObjectDetectionEvaluation WeightedPascalInstanceSegmentationEvaluator PascalInstanceSegmentationEvaluator PascalDetectionEvaluator PerImageEvaluation DetectionResultFields BoxListFields InputDataFields TfExampleFields ROIAlign _ROIAlign _ROIPool ROIPool _get_padding get_conv_params I3D_head Mixed load_conv3d get_padding_shape simplify_padding MaxPool3dTFPadding Unit3Dpy I3D load_mixed get_bn_params ROINet BaseNet weights_init build_base_i3d ContextNet Bottleneck Bottleneck_resample TwoBranchNet build_conv hou_min_sec ava_evaluation get_params WarmupCosineLR WarmupStepLR augment_tubes valid_tubes_old scale_tubes_abs compute_box_iou valid_tubes encode_coef scale_tubes decode_coef extend_tubes get_center_size flatten_tubes compute_tube_iou extrapolate_tubes train_select AverageMeter get_gpu_memory select_proposals inference draw_rectangle overlay_image max_iter reg_thresh cls_thresh add_argument ArgumentParser append parse_args set_defaults range len batch_size stds DataParallel DataLoader cuda open max_iter save_root set_device OrderedDict device_count BaseTransform dirname load_state_dict TwoBranchNet to range synchronize means close eval pool_mode fps image_size load join T scale_norm ContextNet items pool_size time print CustomizedDataset ROINet BaseNet data_root isfile makedirs glob join dirname abspath read_labelmap str sorted list AVADataset append format enumerate write ava_evaluation input_type SGD det_net warmup_iters exp_name base_net WarmupCosineLR initialize name milestones len Adam strftime getattr WarmupStepLR state_dict get update glob TubeAugmentation fp16 vars get_params train resume_path remove now pretrain_path min_ratio cycle_decay empty_cache validate lambda_reg batch_size zero_grad save start_iteration dataset NUM_SAMPLE cuda max_iter str view save_root train_select start_epochs ceil to flatten_tubes range state_dict update format max_epochs synchronize size close perf_counter choice mean eval get_gpu_memory item fp16 enumerate items int T lambda_neighbor remove backward print contiguous AverageMeter clone write save_step isfile step len max_iter str join save_root close ava_evaluation data_root append range open sum append val concatenate select_proposals avg reshape zeros minimum clip maximum intersect astype float32 shape zeros range int sorted items arange append keys range len join list concatenate len read_labelmap zeros array range open int join format _load_images extend ceil join sorted glob linspace isfile append imread len append stack min len extend choice uniform stack array append max range jaccard_numpy append zip time info int time defaultdict reader print_time name make_image_key append float add make_image_key reader set int add set startswith append time print_time PascalDetectionEvaluator read_labelmap read_exclusions evaluate add_single_ground_truth_image_info add_single_detected_image_info pprint pformat info read_csv len add_argument ArgumentParser basicConfig parse_arguments run_evaluation item name id display_name item info append range _validate_label_map item id load_labelmap max convert_label_map_to_categories load_labelmap argsort cumsum astype concatenate maximum sum range len get_coordinates add_field get_extra_fields size BoxList get_field get_field argsort get sort_by_field arange iou filter_scores_greater_than squeeze logical_and num_boxes append expand_dims full range get add_field sort_by_field zeros_like filter_scores_greater_than concatenate reshape BoxList num_boxes get_field range append non_max_suppression get add_field array_split get_extra_fields hstack BoxList get_field get array_split _copy_extra_fields hstack area astype BoxList fmax int32 fmin greater_equal ioa gather array amax get array_split reshape hstack where logical_not max add_field get_extra_fields BoxList shape vstack get_field astype int32 BoxList get _copy_extra_fields scale get_field get_extra_fields add_field max BoxMaskList get_extra_fields get_field append get_masks BoxMaskList greater_equal ioa gather array amax append minimum transpose maximum shape zeros split expand_dims area intersection expand_dims area intersection sum arange append _pad_top_bottom pop zip join _get_padding get_operation_by_name shape append get_attr get_tensor_by_name run join get_tensor_by_name run decode join get_conv_params ones transpose from_numpy get_bn_params join load_conv3d xavier_normal_ data constant_ conv3d_1a_7x7 mixed_3c mixed_4e Sequential maxPool3d_3a_3x3 mixed_3b apply load_state_dict format mixed_4c mixed_4d conv3d_2c_3x3 mixed_4f maxPool3d_4a_3x3 mixed_4b maxPool3d_2a_3x3 load print I3D conv3d_2b_1x1 isfile load update format mixed_5b I3D_head print Sequential apply maxPool3d load_state_dict isfile mixed_5c state_dict int run_evaluation open max_iter requires_grad sorted det_lr named_parameters dict det_lr0 base_lr keys range minimum zeros maximum range get_center_size range randint minimum ndarray isinstance clamp reshape maximum shape range minimum reshape maximum shape zeros get_center_size log get_center_size exp clone minimum range maximum minimum range maximum arange concatenate reshape copy shape tile append range len zeros_like fill_ min clone get_center_size max reshape min maximum zeros max range reshape range zeros argmax view expand decode_coef append to flatten_tubes range cat extrapolate_tubes concatenate size mean tile int T contiguous valid_tubes clone numpy len max_pos_num num_classes squeeze add append range extrapolate_tubes asarray concatenate neg_ratio set mean stack select_proposals selection_sampling tile int T reshape sort valid_tubes cpu zeros numpy len argmax max list exp ones add append sum range shuffle copy set choice zip int remove min compute_tube_iou len check_output int rectangle range asarray format Draw text size draw_rectangle save split range open | [](https://raw.githubusercontent.com/nvlabs/SPADE/master/LICENSE.md)  ## STEP: Spatio-Temporal Progressive Learning for Video Action Detection  [[Paper]](https://arxiv.org/abs/1904.09288) [[Supp]](http://xiaodongyang.org/publications/papers/step-supp-cvpr19.pdf) [[YouTube]](https://www.youtube.com/watch?v=JwaBi_2JFeU&list=LLLPwTGzBXCd3HmILvHfIl6A&index=12&t=609s) [[Poster]](https://drive.google.com/open?id=1GWWLH5HQM8FoEIutIOzvtURBI6y09NBr) STEP: Spatio-Temporal Progressive Learning for Video Action Detection, CVPR 2019 (Oral) <br> [Xitong Yang](http://users.umiacs.umd.edu/~xyang35/), [Xiaodong Yang](https://xiaodongyang.org/), [Ming-Yu Liu](http://mingyuliu.net/), [Fanyi Xiao](http://fanyix.cs.ucdavis.edu/), [Larry Davis](https://www.cs.umd.edu/people/lsdavis), [Jan Kautz](http://jankautz.com/) <br>  STEP is a **fully end-to-end** action detector that performs detection simply from a handful of initial proposals with **no** need of relying on an extra person detector. ## Table of contents | 767 |
NVlabs/Taylor_pruning | ['network pruning'] | ['Importance Estimation for Neural Network Pruning'] | models/preact_resnet.py logger.py utils/utils.py pruning_engine.py models/vgg_bn.py models/densenet_imagenet.py layers/gate_layer.py models/lenet.py utils/group_lasso_optimizer.py main.py models/resnet.py Logger main train str2bool validate GateLayer DenseNet201 densenet161 DenseNet121 DenseNet DenseNet169 _DenseLayer _DenseBlock _Transition LeNet PreActBlock PreActResNet50 PreActResNet PreActResNet18 test PreActResNet101 PreActResNet152 PreActBottleneck norm2d PreActResNet34 ResNet resnet50 Bottleneck resnet152 conv3x3 resnet34 resnet18 BasicBlock resnet101 flatten_model VGG LinView slimmingvgg make_layers group_lasso_decay get_conv_sizes add_hook_for_flops AverageMeter accuracy save_checkpoint adjust_learning_rate connect_gates_with_parameters_for_flops adjust_learning_rate_fixed load_model_pytorch dynamic_network_change_local data model clip_grad_norm_ run_full_oracle do_step tensorboard get_number_flops update detach_ compute_hessian format param_groups size eval fixed_network avg zero_ item enumerate needs_hessian time int criterion backward print add_scalar AverageMeter accuracy get_flops parameters step_after pruning get_number_neurons step len time format print AverageMeter eval avg add_scalar has_attribute SGD ImageFolder wd Logger ArgumentParser vgg11_bn seed connect_tensorboard name parse_args get_conv_sizes Compose pruning_config distributed manual_seed CIFAR10 PruningConfigReader pruning_mask_from join DenseNet121 makedirs dict isfile dynamic_network zero_lr_for_epochs validate DataLoader save_checkpoint DistributedDataParallel max load_model tensorboard DistributedSampler strftime resnet101 append to init_process_group fixed_network add_argument pruning train epochs group_lasso_decay model PreActResNet18 localtime resnet34 dataset cuda PreActResNet50 mgpu exit shape sum range state_dict connect_gates_with_parameters_for_flops augment get_parameters print add_graph named_parameters set_epoch pytorch_pruning load_model_pytorch use_test_as_train data save_models prepare_pruning_list adjust_learning_rate device argv load_mask LeNet PreActResNet152 NotImplementedError dynamic_network_change_local CrossEntropyLoss SummaryWriter format resnet50 read_config Normalize DenseNet201 MNIST 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 print format randn print size PreActResNet18 net 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 BatchNorm1d num_features isinstance print Sequential OrderedDict GateLayer modules append BatchNorm2d enumerate flatten_model VGG load_url load_state_dict make_layers replace save lr_decay_every print tensorboard param_groups lr lr_decay_scalar add_scalar lr_decay_every print tensorboard param_groups lr lr_decay_scalar add_scalar topk size t eq mul_ expand_as append sum max load items format print OrderedDict shape load_state_dict enumerate list hasattr view isinstance print size_mask Conv2d shape modules append BatchNorm2d weight range enumerate isinstance register_forward_hook Conv2d modules enumerate list hasattr add_hook_for_flops rand compute_flops parameters output_dims append print int append enumerate | [](https://raw.githubusercontent.com/nvlabs/SPADE/master/LICENSE.md)  # Importance Estimation for Neural Network Pruning This repo contains required scripts to reproduce results from paper: Importance Estimation for Neural Network Pruning<br> Pavlo Molchanov, Arun Mallya, Stephen Tyree, Iuri Frosio, Jan Kautz .<br> In CVPR 2019.  ### [License](https://raw.githubusercontent.com/nvlabs/Taylor_pruning/master/LICENSE.md) Copyright (C) 2019 NVIDIA Corporation. | 768 |
NVlabs/geomapnet | ['camera localization'] | ['Geometry-Aware Learning of Maps for Camera Localization'] | dataset_loaders/robotcar.py dataset_loaders/seven_scenes.py common/train.py scripts/train.py scripts/plot_activations.py common/criterion.py scripts/calc_pose_stats.py models/posenet.py scripts/dataset_mean.py scripts/plot_vo_poses.py scripts/reverse_vo_poses.py scripts/set_paths.py scripts/align_vo_poses.py common/Logger.py common/optimizer.py dataset_loaders/time_imload.py common/vis_utils.py dataset_loaders/utils.py common/pose_utils.py scripts/process_robotcar_gps.py scripts/eval.py scripts/test_vo.py common/visdom_utils.py dataset_loaders/composite.py scripts/process_robotcar_images.py models/vidloc.py PoseNetCriterion MapNetOnlineCriterion MapNetCriterion QuaternionLoss AverageMeter Logger Optimizer OnlyPoses MFOnline MF MapNet PoseNet filter_hook append clone | [](https://raw.githubusercontent.com/NVIDIA/FastPhotoStyle/master/LICENSE.md)  # Geometry-Aware Learning of Maps for Camera Localization This is the PyTorch implementation of our CVPR 2018 paper "[Geometry-Aware Learning of Maps for Camera Localization](https://arxiv.org/abs/1712.03342)" - CVPR 2018 (Spotlight). [Samarth Brahmbhatt](https://samarth-robo.github.io/), [Jinwei Gu](http://www.gujinwei.org/), [Kihwan Kim](https://www.cc.gatech.edu/~kihwan23/), [James Hays](https://www.cc.gatech.edu/~hays/), and [Jan Kautz](http://jankautz.com/) ### A four-minute video summary (click below for the video) [](https://www.youtube.com/watch?v=X6mF_IbOb4A) ## Citation If you find this code useful for your research, please cite our paper ``` | 769 |
NVlabs/ssn_superpixels | ['superpixels'] | ['Superpixel Sampling Networks'] | loss_functions.py create_solver.py train_ssn.py compute_ssn_spixels.py fetch_and_transform_data.py config.py lib/cython/setup.py create_net.py input_patch_data_layer.py utils.py init_caffe.py cnn_module conv_bn_relu_layer compute_final_spixel_labels get_ssn_net create_ssn_net conv_relu_layer exec_iter normalize decode_features load_ssn_net compute_assignments create_solver_proto create_solver scale_label fetch_and_transform_patch_data convert_label transform_and_get_spixel_init scale_image transform_and_get_image fetch_and_transform_data smooth_loss centroid_pos_color_loss crop_x smooth_loss3 crop_y centroid_pos_color_loss2 gradient_y weight_edges centroid_loss2 centroid_loss position_color_loss l1_loss smooth_loss4 gradient_x weight_edges2 smooth_loss2 main train_net get_spixel_image get_spixel_init initialize_net_weight convert_rel_to_spixel_label get_rand_scale_factor visualize_spixels Convolution Tile Power ReLU Convolution BatchNorm ReLU Convolution ReLU Pooling conv_bn_relu_layer Crop Concat conv_relu_layer Interp int Passoc Softmax RelToAbsIndex ArgMax int Tile Reshape Convolution Smear Eltwise compute_assignments SpixelFeature2 Python feat_spixel_init PixelFeature SpixelFeature2 LossWithoutSoftmax ReLU spixel_feat6 Power Input final_pixel_assoc recon_label problabel compute_final_spixel_labels spixel_feat1 spixel_feat2 recon_feat2 recon_label3 exec_iter spixel_feat8 normalize decode_features img pixel_features position_color_loss SpixelFeature spixel_feat4 spixel_feat7 label trans_features spixel_init init_spixel_feat cnn_module recon_label2 Smear NetSpec spixel_feat5 spixel_label new_spixel_feat spixel_feat3 compute_assignments new_spix_indices spixel_feat9 int str write close NamedTemporaryFile create_ssn_net int str write close NamedTemporaryFile create_ssn_net str name close write NamedTemporaryFile get_solver open extend ADAM SolverParameter GPU Transformer asarray pad preprocess set_transpose expand_dims get_spixel_init print squeeze tolist astype float32 shape argmax convert_label img_as_float transform_and_get_spixel_init expand_dims rgb2lab transform_and_get_image randint imread loadmat len zoom zoom scale_label convert_label img_as_float transform_and_get_spixel_init scale_image expand_dims get_rand_scale_factor rgb2lab transform_and_get_image randint imread loadmat len Reduction AbsVal Eltwise EuclideanLoss Slice EuclideanLoss SpixelFeature EuclideanLoss Slice SpixelFeature EuclideanLoss DummyData Crop DummyData Crop DummyData AbsVal Crop Eltwise DummyData AbsVal Crop Eltwise Convolution Exp Convolution Power Reduction weight_edges gradient_y gradient_x Eltwise Reduction weight_edges2 crop_x crop_y gradient_y gradient_x Eltwise Reduction crop_x crop_y gradient_y Eltwise gradient_x weight_edges2 Reduction gradient_y gradient_x Eltwise Slice EuclideanLoss str int create_solver_proto get_spixel_init initialize_net_weight get_ssn_net solve create_solver copy_from float net num_steps add_argument l_rate train_net caffemodel ArgumentParser parse_args normal min max int keys range startswith range show imshow mark_boundaries astype mark_boundaries astype int arange reshape transpose sqrt pad floor int32 meshgrid array RegularGridInterpolator | ## Superpixel Sampling Networks This is the code accompanying the **ECCV 2018** publication on **Superpixel Sampling Networks**. Please visit the project [website](http://varunjampani.github.io/ssn) for more details about the paper and overall methodology. ### License Copyright (C) 2018 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). ### Installation #### Caffe Installation 1. Go to 'lib' folder if you are not already there: ``` | 770 |
NYUMedML/DARTS | ['brain segmentation'] | ['DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation'] | archive/weights.py DARTS/dataloader.py archive/define_model.py DARTS/loss_func.py execute_training.py archive/define_loss_score.py archive/2class_model1.py validate.py train.py DARTS/__init__.py archive/semi_model_rest_job2.py DARTS/darts.py DARTS/perform_pred.py archive/complete_module_manual.py archive/complete_module.py DARTS/models/unet.py DARTS/utils.py DARTS/models/dense_unet_model.py archive/semi_model_rest_job1.py archive/define_data_loader_manual.py archive/define_training.py archive/define_data_loader_hcp.py setup.py DARTS/_version.py pickling count_parameters unpickling weights_init train_model_non_self_sup train_model_self_sup pickling unpickling visualize1 dice_score run_subs plot_box pickling unpickling dice_loss_2 train_model dice_score Upsample_block BrainImages Unet Downsample_block pickling unpickling weights_init pickling unpickling weights_init BrainImages BrainImages dice_loss_2 dice_loss_1 dice_score Upsample_block Unet Downsample_block train_model pickling unpickling dice_loss_2 train_model dice_score Upsample_block BrainImages Unet Downsample_block pickling unpickling dice_loss_2 train_model dice_score Upsample_block BrainImages Unet Downsample_block Segmentation get_trailing_number_n_img_name BrainImages dice_score dice_loss_2 dice_loss_1 dice_loss_qn main pickling unpickling dice_score back_to_original_4_pred orient_correctly back_to_original_4_prob load_data create_one_hot_seg orient_to_ras Dense_Unet Down_sample Upsample_n_Concat Single_level_densenet Upsample_block Unet Downsample_block dump open load open data __name__ xavier_normal model rand zero_grad clip_grad_value_ save cuda criteria load_state_dict append range CrossEntropyLoss state_dict format inf size eval item time dice_score backward print clamp parameters cel train step rand zero_grad clip_grad_value_ save cuda criteria load_state_dict append range state_dict format inf size eval item time dice_score backward print clamp parameters train step astype softmax append expand_dims numpy range reshape shape append sum range time visualize1 dice_score num_subs concatenate print BrainImages DataLoader append train numpy cuda range len show dump subplots arange plot text set_xlabel len mean set_ylabel savefig model_name score_path boxplot legend open view size pow stack repeat softmax append sum range view FloatTensor denominator size mean stack softmax type numerator dice_loss_2 time format inf dice_score model backward print zero_grad numpy load_state_dict item append train step cuda range state_dict view size repeat softmax sum max max rand save pickling size sub search view size repeat sum max model back_to_original_4_pred get_data from_filename unsqueeze ArgumentParser save max clip is_mgz input_image_path exit load_state_dict parse_args to range file_name concatenate MGHImage astype segmentation_dir_path Dense_Unet eval model_wts_path is_available float load join time print add_argument min moveaxis float32 Nifti1Image load_data Unet numpy load affine orient_correctly shape from_filename pad affine io_orientation transpose astype flip transpose flip transpose astype orient_to_ras pad flip enumerate transpose astype pad flip append orient_to_ras enumerate astype softmax append expand_dims numpy range | # DenseUnet-based Automatic Rapid brain Segmentation (DARTS) ## Paper associated with the project [Here](https://arxiv.org/abs/1911.05567) is the paper describing the project and experiments in detail. ## Package * The DARTS package can be installed using: ``` pip install DARTSeg ``` ## Pre-trained model wts * Download the pretrained models from [here](https://drive.google.com/file/d/1OJ0RmcALNkiU49Npm7Rez6thIKOf3gLQ/view?usp=sharing) as follows: | 771 |
NYXFLOWER/TIP | ['link prediction', 'pose prediction'] | ['Tri-graph Information Propagation for Polypharmacy Side Effect Prediction'] | prepare.py model/ddm-nn.py test/pp_net.py src/layers.py analysis/top10.py model/ddm-df_rgcn.py test/dd_net_scalable.py test/pd_net.py data/preprocess_data.py data/drug_structure/data_deepddi.py test/dd_net.py src/utils.py src/neg_sampling.py analysis/load.py data/utils.py tip.py load_data_torch save_to_pkl process_prot_edge get_side_effect_index_from_text save_to_pkl cut_data get_drug_index_from_text sparse_to_tuple load_data_torch get_edge_list calculate_drug_similarity MultiInnerProductDecoder train test Encoder NNDecoder test train Encoder MyRGCNConv2 TIP Setting FMEncoder FMEncoderCat PPEncoder NNDecoder HierEncoder MultiInnerProductDecoder MyHierarchyConv MyGAE MyRGCNConv negative_sampling typed_negative_sampling remove_bidirection process_edges dict_ep_to_nparray sparse_id auprc_auroc_ap get_range_list uniform dense_id to_bidirection MultiInnerProductDecoder train test Encoder MultiInnerProductDecoder train test Encoder dict_to_nparray test NNDecoder HierEncoder train PP_Encoder tocoo vstack tensor nnz tocsr FloatTensor csr_matrix Size ones tolist append load_npz sum range LongTensor pop join row print col zeros array len data shape transpose tocoo join list nnz csr_matrix print append load_npz range append load_npz join range remove_bidirection LongTensor concatenate to_bidirection binomial from_dict glob to_csv MolFromMolFile AddHs GetMorganFingerprint DiceSimilarity zero_grad train_et ones tolist train_idx encoder to sum range cat format train_range d_feat decoder backward print x_norm auprc_auroc_ap zeros step test_idx decoder ones test_et auprc_auroc_ap eval zeros range cat uint8 view size astype choice from_numpy device tensor to append negative_sampling view clone append ones size get_range_list cat binomial append enumerate LongTensor FloatTensor coo_matrix precision_recall_curve numpy auc uniform_ sqrt zeros items train_type cuda test_type p_feat dp_range_list dp_edge_index p_norm zeros items | # Tri-graph Information Propagation (TIP) model TIP is an efficient general approach for **multi-relational link prediction** in any **multi-modal** (i.e. heterogeneous and multi-relational) network with two types of nodes. It can also be applied to the **Knowledge Graph Completion** and **Recommendation** task. TIP model is inspired by the [Decagon](https://github.com/marinkaz/decagon) and [R-GCN](https://github.com/tkipf/relational-gcn) models, motivated by their limitations of high computational cost and memory demand when graph goes really complex. TIP improves their link prediction **accuracy**, and time and space **efficiency** of node representation learning. See details on the algorithm in our paper [(Xu, Sang, and Lu, 2019)](https://grlearning.github.io/papers/94.pdf). ## TIP for Polypharmacy Side Effect Prediction we are particularly concerned about the safety of [polypharmacy](https://en.wikipedia.org/wiki/Polypharmacy), which is the concurrent use of multiple medications by a patient. Given a pair of drugs (:pill:,:pill:), the TIP model will predict how many polypharmacy side effects the drug pair will have, and what are the possibilities. <div align=center> <img height="100" src="img/pred_dd.png" alt=""hhh/> </div> We use *POSE clinical records* and *pharmacological information* to construct a multi-modal biomedical graph with two types of nodes: Drug (D) and Protein (P). The graph contains three types of interaction (refer to three subgraphs):   :cookie:   D-D graph: drug-drug interactions with side effects as edge labels   :cake:   P-D graph: protein-drug interactions (with a fixed label) | 772 |
NamSahng/SingingStyleTransfer | ['style transfer'] | ['Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium Generative Adversarial Networks'] | Preprocess/Unet_vocalseparation/util.py CycleConsistency-BoundaryEquilibrium-GAN/download.py CycleConsistency-BoundaryEquilibrium-GAN/utils.py CycleConsistency-BoundaryEquilibrium-GAN/convert.py CycleConsistency-BoundaryEquilibrium-GAN/train.py Preprocess/Unet_vocalseparation/const.py Preprocess/Unet_vocalseparation/network.py Preprocess/Unet_vocalseparation/DoExperiment.py CycleConsistency-BoundaryEquilibrium-GAN/model.py CycleConsistency-BoundaryEquilibrium-GAN/preprocess.py CycleConsistency-BoundaryEquilibrium-GAN/module.py conversion download_vcc2016 progress_bar maybe_unzip maybe_download CycleGAN downsample1d_block pixel_shuffler instance_norm_layer gated_linear_layer conv1d_layer discriminator residual1d_block generator_gatedcnn upsample1d_block wavs_to_specs wavs_to_specs_train wavs_to_specs_test mfccs_normalization transpose_in_list wav_padding world_encode_data load_wavs coded_sps_normalization_inverse_transoform world_encode_spectral_envelop world_decode_spectral_envelop world_decompose world_speech_synthesis logf0_statistics coded_sp_padding world_decode_data sample_train_data_for_spec pitch_conversion wavs_to_mfccs coded_sps_normalization_transoform world_synthesis_data sample_train_data coded_sps_normalization_fit_transoform train cross_entropy_loss l2_loss l1_loss TrainUNet UNetTrainmodel UNet LoadDataset SaveSpectrogram LoadAudio SaveAudio ComputeMask load join T write_wav basename CycleGAN pitch_conversion wav_padding ascontiguousarray world_encode_spectral_envelop world_decode_spectral_envelop world_decompose world_speech_synthesis listdir makedirs update ProgressBar finish join str urlretrieve st_size print stat makedirs print maybe_unzip maybe_download multiply instance_norm conv1d conv1d_layer instance_norm_layer gated_linear_layer conv1d_layer instance_norm_layer gated_linear_layer conv1d_layer instance_norm_layer gated_linear_layer pixel_shuffler reshape transpose transpose load join list print append listdir float64 harvest astype d4c cheaptrick code_spectral_envelope decode_spectral_envelope get_cheaptrick_fft_size append world_decompose list world_encode_spectral_envelop append list T append list world_decode_spectral_envelop float32 astype synthesize append list world_speech_synthesis zip list concatenate mean append std append list append list ceil int pad int pad floor ceil len mean std log concatenate exp log stft list append mfcc list append list concatenate mean append std list arange min shuffle zip append randint array range len list arange min shuffle zip append randint array len list stft append abs log list exp angle stft append abs log save max seed transpose_in_list write_wav basename wav_padding world_encode_data load_wavs shape world_encode_spectral_envelop world_decode_spectral_envelop world_decompose world_speech_synthesis range logf0_statistics format CycleGAN ascontiguousarray listdir load join time T savez print pitch_conversion coded_sps_normalization_fit_transoform sample_train_data makedirs update asarray UNetTrainmodel setup BATCH_SIZE print Adam randint UNet range save PATCH_LENGTH sum to_gpu zeros len join savez PATH_FFT resample astype float32 max SR load append load exp angle stft abs write_wav istft SR load UNet shape vstack zeros | # Singing Style Transfer - My contribution on Team Project on Eliceio team4: vocal-style-transfer (https://github.com/eliceio/vocal-style-transfer) - I was in charge of transfering singing style by using separated vocal data(separated by pretrained Deep U net[2]) not by clean speech data. And I adapted BE-GAN[4] training skill to Cycle-GAN-VC[3] refer to Singing Style Transfer C-BEGAN [1]. ## 1. Abstract - Whole architecture for changing singing style transfer is shown below [1] - <img src = "./image/Main_Architecture.JPG" width="60%"> ## 2. Preprocess - First download songs from "Youtube" by using pytube library.(This might be illegal) For the vocal data I downloaded Park Hyo Shin and BolBBalGan Sachungi's songs. (about 15 songs each) - [running ex](https://github.com/NamSahng/SingingStyleTransfer/blob/master/Preprocess/Data_Download_from_YT.ipynb) - For the separation of Singing Voice & Accompaniment I used pretrained deep U-net model. [2] | 773 |
NanaLange/CL-HAABSA | ['sentiment analysis', 'word embeddings', 'aspect based sentiment analysis'] | ['A Hybrid Approach for Aspect-Based Sentiment Analysis Using Deep Contextual Word Embeddings and Hierarchical Attention'] | hyperOpt.py lcrModelAlt.py main_cross.py auxModel.py getCurriculumHyperData.py OntologyReasoner.py loadData.py cabascModel.py prepareBERT.py lcrModelAlt_hierarchical_v3.py lcrModelAlt_hierarchical_v4_baby_steps.py prepareELMo.py svmModel.py lcrModel.py dataReader2016.py utils.py getBERTusingColab.py main_hyper.py lcrModelAlt_hierarchical_v4_trainevaltest.py sentiWordNet.py lcrModelAlt_hierarchical_v2.py lcrModelInverse.py main.py lcrModelAlt_hierarchical_v4.py nn_layer.py att_layer.py lcrModelAlt_hierarchical_v1.py lcrModelAlt_hierarchical_v4_one_pass.py config.py lcrModelAlt_hierarchical_v4_hyper_opt.py softmax_with_len Mlp_attention_layer mlp_attention_layer mlp_layer bilinear_attention_layer dot_produce_attention_layer cam_mlp_attention_layer triple_attention_layer mlp_layer2 triple_attention_layer2 main cabasc main _get_data_tuple read_data_2016 window ff_model main lcr_rot main lcr_rot main lcr_rot main lcr_rot main lcr_rot main lcr_rot main main lcr_inv_objective print_json load_best_hyperspace cabasc_objective save_json_result run_a_trial load_json_result plot_best_model lcr_alt_objective lcr_objective svm_objective dynamic_rnn softmax_layer stack_bi_dynamic_rnn bi_dynamic_rnn_diff reduce_mean_with_len cnn_layer bi_dynamic_rnn OntReasoner get_categories tokenize_data scaled_scores get_sent_length main complete_sentences main exp sequence_mask reshape float32 reduce_sum shape cast as_list reshape matmul expand_dims get_variable as_list softmax_with_len reshape matmul get_variable softmax_with_len tanh reshape matmul get_variable sigmoid reshape matmul get_variable tanh softmax_with_len reshape transpose matmul get_variable softmax_with_len tanh reshape matmul get_variable reshape matmul tanh get_variable tanh reshape matmul softmax get_variable reshape matmul tanh get_variable Sequential accuracy_score argmax list len Adam add Dropout append sum range predict subtract square Dense zip compile print fit embedding_dim n_class random_base multiply max_sentence_len squeeze matmul add reverse expand_dims GRUCell n_hidden softmax_layer dropout tile triple_attention_layer dynamic_rnn print l2_reg reshape mlp_layer cam_mlp_attention_layer print_config ConfigProto append next range iter list window len min lower append range enumerate _get_data_tuple most_common open str word_tokenize getroot iter append get parse replace close lower print text write extend sub findall len flush list print Sequential fit Adam predict add len Dense append accuracy_score argmax Activation range compile Dropout dropout print softmax_layer max_sentence_len squeeze concat matmul LSTMCell bilinear_attention_layer n_class reduce_mean_with_len bi_dynamic_rnn n_hidden random_base str range dot_produce_attention_layer expand_dims test_path reset_default_graph test_path_ont open test_svm_path run OntReasoner loadDataAndEmbeddings hyper_train_path remaining_svm_test_path num_buckets format remaining_test_path sorted_indices hyper_eval_path load train_path str year str print save_json_result main reset_default_graph str print save_json_result hyper_train_path reset_default_graph main hyper_eval_path str print save_json_result hyper_train_path reset_default_graph main hyper_eval_path str print save_json_result hyper_train_path reset_default_graph main hyper_eval_path str print save_json_result hyper_svm_eval_path reset_default_graph hyper_svm_train_path main load dump format print trials fmin open len print dumps format makedirs join print load_best_hyperspace print_json conv2d relu get_variable sequence_mask reshape cell float32 shape reverse cast tile reduce_mean_with_len gather range reshape concat int64 cast reverse_sequence bidirectional_dynamic_rnn reduce_mean_with_len gather range concat cells_fw reshape concat stack_bidirectional_dynamic_rnn int64 cast reverse_sequence reduce_mean_with_len gather range cells_bw split reshape cast float32 reduce_sum get_variable RegexpTokenizer rstrip readlines append tokenize range len append range len insert append index zip append zip reshape OneHotEncoder LabelEncoder array fit_transform len get_categories lesk tokenize_data asarray scaled_scores minmax_scale concatenate name abs astype senti_synset pos_tag get_sent_length vstack startswith complete_sentences A score reshape OneHotEncoder LabelEncoder split CountVectorizer polarity_scores SentimentIntensityAnalyzer expand_dims array fit_transform | # CL-HAABSA Implementation of curriculum learning (CL) in HAABSA++ Code for implementing curriculum learning in the two-step Hybrid Approach for Aspect-Based Sentiment Analysis (HAABSA) with contextual word embeddings and hierarchical attention (HAABSA++). The HAABSA++ paper can be found via: https://arxiv.org/pdf/2004.08673.pdf. The HAABSA paper can be found via: https://personal.eur.nl/frasincar/papers/ESWC2019/eswc2019.pdf. The HAABSA++ model uses a domain sentiment ontology and a neural network as backup. Curriculum learning (CL) is used to improve the results of the HAABSA++ model. ## Software Installation First, the right environment needs to be set up and the right files need to be downloaded. This can be done by following the installation instructions given at https://github.com/ofwallaart/HAABSA. Only the files mentioned in the Read Me have to be downloaded, there is no need to download the HAABSA files. Hereafter, the right word embeddings need to be downloaded via: https://github.com/mtrusca/HAABSA_PLUS_PLUS. Again, only the files mentioned in the Read Me should be downloaded. After completing the instructions, all the CL-HAABSA files need to be installed into the newly created environment. An explanation of the CL-HAABSA files is given below. ## Software Explanation | 774 |
Nanne/pytorch-NetVlad | ['visual place recognition', 'image retrieval'] | ['NetVLAD: CNN architecture for weakly supervised place recognition'] | main.py netvlad.py pittsburgh.py tokyo247.py L2Norm test save_checkpoint train get_clusters Flatten NetVLAD get_250k_test_set get_250k_val_query_set QueryDatasetFromStruct get_whole_val_set WholeDatasetFromStruct get_whole_training_set collate_fn parse_dbStruct get_250k_val_set get_val_query_set get_whole_test_set input_transform get_training_query_set get_whole_val_set WholeDatasetFromStruct get_whole_training_set collate_fn parse_dbStruct get_val_query_set QueryDatasetFromStruct input_transform get_training_query_set batchSize arange pool zero_grad DataLoader cacheRefreshRate memory_allocated shape memory_cached ceil encoder to sum range cat format array_split cachePath cache whichSet eval item enumerate join remove backward print add_scalar Subset split empty_cache step len str IndexFlatL2 format add_scalar print astype search add eval DataLoader any in1d getPositives numQ zeros max enumerate len join str SubsetRandomSampler makedirs num_clusters choice dataPath DataLoader arch ceil dataset len copyfile join savePath save join join join join join join join join T loadmat item list default_collate filter zip chain cat | # pytorch-NetVlad Implementation of [NetVlad](https://arxiv.org/abs/1511.07247) in PyTorch, including code for training the model on the Pittsburgh dataset. ### Reproducing the paper Below are the result as compared to the results in third row in the right column of Table 1: | |R@1|R@5|R@10| |---|---|---|---| | [NetVlad paper](https://arxiv.org/abs/1511.07247) | 84.1 | 94.6 | 95.5 | | pytorch-NetVlad(alexnet) | 68.6 | 84.6 | 89.3 | | pytorch-NetVlad(vgg16) | 85.2 | 94.8 | 97.0 | Running main.py with train mode and default settings should give similar scores to the ones shown above. Additionally, the model state for the above run is | 775 |
Nanyuu/TUA | ['adversarial attack'] | ['A Targeted Universal Attack on Graph Convolutional Network'] | train.py fold_model/layers.py fold_model/GCN_Model.py fold_model/__init__.py fold_util/F_Test.py fold_util/F_Sub_graph.py test.py fold_attack/Targeted_GUA.py fold_util/F_Info.py fold_data/dataset.py fold_util/F_Perturbation.py config.py citation.py fold_data/__init__.py fold_util/F_Normalize.py fold_util/__init__.py opts test F_accuracy train F_lr_scheduler Att_group_node_multi_attack_nodes c_dataset_loader GCN GraphConvolution C_per_info F_one_hot_to_label normalize_adj_degree normalize_feat normalize_adj nor_sub_adj_eye Per_add_fake_feat_based_on_grad_multi_attack_nodes Per_add_fake_node construct_sub_graph find_neighbor_idx Test_attack_success_rate_for_Class_Node normalize_feat model F_accuracy dataset cuda list A normalize_adj_degree shape format feature_Nor eval model_path is_available float long load print cpu normalize_feat arange model F_accuracy zero_grad SGD numpy save dataset cuda seed list A normalize_adj_degree Adam GCN epoch format LongTensor F_lr_scheduler nll_loss np_random_seed feature_Nor eval lr mkdir manual_seed is_available float long flush join backward write parameters step sum type_as double fake_node_num_each_attack_node normalize_feat arange model Per_add_fake_node where unsqueeze save dataset cuda temp_test_time Test_attack_success_rate_for_Class_Node list attack_node_num ancillary_node_np range F_find_neighbor_idx format setdiff1d copy choice eval item float limit_fake_feat int use_cuda backward print Per_add_fake_feat_based_on_grad_multi_attack_nodes makedirs attack_node_np tqdm zeros numpy ancillary_node_num nor_sub_adj_eye F_construct_sub_graph diags csr_matrix dot sum array A diags csr_matrix transpose flatten dot eye sum array A csr_matrix transpose flatten dot eye diags A diags csr_matrix flatten sum array zeros range int sort copy flatten unique range append __contains__ array range diag copy zeros sum range float range item | # Target Universal Attack on GCN (TUA) ## Introdction An implementation of the Target Universal Adversarial Attack on Semi-supervised Graph Convolutional Neural Network. ## Requirements Check the requirements.txt + Python + Pytorch + networkx tqdm scipy numpy ## Usage Example: | 776 |
Narp99/FRI_2018 | ['optical character recognition', 'scene text detection', 'scene text recognition'] | ['STN-OCR: A single Neural Network for Text Detection and Text Recognition'] | mxnet/utils/extract_last_image_from_gif.py datasets/fsns/transform_gt.py mxnet/initializers/spn_initializer.py mxnet/metrics/ctc_metrics.py datasets/svhn/filter_large_images.py mxnet/utils/create_video.py mxnet/networks/text_rec.py mxnet/train_fsns.py mxnet/utils/datatypes.py datasets/fsns/slice_fsns_dataset.py mxnet/operations/ones.py mxnet/eval_text_recognition_model.py datasets/fsns/download_fsns.py mxnet/metrics/test_ctc_loss.py mxnet/data_io/fsns_file_iter.py datasets/svhn/create_svhn_dataset.py mxnet/operations/disable_shearing.py mxnet/utils/extract_images.py datasets/svhn/create_svhn_dataset_4_images.py mxnet/utils/create_gif.py mxnet/train_utils.py mxnet/data_io/file_iter.py mxnet/train_text_recognition.py mxnet/eval_svhn_model.py mxnet/metrics/base.py mxnet/symbols/lstm.py datasets/fsns/swap_classes.py datasets/fsns/tfrecord_utils/tfrecord_to_image.py mxnet/eval_fsns_model.py mxnet/networks/svhn.py mxnet/operations/debug.py mxnet/utils/create_record_io_file.py mxnet/utils/reorder_lst_file.py mxnet/train_svhn.py mxnet/utils/plot_log.py mxnet/utils/bbox_utils.py datasets/svhn/svhn_dataextract_tojson.py mxnet/networks/fsns.py mxnet/metrics/fsns_metrics.py mxnet/callbacks/create_checkpoint.py mxnet/utils/show_progress.py mxnet/metrics/ctc/setup.py mxnet/utils/create_text_rec_gt.py mxnet/data_io/lstm_iter.py mxnet/callbacks/fsns_bbox_plotter.py mxnet/callbacks/save_bboxes.py datasets/fsns/render_text_on_signs.py get_image random_crop get_image_paths get_labels save_image find_font_size find_way_to_common_dir intersects intersects_bbox GaussianSVHNCreator intersection is_close overlap SVHNCreator BBox SVHNDatasetCreator get_images DigitStructFile plot_bboxes get_model plot_bboxes get_model plot_bboxes get_model parse_args init_logging fit get_create_checkpoint_callback FSNSBBOXPlotter BBOXPlotter FileBasedIter _load_worker _load_image FSNSFileIter DataIterDecorator InitStateLSTMIter LSTMIter SPNInitializer ShapeAgnosticLoad STNCrossEntropy STNAccuracy CTCSTNAccuracy remove_blank CTCLoss strip_prediction tile_batch_size FSNSPretrainAccuracy FSNSPretrainCrossEntropy softmax FSNSNetwork SVHNLocalizationNetwork SVHNMultiLineResNetNetwork SVHNMultiLineCTCNetwork SVHNMultiLineResNetNetwork SVHNMultiLineCTCNetwork LocalizationNetwork Debug DebugProp DisableShearingProp DisableShearing ProvideOnes ProvideOnesProp lstm lstm_unroll meshgrid get_sampling_grid make_gif makedelta create_loop_header intToBin get_gt_item make_video LogPlotter split_list ProgressWindow ImageDataHandler ImageServer walk extend truetype multiline_textsize choice height min choice width range join str format get_subdir save makedirs append extend len append split height width left overlap top set_params model_epoch Module bind load_checkpoint model_prefix get_network Group zeros asnumpy save_extracted_regions get_area_data get_outputs join blank_label argmax strip_prediction add_argument ArgumentParser str setFormatter log_dir int info getLogger addHandler StreamHandler upper Formatter rank mkdir log_file setLevel FileHandler progressbar batch_size model_prefix kv_store Speedometer create clip_gradient get_create_checkpoint_callback dirname init_logging append checkpoint_interval ProgressBar load_epoch FactorScheduler load join log_dir FeedForward save_model_prefix makedirs get put array split append list zip shape tile c SliceChannel Activation FullyConnected SliceChannel FullyConnected Reshape lstm h LSTMState LSTMParam reversed Concat append range height ones shape vstack linspace width prod meshgrid shape reshape matmul int getdata write subtract_modulo copy getbbox crop join sorted print _make filter append listdir compile enumerate join sorted ImageData print filter append listdir compile enumerate len | # STN-OCR: A single Neural Network for Text Detection and Text Recognition This repository contains the code for the paper: [STN-OCR: A single Neural Network for Text Detection and Text Recognition](https://arxiv.org/abs/1707.08831) # Please note that we refined our approach and released new source code. You can find the code [here](https://github.com/Bartzi/see) Please use the new code, if you want to experiment with FSNS like data and our approach. It should also be easy to redo the text recognition experiments with the new code, although we did not release any code for that. # Structure of the repository The folder `datasets` contains code related to datasets used in the paper. `datasets/svhn` contains several scripts that can be used to create svhn based ground truth files as used in our experiments reported in section 4.2., please see the readme in this folder on how to use the scripts. `datasets/fsns` contains scripts that can be used to first download the fsns dataset, second extract the images from the downloaded files and third restructure the contained gt files. The folder `mxnet` contains all code used for training our networks. # Installation | 777 |
NataliaSkorokhod/neural-style-transfer | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | NeuralStyleTransfer.py | # neural_style_transfer Creating artistic images via neural style transfer. This project is based on the idea presented by Gatys et al. in the paper "A Neural Algorithm of Artistic Style". https://arxiv.org/abs/1508.06576 The code is based on the neural style transfer example given in the Keras documentation. Below are some examples of images created using this code.      | 778 |
NateKoenig/https-github.com-Kautenja-a-neural-algorithm-of-artistic-style | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | neural_stylization/util/img_util.py neural_stylization/optimizers/adam.py neural_stylization/util/build_callback.py neural_stylization/util/jupyter_plot.py neural_stylization/util/__init__.py neural_stylization/reconstruct_content.py neural_stylization/vgg19.py neural_stylization/optimizers/gd.py neural_stylization/loss_functions.py neural_stylization/reconstruct_style.py neural_stylization/optimizers/l_bfgs.py neural_stylization/optimizers/__init__.py frames_to_video.py neural_stylization/__init__.py neural_stylization/transfer_style.py pairs total_variation_loss gram content_loss style_loss reconstruct_content reconstruct_style Stylizer download_include_top VGG_19 Adam GradientDescent L_BFGS build_callback matrix_to_image normalize denormalize image_to_matrix load_image JupyterPlot next tee shape reshape int abs square clear_session constant normalize function VGG_19 concatenate optimize image_to_matrix placeholder shape uniform content_loss load_image clear_session constant normalize function VGG_19 concatenate optimize image_to_matrix variable placeholder shape uniform load_image style_loss glob remove format makedirs resize open expand_dims asarray astype copy copy | # A Neural Algorithm of Artistic Style (Implementation)
An **implementation** of the arXiv preprint
[_A Neural Algorithm of Artistic Style [1]_](#references)
& paper
[_Image Style Transfer Using Convolutional Neural Networks [2]_](#references).
## Original Photograph: _Tubingen, Germany_
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NathanGavenski/IUPE | ['imitation learning'] | ['Imitating Latent Policies from Observation', 'Imitating Unknown Policies via Exploration', 'Behavioral Cloning from Observation'] | train.py models/general/mlp.py utils/utils.py models/general/empty.py datasets/dataset_Acrobot.py test.py datasets/dataset_Maze.py utils/enjoy.py models/general/resnet.py datasets/dataset_CartPole.py models/general/attention.py utils/args.py models/idm.py models/policy.py datasets/dataset_MountainCar.py read_vector detect_path split_dataset Policy_Vector_Dataset balance_dataset get_idm_vector_dataset IDM_Vector_Dataset get_policy_vector_dataset read_vector detect_path split_dataset Policy_Vector_Dataset balance_dataset get_idm_vector_dataset IDM_Vector_Dataset get_policy_vector_dataset Policy_Dataset read_data detect_path split_dataset get_idm_dataset ExpertMaze IDM_Dataset balance_dataset get_policy_dataset read_vector detect_path split_dataset get_idm_dataset Policy_Vector_Dataset balance_dataset get_policy_dataset get_idm_vector_dataset IDM_Vector_Dataset get_policy_vector_dataset IDM train validation validation train Policy Self_Attn2D Self_Attn1D Empty MlpAttention MlpWithAttention MLP ResnetLast normalize_imagenet ResnetAll Resnet create_resnet ResnetFirst save_vector_alpha delete_alpha save_alpha get_min_reward play_vector get_environment define_goal play performance EnjoyController load_policy_model create_alpha_dataset save_policy_model create_alpha_vector_dataset load_idm_model save_gif save_idm_model policy_infer CheckpointAlreadyExists defaultdict ndarray read_vector list ndarray print min astype choice append range len from_numpy from_numpy split_dataset DataLoader balance_dataset IDM_Vector_Dataset split_dataset DataLoader balance_dataset Policy_Vector_Dataset read_data ndarray range bincount Compose Compose split_dataset IDM_Dataset balance_dataset DataLoader Policy_Dataset DataLoader split_dataset balance_dataset criterion model to backward step zero_grad item add_grid argmax model eval item add_grid to argmax idm_model add_histogram eval idm_model add_histogram clone fromarray makedirs write close save exists enumerate open makedirs write close exists enumerate open rmtree remove get_performance_rewards arange model numpy define_goal tensor argmax transforms ndarray view get_min_reward render append to double choice int reshape convert save_alpha reset step array EnjoyController arange model numpy define_goal tensor argmax transforms ndarray view get_min_reward render append to double save_vector_alpha choice int reshape convert reset step array EnjoyController dict mkdir save state_dict dict mkdir save state_dict load eval load_state_dict Policy to IDM load actions eval load_state_dict to save makedirs play_function ndarray print delete_alpha close training next eval get_environment save_gif performance append dataset transforms range int defaultdict len close makedirs open append max range split int defaultdict print len close makedirs open append range split | # Imitating Unknown Policies via Exploration (IUPE) Official Pytorch implementation of [Imitating Unknown Policies via Exploration](https://arxiv.org/pdf/2008.05660.pdf) --- Behavioral cloning is an imitation learning technique that teaches an agent how to behave through expert demonstrations. Recent approaches use self-supervision of fully-observable unlabeled snapshots of the states to decode state-pairs into actions. However, the iterative learning scheme from these techniques are prone to getting stuck into bad local minima. We address these limitations incorporating a two-phase model into the original framework, which learns from unlabeled observations via exploration, substantially improving traditional behavioral cloning by exploiting (i) a sampling mechanism to prevent bad local minima, (ii) a sampling mechanism to improve exploration, and (iii) self-attention modules to capture global features. Imitating Unknown Policies via Exploration (IUPE) combines both an *Inverse Dynamics Model* (IDM) to infer actions in a self-supervised fashion, and a *Policy Model* (PM), which is a function that tells the agent what to do in each possible state of the environment. IUPE further augments the Behavioral Cloning from Observations framework with two strategies for avoiding local minima, sampling and exploration, and with self-attention modules for improving the learning of global features and, hence, generalization. <p align="center"> <img src="https://github.com/NathanGavenski/IUPE/blob/master/images/iupe_flow_diagram.svg" width="75%" /> | 780 |
Navhkrin/Bts-PyTorch | ['depth estimation', 'monocular depth estimation'] | ['From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation'] | configs.py KittiDataLoader.py Train.py Test.py BTS.py Reduction ASSPBlock LPGLayer bts_model BtsController bts_encoder UpscaleBlock LPGBlock bts_decoder UpscaleNetwork SilogLoss UpscaleLayer AtrousBlock KittiDataset KittiDataLoader CalculateLosses Unnormalize predict KittiDataset add_ zip reshape item masked_select SilogLoss tensor Silog transpose squeeze | # BTS - PyTorch This repository contains the unofficial PyTorch implementation of From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation [Paper](https://arxiv.org/abs/1907.10326) [Official Tensorflow Implementation](https://github.com/cogaplex-bts/bts) This repository is tested on Windows and Ubuntu on PyTorch 1.2, 1.3 and 13.1, installed from pip and built from source. Kitti Validation results: | Model | Silog | rmse | rmse log | abs relative | sqrt relative | | --------------- | ------ | ----- | -------- | ------------ | ------------- | | BTS - PyTorch | 9.83 | 3.03 | 0.10 | 0.06 | 0.29 | | BTS - Official | 9.08 | 2.82 | 0.09 | 0.05 | 0.26 | | 781 |
Neerudubey/Unsupervised-Eye-gaze-estimation | ['gaze estimation'] | ['Unsupervised Learning of Eye Gaze Representation from the Web'] | Tablet_data_process.py | Neerudubey/Unsupervised-Eye-gaze-estimation | 782 |
NervanaSystems/ngraph-python | ['graph partitioning'] | ['Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning'] | examples/ptb/char_rae.py ngraph/transformers/gpu/util.py ngraph/frontends/mxnet/tests/test_utils.py ngraph/testing/execution.py ngraph/frontends/neon/data/cifar100.py ngraph/frontends/tensorflow/tests/test_ops_reduction.py ngraph/frontends/caffe2/tests/test_ops_binary.py ngraph/transformers/gpu/pool.py tests/test_op_metadata.py ngraph/frontends/neon/data/mnist.py ngraph/frontends/onnx/tests/test_ops_batchnorm.py ngraph/frontends/tensorflow/examples/mnist_mlp_save_load.py examples/benchmarks/fake_data_generator.py ngraph/frontends/neon/tests/test_parameter_reuse.py examples/dqn/dqn_atari.py ngraph/frontends/onnx/onnx_importer/ops_bridge.py examples/video-c3d/data.py examples/mnist/data.py ngraph/op_graph/tensorboard/summary.py ngraph/transformers/passes/verify.py ngraph/frontends/onnx/onnx_importer/__init__.py integration_tests/tensorboard/test_tensorboard.py doc/source/ngraph_theme/__init__.py ngraph/frontends/caffe2/c2_importer/utils.py ngraph/frontends/neon/activation.py examples/char_conv/dataset.py ngraph/frontends/neon/tests/test_activations.py examples/dqn/simple_environments.py examples/timeseries/timeseries_lstm.py ngraph/op_graph/comm_nodes.py examples/gan/mnist_gan.py ngraph/frontends/neon/argparser.py doc/source/conf.py tests/test_liveness.py ngraph/frontends/neon/data/__init__.py ngraph/transformers/passes/nviz.py ngraph/transformers/passes/liveness.py ngraph/frontends/mxnet/tests/__init__.py ngraph/op_graph/tensorboard/record_writer.py conftest.py ngraph/transformers/gpu/kernels/winograd_conv.py ngraph/transformers/gpu/kernels/kernel_specs.py ngraph/frontends/neon/tests/test_saver.py examples/ptr-net/ptr-net.py examples/cifar10/cifar10_msra.py ngraph/frontends/caffe2/c2_importer/ops_unary.py ngraph/frontends/cntk/tests/__init__.py examples/ptr-net/utils.py examples/video-c3d/video_c3d.py examples/dqn/gym_wrapper.py examples/dqn/rl_loop.py examples/deepspeech/decoder.py ngraph/frontends/neon/tests/test_dropout.py tests/test_axes.py ngraph/transformers/gputransform.py ngraph/flex/base.py ngraph/transformers/gpu/kernels/cuda/cuda_templates.py ngraph/frontends/onnx/onnx_importer/model_wrappers.py ngraph/testing/error_check.py examples/wide_deep/model.py ngraph/frontends/neon/graph.py ngraph/frontends/caffe2/examples/linear_regression.py ngraph/frontends/neon/data/tsp.py examples/dist_hetr.py ngraph/transformers/gpu/lut.py ngraph/frontends/onnx/__init__.py examples/deepspeech/data.py ngraph/frontends/caffe/cf_importer/caffe_pb2.py tests/transformer_dependent/test_flatten.py ngraph/frontends/neon/initializer.py ngraph/transformers/gpu/flex_pool.py ngraph/frontends/onnx/onnx_importer/utils/conv.py ngraph/frontends/neon/tests/test_batchnorm_layer.py examples/text_generation.py ngraph/op_graph/ctc.py tests/transformer_dependent/test_ssa.py ngraph/transformers/gpu/tensor_ops.py tests/flex_tests/test_flexew/test_redop.py ngraph/frontends/onnx/onnx_importer/importer.py ngraph/util/trace_events.py ngraph/frontends/common/tests/test_lr.py examples/wide_deep/data.py ngraph/frontends/cntk/examples/feed_forward.py ngraph/util/names.py examples/inceptionv3/data.py examples/gan/utils.py ngraph/op_graph/control.py ngraph/transformers/passes/memlayout.py ngraph/frontends/neon/__init__.py ngraph/transformers/passes/dumpgraphpass.py examples/gan/models_lsun.py tests/transformer_dependent/test_pool.py examples/gan/lsun_wgan.py examples/walk_through/gendata.py examples/imdb/imdb_rnn.py ngraph/frontends/tensorflow/examples/minimal.py examples/dqn/dqn.py examples/dqn/dqn_cartpole.py ngraph/transformers/gpu/float_ew2.py ngraph/op_graph/lookuptable.py ngraph/frontends/caffe/cf_importer/importer.py ngraph/frontends/neon/callbacks.py ngraph/op_graph/tensorboard/tensorboard.py tests/test_names.py tests/transformer_dependent/test_tensor_size.py examples/mnist/mnist_mlp.py ngraph/frontends/tensorflow/tests/importer_tester.py ngraph/frontends/cntk/tests/test_ops_binary.py examples/inceptionv3/inception.py examples/ptr-net/tsp_seqarrayiter.py tests/test_serde.py ngraph/frontends/cntk/cntk_importer/ops_unary.py ngraph/testing/conv_utils.py examples/gan/toy_examples/toy_wgan.py examples/char_conv/char_conv.py ngraph/frontends/tensorflow/tests/test_ops_transform.py ngraph/frontends/tensorflow/tests/test_ops_binary.py ngraph/transformers/hetr/hetr_pb2.py examples/dqn/test_dqn_make_axes.py ngraph/op_graph/tensorboard/__init__.py ngraph/frontends/caffe2/c2_importer/importer.py ngraph/frontends/onnx/tests/test_reshape.py ngraph/frontends/neon/aeon_shim.py ngraph/frontends/caffe/examples/sum.py ngraph/frontends/cntk/examples/mnist_scoring.py ngraph/util/pygen.py ngraph/frontends/cntk/cntk_importer/ops_binary.py tests/flex_tests/test_flexew/test_flexew_inequalities.py ngraph/frontends/cntk/tests/test_ops_compoud.py examples/gan/toy_examples/toy_mlp_gan.py ngraph/frontends/neon/arrayiterator.py tests/transformer_dependent/test_lookuptable.py ngraph/__init__.py ngraph/frontends/neon/tests/test_multiple_inputs.py ngraph/transformers/passes/mkldnnpasses.py tests/hetr_tests/test_hetr_utils.py ngraph/frontends/caffe2/tests/test_examples.py ngraph/frontends/tensorflow/tests/test_ops_matmul.py ngraph/frontends/onnx/tests/test_ops_reduction.py ngraph/frontends/tensorflow/tests/debug_tester.py ngraph/frontends/neon/axis.py ngraph/transformers/__init__.py ngraph/frontends/cntk/examples/cifar_prepare.py tests/transformer_dependent/test_dimshuffle.py ngraph/frontends/neon/tests/test_axis.py ngraph/flex/names.py ngraph/testing/decorators.py examples/timeseries/utils.py ngraph/transformers/gpu/kernels/cuda/lookuptable.py ngraph/op_graph/serde/serde_pass.py ngraph/op_graph/tensorboard/tensorboardpass.py ngraph/frontends/caffe2/examples/sum.py ngraph/transformers/gpu/kernel.py ngraph/frontends/neon/saver.py ngraph/frontends/neon/data/librispeech.py ngraph/transformers/passes/gpusimplification.py ngraph/frontends/onnx/tests/test_backend.py ngraph/frontends/neon/logging.py examples/cifar10/cifar10_mlp.py tests/config/jupyter_nbconvert_config.py ngraph/frontends/onnx/onnx_importer/utils/pool.py ngraph/transformers/extransform.py ngraph/transformers/hetrtransform.py ngraph/frontends/neon/data/cifar10.py ngraph/frontends/tensorflow/tf_importer/__init__.py ngraph/transformers/passes/exnviz.py ngraph/transformers/base.py ngraph/frontends/tensorflow/tests/test_ops_constant.py tests/hetr_tests/test_comm_nodes.py tests/transformer_dependent/test_conv.py ngraph/frontends/tensorflow/examples/mnist_mlp.py ngraph/frontends/neon/tests/recurrent_ref.py ngraph/transformers/cpu/cpuengine.py ngraph/util/persist.py tests/test_pygen.py ngraph/frontends/caffe2/tests/test_ops_nn.py ngraph/frontends/tensorflow/tf_importer/ops_bridge.py ngraph/frontends/onnx/onnx_importer/utils/misc.py examples/convnet-benchmarks/vgg_a.py ngraph/op_graph/serde/serde.py ngraph/frontends/cntk/cntk_importer/ops_bridge.py ngraph/transformers/cpu/ctc.py tests/flex_tests/test_flexconv.py ngraph/transformers/passes/opdelegate.py ngraph/frontends/caffe2/tests/test_utils.py ngraph/frontends/onnx/tests/test_ops_convpool.py examples/ptb/char_lstm.py tests/hetr_tests/test_hetr_mnist.py tests/transformer_dependent/test_rngop.py ngraph/testing/flex_util.py ngraph/frontends/tensorflow/tests/test_ops_unary.py ngraph/frontends/neon/optimizer.py ngraph/frontends/neon/data/shakespeare.py ngraph/util/generics.py tests/flex_tests/test_gemm.py ngraph/frontends/tensorflow/examples/mnist_mlp_shaped.py ngraph/frontends/onnx/tests/test_ops_binary.py examples/cifar10/data.py ngraph/transformers/passes/flexfusion.py ngraph/frontends/tensorflow/tests/test_computations.py examples/convnet-benchmarks/googlenet_v1.py ngraph/testing/hetr_utils.py ngraph/testing/__init__.py examples/cifar10/cifar10_conv.py ngraph/transformers/cputransform.py tests/flex_tests/test_flexew/test_flexew_basic_math.py ngraph/frontends/caffe/cf_importer/ops_binary.py tests/transformer_dependent/test_execution.py ngraph/transformers/passes/memoptimize.py setup.py flex_examples/x_plus_const.py tests/transformer_dependent/test_logreg.py examples/wide_deep/train.py ngraph/transformers/hetr/hetr_pb2_grpc.py ngraph/op_graph/serde/ops_pb2.py tests/test_generics.py tests/transformer_dependent/test_transformer.py ngraph/frontends/caffe2/c2_importer/ops_binary.py ngraph/transformers/hetr/rpc_client.py ngraph/op_graph/debug.py ngraph/frontends/tensorflow/tf_importer/importer.py examples/inceptionv3/inceptionv3.py tests/test_graph_pass.py ngraph/op_graph/serde/serde_weights.py ngraph/frontends/caffe2/tests/test_ops_constant.py ngraph/frontends/neon/tests/test_linear_layer.py tests/transformer_dependent/test_ctc.py examples/resnet/resnet.py ngraph/frontends/cntk/cntk_importer/__init__.py ngraph/transformers/passes/flexpass.py ngraph/frontends/cntk/cntk_importer/importer.py ngraph/frontends/cntk/__init__.py examples/ptb/char_rnn.py examples/deepspeech/deepspeech.py tests/hetr_tests/test_hetr_integration.py ngraph/frontends/neon/layer.py ngraph/frontends/neon/tests/test_arrayiterator.py ngraph/frontends/onnx/onnx_importer/utils/axes.py ngraph/frontends/neon/tests/test_conv_layer.py ngraph/transformers/cpu/batchnorm.py ngraph/frontends/tensorflow/tf_importer/utils_broadcast.py ngraph/frontends/caffe/tests/test_constant_ops.py ngraph/transformers/gpu/flex_conv.py ngraph/transformers/exop.py examples/convnet-benchmarks/overfeat.py tests/test_debug.py ngraph/transformers/passes/passes.py ngraph/op_graph/tensorboard/graph_def.py ngraph/frontends/caffe2/c2_importer/ops_constant.py tests/transformer_dependent/test_constant.py ngraph/frontends/neon/utils.py examples/dqn/test_integration_dqn.py ngraph/frontends/tensorflow/tests/test_utils.py tests/transformer_dependent/test_serde_weights.py ngraph/transformers/passes/cpufusion.py ngraph/frontends/neon/saverfile.py examples/gan/lsun_data.py ngraph/transformers/passes/visualizemem.py tests/transformer_dependent/test_dot.py ngraph/frontends/onnx/tests/utils.py ngraph/frontends/neon/data/imdb.py ngraph/frontends/onnx/tests/__init__.py tests/transformer_dependent/test_axes_transformer_dependent.py ngraph/frontends/caffe/tests/test_binary_ops.py ngraph/op_graph/axes.py ngraph/frontends/onnx/onnx_importer/backend.py ngraph/transformers/passes/layout.py ngraph/frontends/cntk/examples/cifar_scoring.py integration_tests/test_walkthrough.py ngraph/frontends/onnx/tests/test_ops_logical.py examples/resnet/ingest.py ngraph/frontends/caffe2/c2_importer/ops_nn.py ngraph/transformers/cpu/hetr.py examples/timeseries/timeseries.py examples/resnet/train_resnet.py ngraph/transformers/gpu/conv.py ngraph/types/__init__.py ngraph/frontends/neon/tests/test_lstm.py third_party/warp_ctc/include_header.py ngraph/transformers/passes/exopdelegate.py ngraph/frontends/onnx/tests/test_model_wrappers.py ngraph/frontends/common/learning_rate_policies.py ngraph/frontends/cntk/examples/cifar_training.py ngraph/frontends/common/legacy_binary_ew_op.py examples/video-c3d/plot.py ngraph/frontends/caffe/cf_importer/ops_constant.py ngraph/frontends/cntk/examples/__init__.py ngraph/frontends/neon/tests/test_layer_subgraph.py ngraph/frontends/neon/tests/test_recurrent.py ngraph/frontends/cntk/cntk_importer/ops_compound.py ngraph/frontends/cntk/examples/mnist_training.py tests/transformer_dependent/test_sum.py tests/transformer_dependent/test_kernel_generator.py ngraph/frontends/neon/data/lsun.py ngraph/frontends/neon/model.py tests/transformer_dependent/test_dimshuffle_op.py examples/dqn/test_dqn_model.py ngraph/frontends/common/utils.py ngraph/frontends/tensorflow/tf_importer/utils.py ngraph/frontends/tensorflow/tests/test_ops_nn.py ngraph/op_graph/pooling.py ngraph/util/threadstate.py examples/benchmarks/benchmark.py tests/transformer_dependent/test_op_graph_transformer_dependent.py ngraph/frontends/neon/tests/lstm_ref.py ngraph/factory/comm_node_factory.py ngraph/frontends/caffe2/examples/mnist_mlp.py ngraph/flex/__init__.py tests/test_op_graph.py ngraph/frontends/neon/tests/test_rnn_cells.py ngraph/transformers/flexgputransform.py tests/flex_tests/test_flexsmoke.py ngraph/frontends/onnx/tests/test_ops_nonlinear.py ngraph/frontends/tensorflow/examples/logistic_regression.py ngraph/transformers/util/layout_common.py ngraph/op_graph/op_graph.py ngraph/transformers/hetr/mpilauncher.py ngraph/frontends/caffe2/c2_importer/__init__.py ngraph/transformers/gpu/gpulayout.py ngraph/frontends/caffe2/examples/fc.py ngraph/transformers/gpu/kernels/cuda/copy_transpose.py ngraph/frontends/onnx/tests/test_ops_unary.py ngraph/frontends/neon/data/ptb.py ngraph/transformers/passes/expass.py ngraph/frontends/onnx/tests/test_ops_variadic.py ngraph/frontends/tensorflow/tf_importer/ngraph_shaped.py ngraph/flex/flexargparser.py ngraph/frontends/onnx/tests/test_graph_import.py ngraph/transformers/passes/cpulayout.py tests/transformer_dependent/test_stack_tensor.py ngraph/frontends/tensorflow/tests/utils.py examples/benchmarks/mnist_lenet.py ngraph/frontends/tensorflow/tests/test_ops_variable.py ngraph/frontends/onnx/tests/test_ops_matmul.py examples/resnet/utils.py ngraph/frontends/cntk/examples/logistic_regression.py ngraph/transformers/gpu/kernels/convolution.py tests/transformer_dependent/test_reshaping.py ngraph/transformers/passes/hetrpasses.py third_party/warp_ctc/ctc.py ngraph/frontends/tensorflow/tf_importer/utils_pos_axes.py examples/dqn/test_dqn_wrappers.py ngraph/transformers/hetr/hetr_server.py ngraph/transformers/gpu/gemm.py ngraph/op_graph/convolution.py ngraph/frontends/cntk/examples/minimal.py ngraph/testing/random.py examples/gan/toy_examples/toy_utils.py ngraph/frontends/neon/tests/conftest.py ngraph/frontends/caffe/cf_importer/ops_bridge.py ngraph/frontends/neon/tests/test_optimizer.py ngraph/frontends/caffe2/c2_importer/ops_bridge.py ngraph/transformers/cpu/relu.py ngraph/frontends/tensorflow/tests/test_examples.py ngraph/frontends/onnx/onnx_importer/utils/reduction.py ngraph/transformers/gpu/ctc.py examples/resnet/data.py ngraph/frontends/tensorflow/tests/test_ngraph_shaped.py ngraph/transformers/gpu/flex_lut.py ngraph/frontends/cntk/tests/test_ops_unary.py tests/hetr_tests/test_hetr_passes.py ngraph/transformers/gpu/kernels/cuda/pooling.py ngraph/frontends/caffe2/tests/test_ops_unary.py ngraph/frontends/caffe2/tests/__init__.py tests/flex_tests/test_flexew/test_complex_math.py ngraph/frontends/onnx/tests/test_utils.py ngraph/transformers/hetr/hetr_utils.py examples/convnet-benchmarks/alexnet.py ngraph/frontends/onnx/onnx_importer/utils/binary.py ngraph/frontends/neon/tests/test_costs.py pytest_addoption pass_method force_serialization_computations pytest_xdist_node_collection_finished transformer_factory pytest_configure pytest_generate_tests get_version build_ext get_html_theme_path train_loop eval_loop generate_text expand_onehot Benchmark DefaultOrderedDict Mark generate_ds2_data generate_data make_fake_dataloader preprocess_ds2_data FakeDataIterator conv_params make_iterators make_embedding_layer make_layers CrepeDataset cifar_mean_subtract cifar_mean_subtract loop_eval cifar_mean_subtract conv_params f_module residual_network ingest_cifar10 make_aeon_loaders Inception SpeechTranscriptionLoader make_aeon_dataloader ArgMaxDecoder Decoder DeepBiRNN decode_outputs Deepspeech Agent Namespace ModelWrapper Memory linear_generator decay_generator make_axes space_shape main model main baselines_model model ReshapeWrapper TerminateOnEndOfLifeWrapper DimShuffleWrapper RepeatWrapper LazyStack ClipRewardWrapper rl_loop_train evaluate_single_episode RandomInputConstantGoalEnv ConstantEnv DependentEnv test_make_axes_noop test_make_axes_array test_model_predict_different test_decay_generator_minimum test_epsilon_linear test_model_train test_epsilon_linear_after_end test_model_predict_wrong_shape test_model_predict_single_shape test_model_predict_single_shape_after_predict test_decay_generator_simple small_model test_model_predict test_repeat_wrapper_reset test_repeat_wrapper_step MockEnvironment test_dependent_environment model make_aeon_loaders make_optimizer make_optimizer make_generator make_discriminator make_discriminator_gp make_generator make_discriminator make_generator_gp get_image Noise save_plots train_schedule make_optimizer ToyGAN affine_layer NormalNoise UniformNoise generate_plot DataGenerator make_optimizer make_generator make_discriminator make_aeon_loaders Inceptionv3_b2 Inceptionv3_b3 Inception conv_params Inceptionv3_b1 Inceptionv3_b5 Inceptionv3_b4 scale_set eval_loop common_config make_aeon_loaders expand_onehot generate_samples expand_onehot expand_onehot TSPSequentialArrayIterator travel_distance distance save_plot first_example ingest_cifar100 ingest_cifar10 make_aeon_loaders IngestI1K transform_and_save process_files_in_tar process_i1k_tar_subpath conv_params BuildResnet num_i1k_resmods cifar10_mean_subtract i1k_mean_subtract loop_eval fill_feed_dict get_network_params set_lr TimeSeries eval_loop generate_sequence plot_inference plot_generated common_config make_validation_loader make_train_loader moving_average plot_logs get_data create_network train_network run_validation MixtureGenerator one_hot CensusDataset categorical_numbers prodm cross_columns sparse_column_with_hash_bucket WideDeepClassifier compute_accuracy make_placeholders test_notebooks test_graph_def_conversion plot_tensorboard_image plot_tensorboard_scalar plot_tensorboard_audio plot_tensorboard_histogram plot_tensorboard_graph get_simple_graph CommNodePair CPUCommNodeFactory GPUCommNodeFactory CommNodeFactory get_comm_pattern Flex FlexManager FlexEntry FlexNgraphArgparser CaffeCLI parse_prototxt registered OpsBinary register_func_with_ops_bridge OpsBridge registered OpsConstant test_scalar_const_sum test_tensor_const_sum test_scalar_dummy_data test_vector_dummy_data C2Importer OpsBinary OpsBridge OpsConstant output_dim _c2_padding OpsNN OpsUnary make_const_op args_shape_to_axes shape_to_axes fc_example linear_regression mnist_mlp sum_example test_fc_example test_sum_example test_mnist_mlp test_linear_regression test_sum test_xavierfill test_constant test_giventensorintfill test_giventensorfill test_uniformintfill test_uniformfill test_gaussianfill test_convolution_nhwc test_fc test_convolution_nchw_no_pad_no_bias test_convolution_nchw test_maxpool test_SquaredL2Distance test_avgpool test_LabelCrossEntropy test_convolution_nhwc_no_pad_no_bias test_AveragedLoss test_NCHW2NHWC test_NHWC2NCHW test_exp run_all_close_compare_initiated_with_random_gauss test_tanh test_softmax test_relu test_negative test_sigmoid test_args_shape_to_axes test_shape_to_axes expected_shape_to_axes test_args_shape_to_axes_wrong_pos create_loss_and_learner CNTKImporter cross_entropy_with_softmax classification_error OpsBinary OpsBridge OpsCompound OpsUnary saveMean readBatch loadData saveTrainImages saveTxt saveTestImages saveImage load_and_score create_resnet_model resnet_basic_stack train_and_evaluate create_basic_model resnet_basic_inc create_terse_model convolution_bn resnet_basic create_reader create_dropout_model create_vgg9_model linear_layer fully_connected_classifier_net dense_layer generate_random_sample linear_layer generate_random_sample load_and_score download_and_save loadLabels create_model loadData try_download savetxt train_and_test create_reader test_minus_3 test_times_4 test_times_5 test_times_6 test_times_2 test_plus_1 test_plus_2 test_element_times_3 test_times_3 test_minus_1 test_plus_3 test_minus_2 test_times_1 test_element_times_2 test_element_times_1 test_maxpool_1 _create_maxpool_model _create_avgpool_layer _create_dense_model _create_convolution_model test_dense_1 _generate_random_sample test_convolution_1 test_dense_3 test_dense_4 _create_model_and_execute_test test_avgpool_2 _create_maxpool_layer test_avgpool_1 _create_avgpool_model test_dense_2 test_convolution_3 test_convolution_4 test_convolution_2 test_maxpool_2 assert_cntk_ngraph_flat_isclose test_log test_negate test_floor test_reduce_sum test_reduce_min assert_cntk_ngraph_isclose assert_cntk_ngraph_flat_equal test_reduce_mean test_softmax test_reduce_max test_exp test_reduce_log_sum_exp test_tanh assert_cntk_ngraph_array_equal test_reduce_prod test_abs test_relu test_reciprocal test_sqrt test_sigmoid lr_policy lr_policy_exp lr_policy_inv lr_policy_poly lr_policy_fixed lr_policy_sigmoid lr_policy_step lr_policy_provided lr_policy_schedule LegacyOpsBinary common_conv2d_pool_output_shape deconv_output_dim conv_output_dim make_convparams common_conv2d_pool_padding squeeze_axes make_poolparams CommonSGDOptimizer ConvParameters max_iter test_sigmoid_lr test_fixed_lr iter_buf test_inv_lr test_exp_lr test_poly_lr test_provided_lr base_lr test_step_lr test_dummy Normalizer Rectlinclip Logistic Softmax Tanh Explin Identity Rectlin AeonDataLoader NgraphArgparser ArrayIterator SequentialArrayIterator assert_no_shadow_axes Namespace reorder_spatial_axes make_shadow_axis shadow_axes_map is_shadow_axis loop_train TrainLoggerCallback loop_eval TrainCostCallback CallbackPhase RunTimerCallback ProgressCallback LossCallback make_default_callbacks CallbackContainer Callback _cache_ops_length SubGraph ScopedDict ComputationalGraph scope_ops GlorotInit KaimingInit UniformInit _input_output_axes GaussianInit ConstantInit XavierInit PoolBase _cells_state_info Recurrent BaseRNNCell infer_axes Activation Pooling DeconvBase Bias ConvBase Dropout BatchNorm LookupTable cast_tuple get_steps Preprocess Convolution Deconvolution BiRNN _cells_initialize_states Affine make_conv Linear unroll RNNCell LSTM Layer PBStreamHandler ProgressBar make_bound_computation Sequential ResidualModule Container Parallel BoundComputation LearningRateOptimizer RMSProp Optimizer Adagrad Adam get_learning_rate_policy_callback clip_gradient_value clip_weight_value GradientDescentMomentum clip_gradient_norm Saver get_root_ops SaverFile get_function_or_class_name make_convolution_placeholder CIFAR10 CIFAR100 pad_sentences IMDB preprocess_text get_files Librispeech ingest_librispeech LSUN MNIST ingest_mnist PTB Shakespeare TSP height recurrent_axis input_placeholder batch_axis extra_feature_axes spatial_axes input_axis output_axis recurrent_input conv_input_placeholder width channel_axis LSTM checkBatchGradient checkSequentialMatchesBatch RefBidirectional RefSeq2Seq RefRecurrent ActivationPair IdentityPair test_activation SoftmaxPair all_inputs LogisticPair RectlinClipPair TanhPair test_derivative activation_pair LeakyRectlinPair RectlinPair test_shuffle batch_size input_seq test_rolling_window test_nowindow seq_len axis_a test_reorder_spatial_single_spatial test_reorder_spatial_triple_spatial test_axis_is_not_shadow test_reorder_spatial_double_spatial test_reorder_spatial_no_batch test_shadow_axis_is_shadow_axis test_reorder_spatial_no_channel test_reorder_spatial_toomany_spatial CDHWN test_reorder_spatial_no_spatial bn_params test_batchnorm_bprop batch_size output_size test_recurrent_batchnorm_bprop test_recurrent_batchnorm_fprop test_batchnorm_fprop input_size sequence_length BatchNormReference test_conv_batchnorm_fprop RNNHelper conv1d_placeholder test_deconv test_conv_inverts_deconv output_size batch_size test_channel_axis_introduction test_alternate_channel_axes test_conv1d input_size test_causal_convolution test_axis_preservation test_dilated_conv reference_conv1d spatial_onehot test_same_convolution test_alternate_spatial_axes width_axis conv1d_no_channel_axis test_costs CrossEntropyBinaryPair SumSquaredPair CostPair MeanSquaredPair CrossEntropyMultiPair safelog test_dropout_train test_dropout_bprop_single_comp test_dropout_inference SimpleLayer test_layer_outputs batch_size test_layer_side_effects test_layer_variables test_computational_graph_capture test_subgraph_scopes_attribute input_size test_scopes_generation test_scope_ops test_layer_metadata NestedLayer test_dual_scope_layer test_nested_layer_scopes test_layer_inputs test_layer_mode_setting test_mode_setting test_linear_axes_nout test_linear_zeros test_linear_ones test_linear_keep_axes_ones output_size input_placeholder batch_axis test_linear_W_axes_nout test_linear_axes test_linear_keep_batch_axes_ones input_size test_linear_invalid_recurrent_axes test_linear_invalid_batch_axes test_linear_keep_axes feature_axis test_linear_keep_batch_axis test_linear_accepts_axes_axis test_linear_invalid_shadow_axes copier_T copier test_ref_stacked pytest_generate_tests check_stacked_lstm check_lstm test_ref_compare_rand test_convolution input_size random_beta_2 GDMReference random_learning_rate RMSPropReference random_beta_1 test_learning_policy_fixed_with_input random_momentum_coef AdamReference data_generator test_adagrad test_adam AdagradReference test_learning_policy_step test_learning_policy_schedule compare_optimizer compare_optimizer_variable_select test_gdm test_rmsprop test_learning_policy_fixed_without_input test_weight_clipping test_inference_reuse_recurrent test_inference_reuse_conv output_size batch_size test_inference_reuse_lstm input_size test_inference_reuse_batch_norm sequence_length test_inference_reuse_lut test_inference_reuse_linear test_rnn_deriv_numerical make_placeholder make_weights test_rnn_deriv_ref test_birnn_output_types weight_initializer bias_initializer test_birnn_fprop test_birnn_deriv_numerical test_seq2seq_deriv_ref test_stacked_birnn_construction test_change_recurrent_axis_length test_rnn_fprop test_rnn_deriv_numerical make_placeholder make_weights test_rnn_deriv_ref weight_initializer bias_initializer test_rnn_fprop test_variable test_persistent_tensor NgraphBackend NgraphBackendRep import_onnx_file import_onnx_model WrapperBaseClass TensorWrapper ModelWrapper AttributeWrapper NodeWrapper GraphWrapper ValueInfoWrapper Neg make_ng_nodes ReduceSum Greater Reciprocal Softmax Selu ReduceMin MaxPool Min Elu Sigmoid Sub Gemm AveragePool MatMul Constant Sqrt Ceil Slice Abs ReduceLogSumExp Flatten ReduceProd Add Reshape Split Xor Conv ConvTranspose Sum Exp ArgMin Dot Pad GlobalAveragePool Relu Concat Tanh And Squeeze GlobalMaxPool ReduceMax Mean PRelu Floor Less Transpose Div Equal LeakyRelu Log ArgMax Max Softplus ReduceMean Not Mul Or BatchNormalization rename_axes reorder_axes reshape_workaround cast_axes_for_binary_broadcast cast_axes_for_matmul get_conv_params get_pads make_conv_output_axes get_dilations make_convolution_op get_strides split_pads_into_pairs verify_symmetric_padding get_pool_params get_kernel_shape make_pooling_op make_global_pooling_op make_pool_output_axes get_reduction_axes make_reduction_op test_simple_graph test_value_info_wrapper onnx_model test_graph_wrapper test_node_wrapper test_model_wrapper test_attribute_wrapper test_batch_norm_test_mode make_batch_norm_node test_sub test_add test_mul test_div import_and_compute test_pool_global_average ndarray_1x1x4x4 test_2d_conv_transpose import_and_compute_conv test_pool_average_3d get_transformer test_3d_conv test_padding test_pool_average test_pool_max test_2d_conv make_onnx_model_for_conv_op test_pool_global_max test_pool_global_average_3d test_logical test_logical_not test_op_matmul make_onnx_model_for_gemm_op make_onnx_model_for_matmul_op import_and_compute_matmul get_transformer numpy_gemm import_and_compute_gemm test_gemm test_op_matmul_3d test_leaky_relu assert_onnx_import_equals_callable test_elu test_parametric_relu test_tanh import_and_compute test_relu test_selu test_sigmoid test_reduce_max_keepdims test_reduce_max test_reduce_argmax test_reduce_sum test_reduce_argmin test_reduce_min test_reduce_prod test_reduce_log_sum_exp test_reduce_mean import_and_compute test_ceil test_log test_neg test_reciprocal test_exp test_floor test_sqrt test_abs test_variadic test_mean test_slice test_flatten_exception test_squeeze test_transpose test_concat test_flatten test_split test_reshape test_get_pads get_transformer convert_and_calculate all_arrays_equal logistic_regression mnist_mlp ng_retrain_mnist train_mnist tf_retrain_mnist mnist_mlp_ns mnist_mlp_tf start_tensorboard DebugTester def_target_feed_dict remove_event_dump clean_up ImporterTester Tester TestExamples Tester Tester Tester gen_conv_testcase gen_pool_testcase Tester Tester Tester Tester Tester test_np_layout_shuffle test_broadcast_to test_is_compatible_numpy_shape test_is_compatible_broadcast_shape test_broadcasted_shape FakeDataset FakeMNIST TFImporter mod reduce_max sign reduce_prod abs log exp ones multiply less_equal placeholder add reduce_sum matmul less greater_equal relu subtract square sparse_softmax_cross_entropy_with_logits _element_wise_binary softmax negative equal minimum tanh constant _reduction not_equal Variable divide greater maximum sigmoid pow reduce_mean zeros reduce_min _get_reduction_indices OpsBridge tf_obj_shape remove_tf_name_prefix get_nested_attr to_int np_layout_shuffle broadcast_to is_compatible_numpy_shape is_compatible_broadcast_shape broadcasted_shape make_pos_axis reorder_pos_axes cast_to_pos_axes make_pos_axes reduce_strides IncompatibleAxesError _make_strides with_args_as_axes _sliced_length UnmatchedAxesError slice_axis make_axes Axes default_int_dtype make_axis Axis _make_stride DuplicateAxisNames _validate_slice duplicates _reduce_nested FlattenedAxis TensorDescription default_dtype AxesMap ScatterSendOp GatherRecvOp AllReduceOp ResultOp BroadcastRecvOp CPUMlslBroadcastSendOp CPUMlslScatterRecvOp GPUCudaScatterSendOp GatherWrapperOp CPUMlslBroadcastRecvOp set_parallel_axes CPUMlslAllReduceWaitOp CPUMlslGatherRecvOp ScatterRecvOp CPUMlslScatterSendOp GatherSendOp RecvOp GPUCudaScatterRecvOp calculate_gather_axes CPUMlslAllReduceStartOp BroadcastSendOp CommunicationOp get_slices SendOp GPUCudaSendOp CPUMlslSendOp GPUCudaGatherSendOp GPUCudaGatherRecvOp CPUMlslGatherSendOp GPUCudaRecvOp CPUMlslRecvOp GPUCudaAllReduceOp ControlBlock ConvolutionOp deconvolution DeconvDerivOp ConvDerivOp DeconvolutionOp update_conv bprop_conv convolution ctc CTCOp PrintOp lookuptable update_lut LookupTableOp bprop_lut lookuptable_update LutDerivOp Greater OneHotOp SquareOp cos ReadOp CommutativeBinaryElementWiseOp CrossEntropyMultiOp MapRolesOp ComputationOp less_equal argmin SequentialOp GreaterEqual Fill IndexOp axes_with_order Maximum prod_adjoints mean Minimum StackOp softmax fill Op cross_entropy_binary not_equal divide CosOp SignOp Subtract as_op tensor_slice cross_entropy_multi RoleCastOp AbsoluteOp max_adjoints doall sigmoidAtomic deriv sign ReorderAxes ElementWiseOp DotLowDimension Power cast_axes safelog DotOp min_adjoints Add multiply add AxesCastOp metadata _unslice Multiply map_roles ControlBlockOp greater_equal squared_L2 tensor_descriptions BroadcastOp flatten_at temporary sqrt DerivOp as_ops minimum CrossEntropyBinaryInnerOp compute_reduction_axes Less greater sigmoid L2_norm create_reduction_op slice_along_axis NotEqual floordivide persistent_tensor ExpandDims flatten assign cross_entropy_binary_inner stop_gradient argmax Mod Flatten broadcast reciprocal TensorSizeOp exp InputOp TanhOp OneHotTwoDimOp placeholder sum_adjoints AssignOneDOp RngOp uniform LessEqual BinaryElementWiseOp L1_norm ParallelOp sin normal AssignableTensorOp subtract DebugInfo sequential stack ReciprocalOp negative equal PatternSkipOp computation SigmoidAtomicOp TensorOp tanh constant Transpose LogOp variance PatternLabelOp absolute maximum AssignOp TensorValueOp dot tensor_size MutateInsteadOfCopyWithNewArgsMixin concat_along_axis set_item mod unflatten batch_size variable TensorSliceOp log SoftmaxOp Divide LiteralScalarOp WriteOp NegativeOp tdcache pad less expand_dims StopGradient Unflatten one_hot SqrtOp square ContiguousOp power FloorDivide value_of ExpOp UnaryElementWiseOp ReturnOp Equal cast_role SigmoidOp ConcatOp with_op_metadata ValueOp UnsliceOp SinOp ReductionOp pooling BpropPoolOp PoolingOp assign_scalar assign_op_attr _deserialize_graph_ops_edges op_to_protobuf tensor_to_protobuf data_to_tensor pb_to_axes_map axis_to_protobuf _deserialize_graph deserialize_graph axes_map_to_protobuf get_ngraph_op_cls dtype_to_protobuf protobuf_to_axes pb_to_dict serialize_graph protobuf_scalar_to_python unhandled_scalar_value _serialize_graph pb_to_tensor axes_to_protobuf protobuf_attr_to_python pb_to_axis add_edges protobuf_to_op is_scalar_type pb_to_dtype SerializationPass set_op_value write_raw_np json_dumps_manifest extract_op deserialize_weights extract_ops read_np_values serialize_weights write_np_values set_op_values json_loads_manifest ngraph_to_tf_graph_def RecordWriter create_event crc_finalize u32 crc32c serialize_protobuf crc_update event_to_record masked_crc32c make_histogram make_image audio _clean_tag make_histogram_buckets image histogram make_audio scalar TensorBoard TensorBoardPass ConvParams slicable reference_deconv_fprop reference_conv pixel_indices reference_deconv_bprop with_error_settings raise_all_numpy_errors transformer_name __overwrite_rtol_atol assert_allclose is_flex_transformer is_flex reset_flex_entries ExecutorFactory is_flex_factory executor check_derivative numeric_derivative template_one_placeholder execute_calculation template_dot_two_placeholders id_func get_placeholder_from_operand unpack_list template_create_placeholder execute_convolution template_dot_one_placeholder template_dot_one_placeholder_and_scalar template_create_placeholders_for_multiplication template_create_placeholder_and_variable get_executor_result template_two_placeholders create_send_recv_graph create_scatter_gather_graph RandomTensorGenerator Computation Transformer make_transformer DeviceBufferStorage DeviceTensor ComputationGraphTransformer transformer_choices Transformer_ABC_Meta allocate_transformer UnsupportedTransformerException make_transformer_factory set_transformer_factory CPUTransformer CPUDeviceTensorView get_tensors CPUCodeGenerator CPUPoolEngine CPUConvEngine CPUDeviceComputation CPUDeviceTensor align_ndarray InputDecl ComputationDecl ExOpBlock TensorViewDecl ExecutionState ExOp etype ExecutionGraphElt literal_scalar_exop TensorDecl OutputDecl ElementType ExecutionGraph DeviceBuffer DeviceTensor ExecutionGraphTransformer DeviceComputation DeviceTensorView FlexGPUKernelGroup _ew_bind_flex_scales FlexGPUTransformer FlexGPUDeviceBufferStorage FlexGPUDeviceTensor Function GPUDeviceTensor GPUKernelGroup GPUTensorAllocator GPUBufferAllocator ElementWiseKernel GPUTransformer GPURegister GPURuntime GPUDeviceBufferStorage build_transformer HetrComputation HetrTransformer BatchnormOp BpropBatchnormOp Mkldnn fprop_lut ConvLocals update_lut ctc_cpu HetrLocals BpropReluOp ReluOp ConvBpropKernel ConvFpropKernel ConvUpdateKernel CTCKernel _get_transpose_kernel _get_convert_kernel _flatten _prepare_convert_kernel ScratchBufferWrapper FlexConvBpropKernel _grid_dim FlexConvFpropKernel _get_shuffle_kernel FlexConvUpdateKernel FlexLUTBpropKernel convert_to_flex FlexPoolFpropKernel FlexPoolBpropKernel _build_register_mapping CudaSourceFile FlexPtrDescription _call_compound_kernel _is_buffer GenerationContext FlexScaleDescription _generate_stage_code _preprocess_ops _are_flex_params _ops_to_hash _generate_kernel_args _generate_new_kernel_args _generate_kernel_code _prepare_compound_kernel _get_compound_kernel TensorDescriptionWrapper _get_axes_mapping _optimize_loop_axis _get_register_type _wrap_tensor_descriptions NvrtcSourceModule GEMMKernel DimshuffleOp GPUCommsLayoutConstraint GPUDotLayoutAssignment GPUFixedLayoutConstraint GPULayoutAssignment GPUIndexOp GPUDotLayoutConstraint GPULutLayoutConstraint GPULayoutView GPUBinaryLayoutConstraint GPUEWLayoutConstraint GPUAssignLayoutConstraint gpu_constraint_factory gpu_layout_factory GPUUnaryLayoutConstraint GPUKernel LUTBpropKernel PoolBpropKernel PoolFpropKernel DimShuffleKernel CudaSendKernel bcast_ipc_handle _reduction_kernel CudaRecvKernel CudaScatterRecvKernel CudaGatherRecvKernel get_dimshuffle RngFillKernel calculate_segment_size FillKernel CudaScatterSendKernel CudaAllReduceKernel FlexAssignKernel FlexFillKernel FlexRngFillKernel CudaGatherSendKernel _closest_divisor _flatten _get_events _get_scratch_data _magic64 _reset_scratch_data get_cache_dir _get_sm_count _ceil_div _magic32 _closest_divisor _get_update_conv_reduce_kernel UpdateConvReduce ConvertDataType _magic64 _get_shuffle_kernel _get_transpose_kernel _flatten BpropDirect FpropCuda _get_compound_ops_kernel BatchNormSum KernelGroup _get_batchnorm_sum_kernel _get_convert_dtype_kernel FilterDimShuffle NoopTransform UpdateCuda _ceil_div UpdateDirect XpropDirect BpropCuda CompoundOps FpropDirect _get_sm_count _magic32 K_partitions get_ptx_file update_grid xprop_conv_kernels run_command extract_includes get_kernel FpropFilter_2x2_5x5 BpropWinograd_4x4_3x3 _get_xprop_image_4x4_3x3_kernel FpropWinograd_2x2_3x3 XpropWinograd_2x2_3x3 BpropFilter_2x2_5x5 BpropFilterTransform UpdateWinograd_3x3_2x2 FpropFilterTransform FpropFilter_4x4_3x3 _get_filter_kernel FpropFilter_2x2_3x3 _get_update_image_3x3_2x2_kernel BpropFilter_4x4_3x3 FpropWinograd_2x2_5x5 BpropFilter_2x2_3x3 _get_update_image_3x3_4x4_kernel UpdateWinograd_3x3_4x4 UpdateImage_3x3_2x2 FilterTransform BpropWinograd_2x2_5x5 BpropWinograd_2x2_3x3 XpropWinograd_2x2_5x5 FpropWinograd_4x4_3x3 _get_update_delta_3x3_4x4_kernel XpropWinograd_4x4_3x3 _get_copy_transpose_kernel _get_oned_copy_kernel _get_sorting_kernel _configure_template_vals_sort _configure_template_vals_bprop _get_lut_bprop_kernel _get_bprop_max_overlap_smallN _get_fprop_lrn map_string2func _get_bprop_avg_overlap _get_bprop_lrn_overlap _get_bprop_max _get_fprop_max _get_bprop_avg _get_bprop_avg_overlap_smallN _get_bprop_max_overlap _get_fprop_avg prepare_template_vals BetaHetrServicer HetrServicer add_HetrServicer_to_server HetrStub beta_create_Hetr_stub BetaHetrStub beta_create_Hetr_server add_HetrServicer_to_server HetrStub HetrServicer HetrServer write_server_info is_port_open serve update_comm_deps get_rng_seeds find_recvs comm_path_exists get_iterable update_parallel_axis MPILauncher RPCTransformerClient is_channel_ready RPCComputationClient CPUFusion CPUTensorLayout is_contiguous DumpGraphPass ExVizPass ExOpGraphOpAccessor CopyElimination SequentialExOpPass SSAConversion IndexElision exop_method DeadCodeEliminationPass FlexFusion FlexDtypePass FlexPropagateEntryPass ClearTensorDescriptions FlexStackOpPass CPUAssignOp GPUSubstitution AxesUpdatePass DeviceAssignPass CommunicationPass UnaryLayoutConstraint AddLayoutConversions GenerateLayoutDomains LayoutAssignment AssignLayouts GenerateLayoutConstraints PruneContiguousPass BinaryLayoutConstraint LivenessPass MemoryManager MemLayoutPass MemoryNode is_child move_op MemOptimizePass is_parent get_mkl_order_from_axes_names get_order_from_axes get_rotated_layout MklCreateOpDescriptors MklAddLayoutConversions get_strides_mkl_order get_flattened_axes get_ctypes_arg get_arg_output_idx MklReorderOp get_native_layout dbg_print_kernel get_axes_mkl_order get_size_mkl_order JSONPass VizPass OpDelegate OpAccessor DelegateOpAccessor OpGraphOpAccessor RequiredTensorShaping HeTrTensorShaping ProcessOpGraphPass GraphRewritePass PeepholeGraphPass CPUTensorShaping SimplePrune GraphPass GraphBuildingPass VerifyPass VisualizeMemPass get_split_groups StridedBinaryLayoutConstraint enumerate_axis_orders flatten split_points_to_groups enumerate_remaining_axes StridedLayoutAssignment generic_function generic_method TypeMethods NameableValue _get_thread_name_scope get_full_scope_name name_scope NameScope get_current_name_scope ScopedNameableValue pickle_load valid_path_append fetch_file get_data_cache_or_nothing ensure_dirs_exist indenting PyModule PyGen get_thread_state TraceEventTracker is_tracing_enabled test_sliced_recurrent_axis test_axes_map_immutable test_sliced_axis_flip random test_axes_ops test_duplicate_axis test_reaxe_0d_to_1d tensorview test_simple_tensors test_invalid_axes_map_message test_sliced_batch_axis test_axes_map_init_from_axes test_sliced_axis_none_end test_sliced_axis_invalid_step test_reaxe_0d_to_2d test_axes_map test_duplicate_axis_different_length test_sliced_axis_invalid test_sliced_axis_negative test_duplicate_axis_names test_sliced_axis_negative_invalid test_sliced_axis test_print_op_fprop test_print_op_bprop test_generic_method A B test_generic_function SubVisitor C selector Visitor derived_selector StubTransformer test_simpleprune_graph_pass get_simple_graph test_memory_manager_free_last_allocated test_liveness test_memory_manager_max_allocated test_memory_manager_memory_align test_memory_manager_align test_memory_manager_bad_free test_memory_manager_free_first_allocated test_memory_manager_free_middle_allocated test_memory_manager_free_first_free test_memory_manager_allocate test_memory_manager_free_middle_free test_nested_namescope test_scope_reuse test_tensor_slice test_slice_nop test_deriv_missing_connection test_pad_invalid_paddings_length test_pad_0 test_one N test_pad_mixed test_metadata_capture test_metadata Dummy HelloCodeGenerator test_hello test_full_graph_serialization_endtoend test_op_handle_selection test_flattenedaxis_serialization test_all_op_references test_scalar_to_protobuf test_axis_serialization test_hetr_send_recv_graph_serialization test_op_references test_op_to_protobuf assert_object_equality strip_dict test_ser_pass test_tensor_to_protobuf get_simple_graph test_execute_convolution test_convolution_limitation test_if_mlp_network_is_trainable test_gemm_multiply_matrices test_gemm_multiply_matrix_by_vector test_gemm_multiply_matrix_by_scalar test_single_operand test_input_types test_single_operand test_double_operand test_assign test_double_operand test_single_operand test_double_operand test_calculate_new_axes_single_device test_get_node_type test_broadcast_ops test_calculate_new_axes_null_parallel_axis test_allreduce_hint test_calculate_new_axes_null_axes test_multiple_gather_ops test_distributed_dot test_distributed_plus_one ClosingHetrServers test_gpu_send_and_recv test_comm_broadcast_op test_mpilauncher test_distributed_dot_parallel_second_axis test_recvop_tensorupdate test_hetr_benchmark test_distributed_plus_two test_broadcast_scalar test_multi_computations test_repeat_computation test_to_and_from_device test_recvop_axes_using_dot test_reduce_vector test_rpc_transformer test_reduce_scalar test_computation_return_list test_compare_hetr_cpu train_mnist_mlp test_scatter_gather_graph test_singleton_device_id check_device_assign_pass check_communication_pass test_hetr_graph_passes test_scatter_gather_node_axes test_find_recvs_scatter_gather test_update_comm_deps test_find_recvs test_comm_path_exists_scatter_gather test_update_comm_deps_scatter_gather test_comm_path_exists assert_axes_eq_len test_idempotent_axes_a test_idempotent_axes_c test_idempotent_axes_b test_scalar_broadcast test_cputensor_dot test_cputensor_add_constant test_cputensor_fusion test_cputensor_mlp test_constant_add test_constant_multiply test_cputensor_add test_cputensor_multiply_constant test_constant_init test_deconv test_wrong_filters_shape_length test_wrong_input_shape_length deconv_n4_hw4_c1_5x5 test_convolution_backprop test_2layer_deconv test_conv_flatten_deriv test_conv n64_hw32_c32_3x3 n4_hw12_c3_5x5 test_first_axes_not_same n128_hw32_c3_2x2 ctc_ref pytest_generate_tests test_ctc TestDimShuffleFail A B C TestDimShuffleFpropBprop x test_dimshuffle_op ngraph_l2_norm get_random_shape test_tensor_dot_tensor test_dot_sum_backprop test_squared_L2 test_flat_tensor_dot_tensor get_random_np_array test_cross_entropy_binary_unmatched_axes np_softmax test_cross_entropy_softmax test_softmax2 test_mean test_cross_entropy_binary adiff_softmax test_constant_tensor_multiply test_argmax test_variance_wgrad cross_entropy_binary_logistic_shortcut test_return_type test_wrong_placeholders test_empty_finalize test_prod_deriv recurrent_input_tensor test_softmax sub_axes reduction test_scalar test_elementwise_ops_unmatched_args test_cross_entropy_softmax_rec_deriv test_empty_computation test_cross_entropy_multi_axis_order recurrent_axis test_elementwise_binary_ops_matched_args_deriv_lhs test_softmax_rec_deriv test_log_sigmoid_deriv test_clip test_softmax_deriv batch_axis test_elementwise_binary_ops_matched_args_deriv_rhs test_softmax_rec test_elementwise_fp16_out test_variance_sqrt_inverse test_elementwise_fp16_in test_reduction_deriv prod_constant test_reciprocal_derivative compare_f_at_x test_onehot prod_deriv_arrays test_prod_constant test_elementwise_unary_ops_matched_args one_hot_comparison elementwise_binary_op test_elementwise_binary_ops_matched_args test_cross_entropy_softmax_large_input test_reduction test_cross_entropy_multi_unmatched_axes test_constant_multiply test_np_softmax test_cross_entropy_rec input_tensor test_cross_entropy_softmax_deriv test_broadcast_deriv_reorder test_sigmoid_deriv input_axes test_multiply_unit32_convertion test_cross_entropy_binary_logistic_shortcut test_placeholder test_reciprocal np_cross_entropy_multi test_sigmoid_value test_fill_slice symmetric_tensor test_tensor_derivative feature_axis test_tensor_constant elementwise_unary_op test_tensor_sum_single_reduction_axes cross_entropy_binary_logistic test_flatten_deriv_simplified test_flatten_deriv input_axes test_exit_condition test_kernel_cache test_4d_reduction test_4d_chained test_4d_elementwise test_logreg NumpyLogreg lut_fprop_ref test_lut pytest_generate_tests lut_update_ref concatenate_variables test_initial_value test_sequential_reduce test_concat_different_axis_lengths test_sequential test_sequential_side C test_variable_init test_concatenate N test_sign test_multiple_computations M test_wrong_op_name PoolParams test_wrong_input_shape_length test_gen_reference test_expand_dims test_tensor_description_init test_slice test_cast_axes test_padding test_slice_tensor_description test_multiple_slices test_shuffled_deriv test_reverse_slice test_uniform_range_posneg test_uniform_range_pos test_rng_repetition test_normal_negative_mean input_tensor test_extract_op test_serialize_and_deserialize_multi_np test_set_op_values assign_ops test_serialize_and_deserialize_single_raw_np test_round_trip test_extract_many_ops make_axes test_set_op_value test_json_dumps_manifest test_modify_state test_write_state test_read_state test_specific_slice_deriv test_fill_state test_concatenate test_slice_deriv test_use_state test_stack batch_size extra_axes test_sum sequence_length num_units test_tensor_size test_evaluation_twice test_missing_arguments_to_execute test_execute_non_placeholder CTC get_ctc_lib find ctc_header addoption sort getoption parametrize getoption make_transformer_factory set_transformer_factory Transformer setattr getoption add_computation xfail skipif get IGNORECASE compile dirname abspath eval_function reset enumerate join __iter__ print train_function eval_loop reset generate_text append next enumerate sum generation_function multinomial append argmax range Mark seed RandomState reshape astype float32 randint ravel float32 array int32 astype int make_fake_dataloader placeholder make_placeholders FakeDataIterator ArrayIterator ConstantInit concatenate eye append zeros LookupTable Pooling Convolution make_embedding_layer Affine GaussianInit append range Dropout persistent_tensor list extend reset zip computation fromarray join uint8 format list reshape copy tqdm pad savetxt load_data save zip append enumerate makedirs ingest_cifar10 AeonDataLoader common_config get_data_cache_or_nothing dict decode convert_to_string cer process_string float len Axes isinstance Discrete isinstance ReshapeWrapper make Agent rl_loop_train TerminateOnEndOfLifeWrapper DimShuffleWrapper wrap_dqn action_space RepeatWrapper make_axes ClipRewardWrapper print evaluate_single_episode list format act progress_bar ProgressBar end_of_episode range mean render reset set_description evaluate_single_episode iter info deque step observe_results append act end_of_episode render reset step make_axes zeros predict ModelWrapper ModelWrapper zeros ModelWrapper predict_single zeros predict ModelWrapper predict_single ones copy ModelWrapper zeros predict ones copy ModelWrapper zeros train range predict linear_generator assert_allclose linear_generator assert_allclose decay_generator decay_generator RepeatWrapper list MockEnvironment range list RepeatWrapper reset MockEnvironment range make Agent rl_loop_train action_space observation_space append space_shape range evaluate_single_episode list lsun_categories dict ingest_lsun zip range len RMSProp Adam append Deconvolution Convolution range append Deconvolution append Convolution range append Convolution Affine loss_type batch_size axis plot_dir log2 clf list transpose ylabel gray im_size imshow title savefig plot astype close float keys int T xlabel reshape cla expand_dims transpose float astype GradientDescentMomentum set_aspect str T format loss_type list plot xlabel float squeeze axis ylabel ylim title savefig clf xlim keys print exit BatchNorm list scale_set extend zip computation get_data_cache_or_nothing ingest_mnist DataLoader decode list squeeze lengths encode zeros argmax range append enumerate len zip len format use plot batch_size print xlabel ylabel hs title lr savefig train_file fromarray join uint8 format list copy tqdm savetxt load_data save zip append enumerate makedirs ingest_cifar100 str randint open join getmembers str join name transform_and_save append makedirs extract int extractfile join move name resize size save split float open persistent_tensor persistent_tensor list next all keys int T invert fill_feed_dict not_equal ndata warning any range equal dict str print append copy list subplots set_title plot concatenate reshape grid shape clf savefig swapaxes legend range len list subplots set_title plot grid clf savefig legend range eval_function concatenate reshape copy zeros range common_config float AeonDataLoader common_config float AeonDataLoader mean append moving_average subplots arange plot set_xlabel title set_ylabel twinx savefig legend append array len ConstantInit GaussianInit one_hot model sequential GradientDescentMomentum cross_entropy_multi make_placeholders items list extend dict reset enumerate computation make_validation_loader make_train_loader astype zip pop concat apply unique append range len astype unique get_dummies make_axis placeholder make_axes append range len list eval_fun values zip update exp named make_axes log ngraph_to_tf_graph_def get_simple_graph add_graph get_simple_graph arange pi sin enumerate add_scalar add_histogram normal uniform astype add_image normal arange add_audio pi sin normalize isinstance NetParameter OpsBridge format print name SolverParameter bottom append ops_bridge type net layer print get join parse_prototxt get join full parse_prototxt get join parse_prototxt get join full parse_prototxt common_conv2d_pool_padding mod array get_op_handle format RunNetOnce C2Importer print ConstantFill GivenTensorFill Net FetchBlob parse_net_def ResetWorkspace FC Proto AveragedLoss make_transformer randn ConstantFill UniformFill get_op_handle CreateNet name Iter parse_net_def FC C2Importer range format RunNetOnce Net AddGradientOperators FetchBlob zip AddExternalInput computation FeedBlob minimize print update_fun WeightedSum get_iter_buffer CommonSGDOptimizer LearningRate RunNet array SquaredL2Distance len LabelCrossEntropy AveragedLoss make_transformer Softmax ConstantFill lrate UniformFill max_iter get_op_handle CreateNet name data_dir placeholder Iter parse_net_def C2Importer FC range format RunNetOnce astype Relu Net AddGradientOperators FetchBlob gamma batch computation FeedBlob minimize print WeightedSum LearningRate next_batch RunNet read_data_sets update_fun get_op_handle format RunNetOnce print Sum GivenTensorFill Net FetchBlob parse_net_def ResetWorkspace sum C2Importer Proto linear_regression fc_example sum_example mnist_mlp get_op_handle RunNetOnce Sum Net GivenTensorFill parse_net_def ResetWorkspace C2Importer Proto get_op_handle RunNetOnce ConstantFill random Net parse_net_def ResetWorkspace C2Importer Proto get_op_handle RunNetOnce GaussianFill Net parse_net_def ResetWorkspace C2Importer Proto get_op_handle RunNetOnce Net parse_net_def UniformFill ResetWorkspace C2Importer Proto get_op_handle RunNetOnce Net UniformIntFill parse_net_def ResetWorkspace C2Importer Proto XavierFill get_op_handle RunNetOnce Net parse_net_def ResetWorkspace C2Importer Proto get_op_handle RunNetOnce random Net GivenTensorFill parse_net_def ResetWorkspace C2Importer Proto GivenTensorIntFill get_op_handle RunNetOnce random_integers Net parse_net_def ResetWorkspace C2Importer Proto get_op_handle RunNetOnce C2Importer ConstantFill Net GivenTensorFill parse_net_def ResetWorkspace FC Proto get_op_handle RunNetOnce Net GivenTensorFill parse_net_def ResetWorkspace C2Importer SquaredL2Distance Proto AveragedLoss get_op_handle RunNetOnce Net GivenTensorFill parse_net_def ResetWorkspace C2Importer Proto GivenTensorIntFill LabelCrossEntropy get_op_handle RunNetOnce GivenTensorFill uniform Net parse_net_def ResetWorkspace randint C2Importer Proto get_op_handle RunNetOnce MaxPool Net GivenTensorFill parse_net_def ResetWorkspace C2Importer Proto get_op_handle RunNetOnce Net GivenTensorFill parse_net_def ResetWorkspace AveragePool C2Importer Proto get_op_handle RunNetOnce Net GivenTensorFill Conv parse_net_def ResetWorkspace C2Importer Proto get_op_handle RunNetOnce GivenTensorFill Net Conv parse_net_def C2Importer Proto get_op_handle RunNetOnce Net GivenTensorFill Conv parse_net_def ResetWorkspace C2Importer Proto get_op_handle RunNetOnce Net GivenTensorFill Conv parse_net_def ResetWorkspace C2Importer Proto get_op_handle RunNetOnce Net GivenTensorFill parse_net_def ResetWorkspace C2Importer Proto run_all_close_compare_initiated_with_random_gauss run_all_close_compare_initiated_with_random_gauss run_all_close_compare_initiated_with_random_gauss run_all_close_compare_initiated_with_random_gauss run_all_close_compare_initiated_with_random_gauss ResetWorkspace run_all_close_compare_initiated_with_random_gauss get_op_handle RunNetOnce Net GivenTensorFill NCHW2NHWC parse_net_def ResetWorkspace C2Importer Proto get_op_handle RunNetOnce Net GivenTensorFill NCHW2NHWC parse_net_def ResetWorkspace C2Importer Proto expected_shape_to_axes shape_to_axes funct Transpose cast_axes softmax GradientDescentMomentum cross_entropy_multi cross_entropy_multi cast_axes softmax Transpose argmax not_equal hstack urlretrieve format readBatch print vstack empty range load reshape new write mean pad save range str join parse SubElement Element write ElementTree zeros saveMean makedirs makedirs open count_nonzero all load_model squeeze split append import_model asarray format eval_fun ascontiguousarray eval computation join print allclose moveaxis len convolution_bn convolution_bn range resnet_basic resnet_basic_inc convolution_bn resnet_basic_stack make_transformer classification_error Trainer learning_rate_schedule todense train_minibatch squeeze training_fun placeholder minibatch append input import_model range asarray format element_times model_func cross_entropy_with_softmax computation int momentum_schedule next_minibatch print create_loss_and_learner parameters momentum_sgd moveaxis randint zeros astype float32 parameter linear_layer range dense_layer int glob flatten urlretrieve loadData loadLabels print dirname makedirs join savetxt try_download StreamDef make_transformer classification_error Trainer save learning_rate_schedule train_minibatch squeeze training_fun sgd placeholder minibatch append create_reader input import_model variables range asarray create_model format cross_entropy_with_softmax computation join int next_minibatch minimize print parameters moveaxis import_model eval plus import_model eval plus import_model eval plus import_model eval minus import_model eval minus import_model eval minus import_model eval element_times import_model eval element_times import_model eval element_times import_model eval times import_model eval times import_model eval times import_model eval times import_model eval times import_model eval times _create_avgpool_layer _create_maxpool_layer isinstance astype float32 randint zeros make_transformer classification_error Trainer input_variable learning_rate_schedule _generate_random_sample glorot_uniform train_minibatch identity sgd minibatch import_model variables cross_entropy_with_softmax computation previous_minibatch_loss_average minimize float32 parameters moveaxis _create_model_and_execute_test _create_model_and_execute_test _create_model_and_execute_test _create_model_and_execute_test _create_model_and_execute_test _create_model_and_execute_test _create_model_and_execute_test _create_model_and_execute_test _create_model_and_execute_test _create_model_and_execute_test _create_model_and_execute_test _create_model_and_execute_test import_model eval import_model eval import_model eval import_model eval sigmoid exp assert_cntk_ngraph_isclose exp assert_cntk_ngraph_isclose tanh assert_cntk_ngraph_isclose assert_cntk_ngraph_array_equal relu reciprocal assert_cntk_ngraph_isclose negate assert_cntk_ngraph_array_equal assert_cntk_ngraph_array_equal log sqrt assert_cntk_ngraph_isclose floor assert_cntk_ngraph_array_equal abs assert_cntk_ngraph_array_equal assert_cntk_ngraph_isclose softmax reduce_max array assert_cntk_ngraph_flat_equal reduce_min array assert_cntk_ngraph_flat_equal array assert_cntk_ngraph_flat_equal reduce_sum reduce_mean array assert_cntk_ngraph_flat_equal reduce_prod array assert_cntk_ngraph_flat_equal array assert_cntk_ngraph_flat_isclose reduce_log_sum_exp ceil float common_conv2d_pool_output_shape int append axes tensor_slice slice dict zip dict zip arange full arange arange arange arange exp arange any list expand_with_name all batch_axis make_axes is_not_equal_set enumerate TrainLoggerCallback TrainCostCallback ProgressCallback LossCallback append CallbackContainer train_pre_ minibatch_post minibatch_pre_ callbacks train_post enumerate items SubGraph dict extend isinstance Iterable update list isinstance zip keys list isinstance length reversed range get_steps recurrent_axis batch_axis sequential stack reverse doall range feature_axes len Real all isinstance sqrt squared_L2 sorted ComputationOp isinstance tuple keys values make_axis set_shape placeholder __name__ hasattr callable max astype max enumerate len join list iglob extend walk get_files join write fromarray join str list tqdm savetxt save zip append range ensure_dirs_exist enumerate make_axis make_axes list zeros_like randn backward reshape print reversed init allclose zeros forward range fwd backward randn abs print size init float forward range len Identity Rectlin Rectlin Rectlinclip Tanh Logistic Softmax reference_value baseline_value assert_allclose reference_derivative xfail flex_skip_now baseline_derivative assert_allclose arange reshape SequentialArrayIterator len enumerate discard reshape SequentialArrayIterator set sample2char enumerate len SequentialArrayIterator enumerate placeholder axes placeholder placeholder axes placeholder axes placeholder axes placeholder placeholder BatchNorm layer BatchNorm layer BatchNorm axes layer placeholder reference_rnn reference_input RNNHelper rnn reference_rnn axes get_ancestor_op placeholder has_gates append reference_input RNNHelper rnn squeeze activation shape zeros expand_dims sum range zeros Convolution conv_layer length Convolution conv_layer length Convolution conv_layer Convolution conv_layer axes Convolution conv_layer Convolution conv_layer int model ones Sequential min OrderedDict make_axes zeros sum make_axis randn Convolution placeholder conv1d GaussianInit make_axes make_axis deconv reshape Deconvolution placeholder make_axes zeros deconv_output_dim sum conv_output_dim reference_value baseline_value assert_allclose make_axis placeholder uniform layer Dropout uniform make_axis placeholder Dropout make_axis deriv placeholder uniform sum layer Dropout layer SimpleLayer layer SimpleLayer layer SimpleLayer layer SimpleLayer name layer NestedLayer layer list SimpleLayer items ops variable make_axis SimpleLayer container2 SimpleLayer NestedLayer container1 ComputationalGraph variable make_axis SubGraph ComputationalGraph layer SimpleLayer NestedLayer random lengths Linear ones assert_allclose lengths Linear make_axis placeholder linear make_axis placeholder Linear Linear make_axis placeholder make_axis placeholder ones lengths Linear make_axis placeholder ones lengths Linear product GaussianInit check_lstm check_stacked_lstm GaussianInit make_axis make_axis Convolution make_axis XavierInit placeholder make_axis length variable rand placeholder sequential dot sum make_axis length variable rand placeholder sequential dot sum range compare_optimizer_variable_select GradientDescentMomentum GDMReference compare_optimizer RMSPropReference compare_optimizer_variable_select RMSProp compare_optimizer compare_optimizer_variable_select AdamReference Adam compare_optimizer compare_optimizer_variable_select Adagrad AdagradReference compare_optimizer LearningRateOptimizer placeholder LearningRateOptimizer placeholder LearningRateOptimizer LearningRateOptimizer inf placeholder append array make_axis isinstance length rand variable argon_skip_now placeholder sequential dot sum optimizer layer zip Linear BatchNorm layer zip layer zip ConvBase Recurrent layer zip LSTM layer zip LookupTable layer placeholder zip make_axis named uniform set_shape make_axes make_axis batch_axis placeholder weight_initializer bias_initializer uniform make_axes rnn_ng fprop_only Recurrent reshape make_placeholder transpose make_weights shape RefRecurrent set_weights rnn_ng constant randn Recurrent reshape make_placeholder make_weights shape RefRecurrent set_weights Recurrent rnn_ng make_placeholder make_weights rnn_ng reshape make_placeholder flex_skip_now make_weights transpose BiRNN shape fprop RefBidirectional set_weights rnn_ng W_input make_placeholder BiRNN make_weights W_recur b rnn1 BiRNN rnn1 BiRNN rnn2 constant rnn_dec_ng randn Recurrent reshape make_placeholder lossFun make_weights transpose shape rnn_enc_ng RefSeq2Seq set_weights normal make_axis placeholder rec3 make_axes recurrent_layer_cls rec2 rec1 placeholder length unroll RNNCell length unroll RNNCell length unroll RNNCell remove persistent_tensor dict Saver make_axes computation remove variable rand sequential AssignOp dict Saver make_axes computation ModelWrapper load op_type ng_node_factory get_ng_inputs get_attribute_value name warning axes broadcast get_attribute_value get_attribute_value name warning sum exp make_reduction_op log cast_axes_for_binary_broadcast cast_axes_for_binary_broadcast cast_axes_for_binary_broadcast cast_axes_for_binary_broadcast cast_axes_for_binary_broadcast cast_axes_for_binary_broadcast cast_axes_for_binary_broadcast cast_axes_for_binary_broadcast not_equal cast_axes_for_binary_broadcast not_equal cast_axes_for_binary_broadcast not_equal name warning cast_axes_for_matmul Transpose get_attribute_value name dot warning cast_to_pos_axes cast_axes_for_matmul get_attribute_value get_attribute_value get_attribute_value join reorder_axes list get_attribute_value max range len get_attribute_value get_attribute_value get_attribute_value lengths get_attribute_value axes length get_output_names append tensor_slice len get_attribute_value get_attribute_value reciprocal unflatten get_attribute_value sqrt rename_axes append list flatten_at index lengths flatten cumprod len get_attribute_value name warning make_axes op_type cast_axes broadcast axes cast_axes len get_attribute_value verify_symmetric_padding len get_attribute_value get_attribute_value get_dilations get_strides get_pads length output_dim make_axes constant cast_to_pos_axes get_conv_params deconvolution get_attribute_value reorder_axes make_conv_output_axes tensor_slice convolution int len split_pads_into_pairs get_attribute_value get_kernel_shape get_strides get_pads length int make_axes update get_pool_params reorder_axes tensor_slice pooling make_pool_output_axes lengths get_attribute_value axes make_axes make_axis get_attribute_value get_reduction_axes index ng_op_type expand_dims make_model make_transformer make_node make_graph computation make_model make_node make_graph ModelWrapper graph get_initializer ModelWrapper get_ng_placeholder get_ng_axes ModelWrapper get_ng_node get_ng_variable get_ng_constant get_initializer get_ng_nodes_dict ModelWrapper get_ng_inputs FLOAT make_tensor var make_batch_norm_node reshape convert_and_calculate astype float32 mean array array make_node ones import_and_compute astype float32 make_model make_node make_graph shape make_onnx_model_for_conv_op computation import_and_compute_conv reshape import_and_compute_conv reshape broadcast_to import_and_compute_conv reshape make_node ones convert_and_calculate astype float32 pad reshape convert_and_calculate make_node broadcast_to convert_and_calculate make_node reshape reshape convert_and_calculate make_node reshape convert_and_calculate make_node reshape convert_and_calculate make_node broadcast_to convert_and_calculate make_node reshape numpy_func array make_node convert_and_calculate convert_and_calculate array make_node logical_not shape make_model make_node make_graph get_transformer array make_onnx_model_for_matmul_op computation T shape make_model make_node make_graph get get_transformer make_onnx_model_for_gemm_op computation array assert_onnx_import_equals_callable assert_onnx_import_equals_callable assert_onnx_import_equals_callable assert_onnx_import_equals_callable pop parametic_relu make_node assert_onnx_import_equals_callable assert_onnx_import_equals_callable array array array make_node convert_and_calculate convert_and_calculate make_node make_node convert_and_calculate make_node convert_and_calculate negative convert_and_calculate make_node convert_and_calculate make_node ceil convert_and_calculate make_node convert_and_calculate make_node convert_and_calculate reduce make_node make_node convert_and_calculate reduce add len reshape convert_and_calculate make_node reshape convert_and_calculate make_node reshape make_node T make_node reshape transpose convert_and_calculate convert_and_calculate array make_node T make_node concatenate convert_and_calculate array reshape convert_and_calculate make_node reshape convert_and_calculate wrap_node make_node make_model get_transformer import_onnx_model append make_graph computation import_graph_def get_op_handle global_variables_initializer Variable float32 placeholder matmul sigmoid TFImporter as_graph_def zeros array log global_variables_initializer Variable float32 matmul TFImporter as_graph_def reduce_mean zeros import_graph_def Graph data_dir random_data reset read_data_sets max_iter import_meta_graph checkpoint_path batch_size data_dir random_data TFImporter get_collection_handle reset next_batch range read_data_sets get_restore_op max_iter import_meta_graph checkpoint_path batch_size data_dir random_data reset next_batch range read_data_sets Variable float32 placeholder add matmul reduce_mean zeros global_variables_initializer Variable float32 matmul placeholder reduce_mean zeros join remove print search listdir system remove_event_dump system seed as_list relu Variable reshape astype float32 placeholder max_pool matmul conv2d int64 reduce_mean sparse_softmax_cross_entropy_with_logits global_variables_initializer next_batch bias_add read_data_sets list product list product np_layout_shuffle randn constant rand broadcast_to zeros array make_pos_axes broadcast_to lengths broadcasted_shape axes make_axes ng_op iter fill empty array Transpose named make_axes cast_axes cast_to_pos_axes shape make_pos_axes one_hot axes cross_entropy_multi axes_with_order softmax make_axes cast_axes join list reshape transpose len set shape enumerate NodeDef isinstance TensorShape AttrValue Tensor ndarray isinstance split min zip len zip abs len constant axes slice len reorder_pos_axes lengths zip append tensor_slice enumerate list range reversed len dict lengths zip len dtype float32 dtype int32 _validate_slice indices _validate_slice make_axis items list defaultdict extend append isinstance Iterable is_flattened list reversed _make_stride zip append make_axes isinstance name dict as_nested_list make_axes Iterable append enumerate list slice length append range named update isinstance isinstance slice shape indices zip value_tensor SequentialOp make_axes make_axes make_axes make_axes tuple index is_scalar make_axes axes Axes extend named name is_constant format asarray AssignableTensorOp make_axes broadcast exp axes sample_axes make_axes axes sample_axes make_axes axes sample_axes make_axes axes recurrent_axis sample_axes make_axes type generate_add_delta equal generate_add_delta equal axes generate_add_delta broadcast axes reduction_axes generate_add_delta sum prod equal broadcast tuple make_axes float sum log cross_entropy_binary_inner upper str storage_bits CopyFrom axes_to_protobuf is_recurrent isinstance Axis name length bytes is_batch _axes assign_scalar mapping dict AxesMap CopyFrom bytes axis_to_protobuf add Axes dtype isinstance extend dtype_to_protobuf tobytes shape Tensor dtype CopyFrom isinstance axes_map_to_protobuf axis_to_protobuf dtype_to_protobuf start integer_types asscalar generic stop step Slice assign_scalar CopyFrom axes_to_protobuf ndarray is_scalar_type isinstance tensor_to_protobuf add scalar assign_op_attr Op dtype CopyFrom hasattr bytes tensor_to_protobuf tensor_description dtype_to_protobuf difference metadata getattr valfun add_edge hasattr DATA _ops isinstance OTHER CONTAINER difference CONTROL getattr _args forward _control_deps CopyFrom op_to_protobuf GraphDef all_op_references add append add_edges dtype fromstring pb_to_dtype WhichOneof slice_val list axes name map UUID make_axes HasField Axis HasField name UUID FlattenedAxis flattened_axes protobuf_to_axes pb_to_dict map AxesMap __path__ import_module hasattr iter_modules str pb_to_tensor setattr protobuf_attr_to_python name get_ngraph_op_cls UUID __init__ difference startswith op_type tensor __new__ CopyFrom GraphDef add clear dtype list string_val hasattr _args ops map add OrderedSet pb_to_dtype edges zip append setattr tostring write dtype encode list items isinstance extend dtype_to_protobuf add shape binary_type TensorManifest Parse TensorManifest set_op_value string_val bool_val str list name length GraphDef add shape edges op_type encode append _serialize_graph replace ops set double_val WhichOneof startswith int items axes _clean_tag dict int_val pack serialize_protobuf len int time ParseFromString serialize_protobuf Event SerializeToString u32 crc32c array lstrip debug format sub float _clean_tag make_histogram astype _clean_tag append make_histogram_buckets sum bisect_left len make_image ndarray reshape _clean_tag shape fromarray close getvalue save StringIO shape make_audio _clean_tag writeframes setcomptype close getvalue tostring setframerate open float setnchannels setsampwidth flush StringIO prod append list range product T list product slicable reshape float32 dot pixel_indices zeros range list product slicable reshape float32 shape pixel_indices zeros range T list product slicable reshape float32 dot shape pixel_indices zeros range transformer_name get_default_tolerance __overwrite_rtol_atol list multi_index nditer f extend copy float32 shape zeros output_flex_ids reset_entry variable make_axis placeholder make_axis make_axis placeholder shape make_axis make_axes unpack_list isinstance isinstance format print unpack_list float assert_allclose const_executor get_placeholder_from_operand get_placeholder_from_operand ones reshape print copy dot template_create_placeholder_and_variable dot reshape template_create_placeholders_for_multiplication print print reshape template_create_placeholder placeholder dot ConvParams dimI dimF float32 placeholder ax_i conv_params uniform reference_conv ax_f ax_o RandomTensorGenerator dimO RecvOp SendOp make_axis make_axes ScatterSendOp GatherRecvOp make_axis ScatterRecvOp make_axes GatherSendOp sorted keys data empty itemsize dtype isinstance ExOp params bind_ptr flex_scale_info scale fixed_point_resolution WeakSet take astype astype take unique zip sum bind_to_cpu astype shape int32 fill CTC MLSL get_process_count get_process_idx init prepare SourceModule get_function prepare SourceModule get_function _get_convert_kernel prepare SourceModule get_function min _get_sm_count list remove min _optimize_loop_axis shape _is_buffer append max range list zip append add_dep range len isinstance list isinstance tuple TensorDescriptionWrapper shape append range len str dtype list is_flex isinstance FlexPtrDescription zip _get_register_type GenerationContext _is_buffer flex_entry type range append len join list zip append range keys shared_buffers values has_argmaxmin str dtype flex_stats_ptr extend set _get_register_type append td FlexScaleDescription flex_entry FlexPtrDescription extend set append td FlexScaleDescription _build_register_mapping dtype str list is_flex _generate_stage_code isinstance zip join extend _preprocess_ops add _generate_kernel_args _is_buffer append _generate_kernel_code range len _get_compound_kernel _get_axes_mapping prepare _are_flex_params _wrap_tensor_descriptions SourceModule get_function _prepare_compound_kernel prepared_async_call str join format append enumerate len isinstance isinstance mem_get_ipc_handle bcast prepare format SourceModule get_function _get_copy_transpose_kernel list strides args int range bin len _magic32 get_attributes get join user_cache_dir makedirs delattr __wrapped__ get prepare SourceModule get_function prepare SourceModule get_function get join prepare append SourceModule get_function get prepare SourceModule get_function get str join format read close write eval open append exists split join list search group append open communicate join Popen returncode items list get_attributes format get_ptx_file join getmtime print insert module_from_file run_command extract_includes get_function prepare append split sort min ceil range append append append K_partitions get prepare SourceModule get_function get prepare SourceModule get_function get prepare SourceModule get_function get prepare SourceModule get_function get prepare SourceModule get_function dict prepare SourceModule get_function join list tuple reduce extend prepare dict _magic64 get_function append _ceil_div range SourceModule len prepare flex_sig_bprop SourceModule get_function prepare flex_sig_sort native_str SourceModule get_function isinstance isinstance dict prepare_template_vals flex_sig prepare SourceModule get_function prepare_template_vals flex_sig prepare SourceModule get_function prepare_template_vals flex_sig prepare SourceModule get_function prepare_template_vals flex_sig prepare SourceModule get_function prepare_template_vals flex_sig prepare SourceModule get_function prepare_template_vals flex_sig prepare SourceModule get_function prepare_template_vals flex_sig prepare SourceModule get_function prepare_template_vals flex_sig prepare SourceModule get_function prepare_template_vals flex_sig prepare SourceModule get_function prepare_template_vals flex_sig prepare SourceModule get_function method_handlers_generic_handler add_generic_rpc_handlers server_options stub_options socket bind setsockopt SO_REUSEADDR AF_INET SOCK_STREAM SOL_SOCKET debug strip getpid sleep Get_rank COMM_WORLD seed int is_port_open debug add_HetrServicer_to_server add_argument add_insecure_port write_server_info HetrServer start server ThreadPoolExecutor ArgumentParser sleep parse_args Get_rank pop update isinstance add OrderedSet args pop update hasattr isinstance add OrderedSet args set OrderedSet find_recvs send_node comm_path_exists get_iterable add_control_dep set_parallel_axes isinstance axes reduction_axes ordered_ops y_out_axes x_out_axes set check_connectivity_state move_exop_to_after_exop output_decls input_decls output_decls names append enumerate itemsize mkldnn_engine axes len create_layout_md get_ctypes_arg get_size_mkl_order print_kernel mkldnn_verbose layout_reorder get_ctypes_arg user_input_decls output_decls enumerate append insert append list tuple reversed split_points_to_groups append range WeakValueDictionary name_scope _get_thread_name_scope append NameScope get_or_create_scope dirname makedirs getenv append expanduser join makedirs get join int format print indent make_axis make_axis isinstance make_axes tensorview random TensorDescription broadcast tensorview random TensorDescription broadcast tensorview length random flatten TensorDescription range val2 broadcast slice make_axis slice_axis slice make_axis slice_axis slice make_axis slice_axis slice make_axis slice_axis slice make_axis slice_axis slice make_axis slice_axis make_axis slice make_axis slice_axis slice make_axis slice_axis value make_axis AxesMap make_axes AxesMap AxesMap axes check_derivative placeholder uniform PrintOp make_axes readouterr placeholder PrintOp make_axes array assert_allclose SubVisitor Visitor constant as_op do_pass get_simple_graph MemoryManager free MemoryManager free MemoryManager free MemoryManager free MemoryManager free MemoryManager free MemoryManager MemoryManager MemoryManager variable one variable variable variable pad variable make_axes tensor_slice variable make_axes placeholder make_axes Op constant configure Dummy hello evaluate generate_try_twice try_twice HelloCodeGenerator generate_hello generate_make_list make_list execute deepcopy hasattr __dict__ zip _args strip_dict flatten pb_to_axis make_axis axis_to_protobuf pb_to_axis make_axis axis_to_protobuf pb_to_tensor reshape tensor_to_protobuf assert_allclose pb_to_tensor tensor_to_protobuf float32 assert_allclose make_axis op_to_protobuf slice placeholder dict make_axes assert_object_equality protobuf_to_op named serialize_graph placeholder sorted all_op_references zip deserialize_graph assert_object_equality serialize_graph get_simple_graph deserialize_graph assert_object_equality serialize_graph get_simple_graph do_pass name SerializationPass unlink get_simple_graph sorted create_send_recv_graph all_op_references zip deserialize_graph assert_object_equality serialize_graph all_op_references get_simple_graph execute_convolution print print getenv dirname decode print template_dot_two_placeholders print template_dot_one_placeholder print template_dot_one_placeholder_and_scalar template_one_placeholder template_one_placeholder template_two_placeholders variable placeholder sequential set_parallel_axes set_parallel_axes assert_array_equal str list join make_axis xfail tolist set start make_axes myProcess append range CPUMlslBroadcastRecvOp len make_axis skip make_axis xfail placeholder skip randint placeholder skip randint xfail randint lengths ones xfail randint placeholder make_axes xfail placeholder randint make_axis skip xfail randint make_axis placeholder xfail randint make_axis placeholder make_axis xfail placeholder named randint randint xfail variable array placeholder make_axis ConvParams make_axis UniformInit variable float32 placeholder ax_i conv_params uniform make_axes ax_f assert_array_equal RandomTensorGenerator convolution feature_axes xfail list len launch MPILauncher close run_resnet_benchmark skip seed ArrayIterator load_data make_placeholders getenv assert_allclose train_mnist_mlp OrderedSet check_device_assign_pass OrderedSet check_device_assign_pass make_axis check_communication_pass OrderedSet check_device_assign_pass check_communication_pass placeholder create_send_recv_graph create_scatter_gather_graph RecvOp SendOp make_axes create_scatter_gather_graph update_comm_deps dict update_comm_deps make_axis make_axes zip ScatterSendOp GatherRecvOp axes ScatterRecvOp make_axes zip GatherSendOp assert_axes_eq_len list constant print array range assert_allclose print constant multiply constant constant make_axis array dot make_axis named array constant make_axis print multiply array constant make_axis print add array constant make_axis print multiply add array constant make_axis print dot array is_flex_factory is_flex_factory ConvParams dimI dimF placeholder ax_i conv_params uniform update_conv reference_conv ax_f ax_o bprop_conv dimO convolution ConvParams deriv deconvolution reference_deconv_fprop ax_i conv_params uniform ax_f ax_o reference_deconv_bprop ConvParams deriv deconvolution reference_deconv_fprop placeholder ax_i conv_params uniform ax_f ax_o dimO reference_deconv_bprop ConvParams placeholder ax_i ConvParams placeholder ax_f ConvParams placeholder ax_f ConvParams placeholder ax_i conv_params uniform ax_f ax_o sum convolution ConvParams ones flatten_at lengths named conv_params axes_with_order make_axes tensor_slice sum convolution bind_to_cpu zeros_like astype shape int32 zeros CTC items list placeholder named axes_with_order negative array randint range get_random_shape randn shape constant variable enumerate make_axis astype named dot make_axes sum items list placeholder named array ones_like constant make_axis ones flatten_at length lengths dot make_axes assert_allclose constant make_axis ones lengths make_axes make_axis array assert_allclose multiply constant make_axis array sum constant make_axis array constant assert_allclose constant reshape size lengths named make_axes assert_allclose make_axis squared_L2 reshape size lengths placeholder make_axes npred tuple argon_skip_now placeholder uniform getattr bered make_axes flex_skip_now check_derivative skip placeholder discrete_uniform getattr bered make_axes OrderedDict param constant ones prod assert_allclose broadcast check_derivative placeholder shape_to_axes shape get_all_reduction_axes prod axes uniform assert_allclose axes uniform check_derivative reciprocal make_axes make_axes be_op axes placeholder uniform getattr compare_f_at_x be_op axes check_derivative placeholder uniform getattr be_op axes check_derivative placeholder uniform getattr np_op be_op axes uniform getattr np_op make_axis be_op axes reshape placeholder uniform getattr shape list exp reshape exp exp np_softmax axes assert_allclose placeholder uniform cross_entropy_binary_logistic_shortcut cross_entropy_binary_logistic axes placeholder sigmoid uniform softmax cross_entropy_binary_inner axes placeholder np_softmax softmax_adiff zeros_like zeros range np_softmax make_axis length reshape adiff_softmax uniform numeric_derivative make_axes empty log assert_allclose axes reshape length uniform log axes uniform softmax compare_f_at_x axes uniform check_derivative softmax axes uniform softmax compare_f_at_x axes uniform check_derivative softmax np_softmax axes placeholder uniform softmax compare_f_at_x cross_entropy_multi T axes placeholder uniform softmax compare_f_at_x cross_entropy_multi np_softmax axes check_derivative placeholder uniform softmax cross_entropy_multi np_softmax axes placeholder uniform softmax compare_f_at_x cross_entropy_multi np_softmax axes check_derivative placeholder uniform softmax cross_entropy_multi axes placeholder T axes lengths placeholder uniform make_axes axes uniform check_derivative sigmoid axes check_derivative sigmoid uniform log axes uniform compare_f_at_x sigmoid multi_index nditer length extend lengths placeholder random_integers zeros one_hot_comparison make_axis make_axes minimum make_axis maximum placeholder uniform make_axes abs clip multiply constant array make_axes multiply constant array make_axes placeholder argmax make_axes placeholder make_axes sequential make_axis placeholder axes sum mean placeholder deriv axes variance placeholder sum reciprocal deriv axes variance placeholder sqrt sum placeholder placeholder constant make_axis deriv rand sum broadcast placeholder constant make_axis axes check_derivative placeholder dot uniform make_axes sum seed sum constant randn axes flatten_at check_derivative placeholder dot uniform make_axes cast_axes constant randn_abs_clip is_flex_factory softmax make_axes log constant randn_abs_clip is_flex_factory add assert_allclose randn_abs_clip is_flex_factory constant sum reciprocal constant randn_abs_clip is_flex_factory add sum assert_allclose constant make_axis randn_abs_clip is_flex_factory reshape add make_axes assert_allclose deriv variable placeholder sequential sigmoid dot NumpyLogreg array log flatten shape take unique zip zeros sum list param make_axis constant uniform full_lengths make_axes append range make_axis variable sign array len variable sequential sum variable sequential value_of variable persistent_tensor sequential array make_axis lengths placeholder zeros concat_along_axis length rand variable make_axes assert_allclose constant variable asarray assert_allclose make_axis placeholder array PoolParams PoolParams placeholder get_fprop_bprop dimI reshape PoolParams dimO assert_allclose make_axis placeholder ExpandDims zip array range len make_axis make_axis placeholder make_axis placeholder make_axis named cast_axes make_axis placeholder sum make_axis deriv variable axes_with_order expand_dims cast_axes make_axes make_axis TensorDescription make_axes print uniform print uniform make_transformer variable close copy uniform make_axes sum computation normal print persistent_tensor named mean std read BytesIO write_raw_np seek random items list BytesIO seek random read_np_values write_np_values AssignOp variable make_axes list assign_ops make_axes range assert_allclose enumerate variable make_axes assert_allclose make_axes zeros json_dumps_manifest Parse TensorManifest AssignOp variable make_axes named make_axis array make_axis float32 stack make_axes RandomTensorGenerator range len constant make_axis reshape dot uniform make_axes eye sum make_axis reshape tensor_size placeholder make_axes constant make_axis dot make_axes array make_axis placeholder variable make_axis temporary str from_iterable print exit | **DISCONTINUATION OF PROJECT.** This project will no longer be maintained by Intel. Intel will not provide or guarantee development of or support for this project, including but not limited to, maintenance, bug fixes, new releases or updates. Patches to this project are no longer accepted by Intel. If you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain patches for the community, please create your own fork of the project. # nGraph Python ### An Intermediate Representation, Compiler, and Executor for Deep Learning *Updated: March 17, 2018* Welcome to the nGraph Python repo. Now that we've released our [C++ rewrite] to the community, we are transitioning away from this original Python implementation. You can browse here to learn a bit about the roots of the [legacy] project. ## Why did we build nGraph? When Deep Learning (DL) frameworks first emerged as the vehicle for training and | 783 |
NetEase-GameAI/SARG | ['text generation'] | ['SARG: A Novel Semi Autoregressive Generator for Multi-turn Incomplete Utterance Restoration'] | data_utils.py covert_weight_from_bert_to_sarg.py run_eval.py modeling_sarg.py run_train.py transformer_utils.py score.py finetune_trainer.py convert_weight_bert_to_ptrgen _backtrack data_iter convert_tags _lcs_table str2bool insert_dummy _decode_valid_tags _compute_lcs convert_tokens_to_string set_seed is_tensorboard_available is_apex_available get_tpu_sampler torch_distributed_zero_first Trainer is_wandb_available LSTMWithCovAttention CoverageAttention SARGModel SARGPreTrainedModel SARGConfig main eval_on_standard_test_set DataTrainingArguments ExampleDataset DictDataCollator ModelArguments main get_examples BasicTokenizer Scorer make_gram whitespace_tokenize _is_whitespace _is_control _is_punctuation MultiHeadAttention Embedding FeedForward TransformerBlock from_pretrained SARGModel SARGConfig _lcs_table max range len append append zip join print insert_dummy convert_tokens_to_string _compute_lcs append tokenize range len seed manual_seed_all manual_seed barrier from_pretrained DataLoader tolist add append to range rouge_score get replace Scorer ExampleDataset size set zip convert_tokens_to_string tokenize join items em_score print corpus_bleu_score infer tqdm dict insert_dummy resolution_score get_examples split print add_argument eval_on_standard_test_set ArgumentParser parse_args data_iter join info cls_token_id convert_tokens_to_ids convert_tags extend tqdm startswith split append range tokenize exists len from_pretrained gradient_accumulation_steps config_name save_model get_linear_schedule_with_warmup blend_copy Trainer SARGModel warning mix_neighbors device do_train output_dir seed basicConfig set_seed SARGConfig cov_weight is_world_master tokenizer_name ExampleDataset change_weight save_pretrained alpha info fp16 parse_args_into_dataclasses DictDataCollator train int n_gpu blend_gen model_name_or_path AdamW bool HfArgumentParser local_rank get_examples len strip split category category startswith startswith category ord split | # SARG [](https://github.com/NetEase-GameAI/SARG/blob/master/LICENSE) This repository is the implementation of [SARG: A Novel Semi Autoregressive Generator for Multi-turn Incomplete Utterance Restoration](https://arxiv.org/abs/2008.01474) in python 3.6 environment and pytorch 1.5.1.  ## Requirements To install requirements: ```setup pip install -r requirements.txt ``` Note: install the torch-gpu version corresponding to the version of cuda. | 784 |
Networks-Learning/regression-under-assistance | ['medical diagnosis'] | ['Regression Under Human Assistance'] | generate_synthetic.py train.py plot_util.py algorithms.py baseline_classes.py process_image_data.py generate_human_error.py test.py myutil.py process_text_data.py load_data ma write_txt_file save latexify ones convolve rc | # Regression Under Human Assistance This is a repository containing code and data for the paper: > A. De, N. Okati, P. Koley, N. Ganguly and M. Gomez-Rodriguez. _Regression Under Human Assistance._ AAAI Conference on Artificial Intelligence , February, 2020. The paper is available [here](https://arxiv.org/pdf/1909.02963.pdf). ## Pre-requisites This code depends on the following packages: 1. `numpy` 2. `pillow` 3. `matplotlib` 4. `sklearn` | 785 |
NeuroAI-HD/HD-GLIO | ['medical image segmentation', 'semantic segmentation'] | ['Automated Design of Deep Learning Methods for Biomedical Image Segmentation'] | hd_glio/setup_hd_glio.py hd_glio/hd_glio_predict.py hd_glio/hd_glio_predict_folder.py setup.py hd_glio/utils.py hd_glio/paths.py hd_glio/__init__.py main main maybe_download_weights enablePrint blockPrint maybe_download_weights flair t1 add_argument t2 predict_cases t1c output_file ArgumentParser parse_args output_folder predict_from_folder processes overwrite_existing input_folder join maybe_mkdir_p read remove isdir print loadtxt extractall rmtree devnull open __stdout__ | NeuroAI-HD/HD-GLIO | 786 |
NeuroAI-HD/HD-GLIO-AUTO | ['medical image segmentation', 'semantic segmentation'] | ['Automated Design of Deep Learning Methods for Biomedical Image Segmentation'] | fslinstaller.py scripts/run.py UnknownArchiveType make_applications_links move line_starts_replace SiteNotResponding md5File SudoPasswordError fastest_mirror RunCommandError download_file external_validate MsgUser configure_matlab run_cmd_dropstdout DownloadFileError yes_no shell_config parse_options CopyFileError copy_file setup_system_environment CheckX11Warning AddToFileError is_writeable_as_root MakeApplicationLinksError check_for_updates archive_type get_releases is_writeable move_file IsDirectoryError memoize ImproperlyConfigured get_web_manifest OpenUrlError latest_release get_sudo_pwd run_cmd SetupEnvironmentSkip MoveError get_json UnsupportedOs file_contains PostInstallError InvalidVersion NotAFslVersion Spinner ConfigureMatlabError DeletionRefused FixFslDirError create_file download_release SetupEnvironmentError get_installer ConfigureMatlabWarn add_to_file CreateFileError add_fsldir SafeDeleteError build_url run_cmd_displayoutput InstallQuestions setup_environment Progress_bar open_url check_sudo get_installed_version EditFileError Host get_extra post_install check_fsl_install build_url_with_protocol fsl_downloadname InstallArchiveError ServerFailure get_fsldir which_shell GetInstallerError AddFslDirError file_checksum MoveFileError DownloadError check_X11 GetInstalledVersionError get_profile safe_delete line_string_replace list_releases AutoDict InstallInstallerError parsesha256sumfile SelfUpdateError shell_colours archive_version BadVersion fix_fsldir check_install_location get_web_version_and_details install_archive Settings Version do_install InstallError file_contains_1stline ChecksumCalcError install_installer ExtraDownloadError edit_file sha256File GetFslDirError temp_file_name get_csv_dict self_update register_to_t1 reorient2std run mkstemp close fdopen cycle flush insert communicate debug get_sudo_pwd write Spinner returncode start Popen split flush insert debug communicate get_sudo_pwd write Spinner returncode start Popen split flush insert debug communicate get_sudo_pwd write returncode call Popen split flush communicate debug write Popen getpass failed check_sudo message join run_cmd isdir join run_cmd_dropstdout join rstrip isdir debug run_cmd debug search escape close compile open debug search escape close compile open startswith join remove run_cmd_dropstdout move temp_file_name debug write close edit_function move_file open temp_file_name isinstance debug write close move_file split open temp_file_name isinstance debug close write move_file split machine hasattr name linux_distribution dist lower Version mac_ver release NamedTemporaryFile close join remove run_cmd_dropstdout temp_file_name debug close write update read Progress_bar message close position sha256 open split update read Progress_bar message md5 close position open Request debug urlopen setdefaulttimeout update int read Progress_bar message open_url write close info sleep range open extend extend join time debug setdefaulttimeout close urlopen startswith create_connection debug setdefaulttimeout debug ask_question validate_fsldir split join readline debug strip close Version exists open str join chmod get_installer message temp_file_name debug execv ok extend executable download_file debug get_web_manifest debug warning major get_web_manifest debug get_web_manifest items list message debug get_releases get_releases debug join debug abspath copy_file message debug message debug join exists isdir InstallQuestions add_question open_url debug join list reader open_url debug dict iter zip AutoDict get_releases debug Version latest_release str remove join message get_extra exists temp_file_name debug ask_question ok download_file warning isfile get_web_version_and_details debug expanduser join debug shell_config edit_file get_profile add_to_file debug shell_config get_profile create_file join add_to_file debug shell_config mkdir dirname expanduser edit_file file_contains exists create_file join isdir debug shell_config file_contains_1stline append edit_file exists create_file configure_matlab message debug shell_config add_fsldir get_profile get_fsldir which_shell fix_fsldir file_contains lower run_cmd join post_inst_dir make_applications_links message is_writeable_as_root install_installer check_X11 debug applications ok is_writeable run_cmd_displayoutput x11 exists join run_cmd_dropstdout is_writeable_as_root message move debug ask_question ok archive_type get_fsldir get_installed_version is_writeable dirname safe_delete clean_up_temp exists get_installed_version message join basename run_cmd_dropstdout message lexists debug islink exists join message communicate debug returncode startswith exists Popen split env_all archive_version get_installed_version warning archive bold get_web_version_and_details exists fastest_mirror configure_environment install_archive post_install title quiet o_s get_feeds mirrors_file update message debug ask_question list_versions get_fsldir test_installer download default download_release print check_for_updates ok mirrors getuid safe_delete mirror list_releases get_source self_update add_option_group add_option OptionParser OptionGroup check_output format replace check_output format replace chown check_output cpu_count get_data save Pool list basename getcwd map copyfile header get_zooms dirname append sum format partial replace product chdir copy mean zip listdir enumerate load join int affine remove isdir print min makedirs Nifti1Image copymode std len | # Automated processing of MRI in neuro-oncology This container provides easy to use access to our automated processing tool (HD-GLIO-AUTO) for brain tumor MRI scans. HD-GLIO-AUTO is the result of a joint project between the Department of Neuroradiology at the Heidelberg University Hospital, Germany and the Division of Medical Image Computing at the German Cancer Research Center (DKFZ) Heidelberg, Germany. If you are using HD-GLIO-AUTO please cite the following three publications: * Kickingereder P, Isensee F, Tursunova I, Petersen J, Neuberger U, Bonekamp D, Brugnara G, Schell M, Kessler T, Foltyn M, Harting I, Sahm F, Prager M, Nowosielski M, Wick A, Nolden M, Radbruch A, Debus J, Schlemmer HP, Heiland S, Platten M, von Deimling A, van den Bent MJ, Gorlia T, Wick W, Bendszus M, Maier-Hein KH. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol. 2019 May;20(5):728-740. (https://doi.org/10.1016/S1470-2045(19)30098-1) * Isensee F, Schell M, Tursunova I, Brugnara G, Bonekamp D, Neuberger U, Wick A, Schlemmer HP, Heiland S, Wick W, Bendszus M, Maier-Hein KH, Kickingereder P. Automated brain extraction of multi-sequence MRI using artificial neural networks. Hum Brain Mapp. 2019; 1–13. (https://doi.org/10.1002/hbm.24750) * Isensee F, Petersen J, Kohl SAA, Jaeger PF, Maier-Hein KH. nnU-Net: Breaking the Spell on Successful Medical Image Segmentation. arXiv preprint 2019 arXiv:1904.08128. (https://arxiv.org/abs/1904.08128) The HD-GLIO-AUTO container requires the following MRI sequences from a brain tumor patient as input (with MRI sequences either in DICOM or NIfTI format): * T1-weighted sequence before contrast-agent administration (T1-w) acquired as 2D with axial orientation (e.g. TSE) or as 3D (e.g. MPRAGE) * T1-weighted sequence after contrast-agent administration (cT1-w) acquired as 2D with axial orientation (e.g. TSE) or as 3D (e.g. MPRAGE) * T2-weighted sequence (T2-w) acquired as 2D * Fluid attenuated inversion recovery (FLAIR) sequence acquired as 2D with axial orientation (e.g. TSE). A 3D acquisition (e.g. 3D TSE/FSE) may work as well. | 787 |
NiallJeffrey/DeepMass | ['density estimation', 'time series'] | ['Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks'] | unit_tests.py DES_mass_maps_demo/original_run_scripts/script_functions.py CMB_foreground_demo/wph_synthesis_script.py deepmass/map_functions.py DES_mass_maps_demo/original_run_scripts/make_single_training_files.py CMB_foreground_demo/foregrounds_utils.py DES_mass_maps_demo/original_run_scripts/test_wiener_prototype.py setup.py deepmass/lens_data.py deepmass/wiener.py deepmass/cnn_keras.py DES_mass_maps_demo/original_run_scripts/training_data/generate_training.py deepmass/spherical_deepmass.py augmented_foreground_generation SoftHistogram objective BatchGenerator LossHistory UnetlikeBaseline SimpleModel ks_inv make_healpix_map random_map ks_fourier_matrix shape_noise_realisation ks radial_profile mean_square_error compute_spectrum_map power_function generate_sv_maps rescale_unit_test mask_images rescale_map make_map HealpyUNet rotate H_adjoint H_operator filtering plot_cnn load_data_list plot_noisy_clean load_data load_data_preallocate load collect print gaussian_filter astype float32 range ks_fourier_matrix mean flatten shape vstack real meshgrid empty imag ks len flatten abs log dtype str apply to sum range astype stack time preconfigure backward print reshape float32 softhist zeros numpy randint print close gnomview bincount ang2pix make_healpix_map shuffle copy fftfreq reshape T where ks_fourier_matrix flatten ks_fourier_matrix flatten sqrt float exp pi normal int power_function reshape ndenumerate sqrt zeros conj all print exit rescale_map linspace range len int astype indices shape sqrt bincount ravel zeros sqrt ndenumerate data arange ks_inv ang2pix where save nside2npix open str npix2nside pix2ang field ks_fourier_matrix random_map filtering read_map real bincount range ks gnomview normal close sqrt shape_noise_realisation empty time T UNSEEN print zeros array imag len rotate_map_pixel rotate_map_alms Rotator ud_grade reorder fftfreq fft2 shape real meshgrid fftfreq shape real meshgrid ifft2 fftshift H_adjoint min fft2 real zeros H_operator range ifft2 imag load str concatenate print array range len concatenate load str print shape empty array range len subplot imshow figure randint range len subplot imshow figure randint range len | # DeepMass [](https://arxiv.org/abs/1908.00543) [](https://opensource.org/licenses/MIT) ## Cosmological map inference with deep learning DeepMass was developed for inferring dark matter maps from weak gravitational lensing measurements, and uses deep learning to reconstruct cosmological maps. DeepMass can also be incorporated into a Moment Network (see [ArXiv:2011.05991](https://arxiv.org/abs/2011.05991)) enabling high-dimensional likelihood-free inference. ##  (Dark matter mass map demo [DES_mass_maps_demo/Training_example](https://github.com/NiallJeffrey/DeepMass/blob/main/DES_mass_maps_demo/Training_example.ipynb)) ##  | 788 |
NiallJeffrey/MomentNetworks | ['density estimation', 'time series'] | ['Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks'] | setup.py momentnetworks/demo.py log_posterior_likelihood grav_wave_series initial_parameters simple_leaky log_prior generate__signal simpler_leaky log_posterior_delfi log_likelihood multivariate_normal identity where mean pinv logspace real zeros sum range array aLIGOZeroDetHighPower int str print vstack get_td_waveform noise_from_psd empty range | # MomentNetworks [](https://arxiv.org/abs/2011.05991) [](https://opensource.org/licenses/MIT) Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks [ArXiv paper (Accepted in the Third Workshop on Machine Learning and the Physical Sciences, NeurIPS 2020)](https://arxiv.org/abs/2011.05991)  ## 100-dimensional model [](https://colab.research.google.com/github/NiallJeffrey/MomentNetworks/blob/master/MomentNetwork_demo/moment_network_100D.ipynb) From a 100-dimensional parameter space, directly estimate the mean and covariance for pairs of parameters with Moment Networks: [MomentNetwork_demo/moment_network_100D.ipynb](https://github.com/NiallJeffrey/MomentNetworks/blob/master/MomentNetwork_demo/moment_network_100D.ipynb) Google Colab notebook can be run in browser with GPU acceleration | 789 |
NicolasAG/convai | ['text generation'] | ['The RLLChatbot: a solution to the ConvAI challenge'] | models/hred/dialog_encdec.py models/hred/rank_responses.py models/candidate.py models/hred/Evaluation/diversity.py models/hred/telegram_bot.py bot.py utils.py models/hred/search.py models/hred/sample.py ranker/models/long_term/get_model_stats.py models/alicebot/aiml/DefaultSubs.py models/hred/convert-text2dict.py ranker/models/q_estimator/get_best_model.py config.py storage.py model_selection_zmq.py ranker/train.py models/alicebot/nlu_alicebot.py models/wikipedia.py models/wrapper.py ranker/q_train.py models/hred/generate_response_candidates.py bot_zmq.py models/alicebot/nlg_alicebot.py models/hred/compute_response_recall.py models/hred/Evaluation/tfidf_metrics.py models/hred/model.py ranker/q_networks.py ranker/models/short_term/get_model_stats.py ranker/features.py ranker/models/long_term/get_best_params.py models/hred/compare.py ranker/q_experiments.py models/hred/server.py models/alicebot/aiml/PatternMgr.py ranker/extract_db_for_supervised_ranker.py models/alicebot/aiml/AimlParser.py ranker/extract_amt_for_q_ranker.py ranker/data/amt_data_stats.py ranker/q_test.py ranker/estimators.py ranker/q_data_loader.py models/hred/numpy_compat.py leaderboard.py ranker/models/short_term/get_best_params.py model_selection_q.py models/hred/data_iterator.py models/alicebot/test.py models/hred/visualize_model.py model_selection_mturk.py models/hred/train.py ranker/extract_amt_for_supervised_ranker.py models/hred/SS_dataset.py models/hred/retrieve_response.py bot_final.py bot_mturk.py ranker/test_amtdata_with_oldtf_model.py models/hred/interact.py ranker/embedding_metrics.py ranker/test.py models/hred/state.py ranker/extract_round1_for_supervised_ranker.py ranker/data/build_feature_data.py models/hred/compute_dialogue_embeddings.py models/hred/utils.py models/alicebot/aiml/__init__.py models/alicebot/aiml-en-us-foundation-alice/find_bot_attributes.py model_selection.py models/alicebot/aiml/WordSub.py models/hred/Evaluation/embedding_metrics.py bot_q.py models/hred/adam.py models/hred/convert-wordemb-dict2emb-matrix.py models/alicebot/aiml/Utils.py model_selection_final.py models/alicebot/aiml/Kernel.py ranker/build_colorful_toy_data.py test_app ConvAIRLLBot producer producer_process reply_sender stop_app ChatState response_receiver test_app ConvAIRLLBot producer producer_process demogrify reply_sender stop_app ChatState mogrify response_receiver test_app ConvAIRLLBot producer producer_process reply_sender stop_app ChatState response_receiver test_app ConvAIRLLBot consumer producer reply_sender stop_app ChatState response_receiver get_config dotdict consumer strip_emojis responder get_response ranker isEveryoneAwake submit_job stop_models start_models act demogrify no_duplicate dead_models ModelClient producer mogrify ModelID Policy clean match_data store_data detokenize_utterance tokenize_utterance CandidateQuestions NLUAlice test_profile_answers test_savebrain Adam sharedX compute_encodings parse_args main Timer safe_pickle Model DiverseBeamSampler sample_wrapper RandomSampler BeamSampler Sampler SSFetcher SSIterator prototype_reddit_HRED prototype_ubuntu_HRED_NormOp_ClusterExp3 prototype_debate_GaussOnly_VHRED_NormOp_ClusterExp4 prototype_debate_GaussPiecewise_VHRED_NormOp_ClusterExp6 prototype_book_SkipThought_NormOp_VAE_Exp2 prototype_debate_GaussOnly_VHRED_NormOp_ClusterExp2 prototype_book_SkipThought_NormOp_VAE_Exp5 prototype_debate_GaussPiecewise_VHRED_NormOp_ClusterExp10 prototype_reddit_SkipThought_NormOp prototype_twitter_GaussPiecewise_VHRED_NormOp_ClusterExp7 prototype_twitter_VHRED prototype_twitter_GaussPiecewise_VHRED_LSTMDecoder_NormOp prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp7 prototype_ubuntu_GaussPiecewise_NormOp_VHRED_Baseline_Exp2 prototype_twitter_GaussPiecewise_VHRED_NormOp_ClusterExp4 prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp17 prototype_twitter_Gauss_VHRED_NormOp prototype_book_SkipThought_NormOp prototype_twitter_GaussOnly_VHRED_NormOp_ClusterExp4 prototype_twitter_HRED_NormOp_ClusterExp3 prototype_ubuntu_GaussPiecewise_NormOp_VHRED_Exp6 prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp10 prototype_twitter_HRED_NormOp_ClusterExp5 prototype_twitter_LSTM_NormOp_ClusterExp3 prototype_twitter_GaussPiecewise_VHRED_NormOp_ClusterExp6 prototype_ubuntu_GaussPiecewise_NormOp_VHRED_Baseline_Exp1 prototype_reddit_SkipThought_NormOp_VAE_Exp3 prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp18 prototype_reddit_SkipThought_NormOp_VAE_Exp5 prototype_twitter_GaussPiecewise_VHRED_NormOp_ClusterExp8 prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp12 prototype_reddit_SkipThought_NormOp_VAE_Exp1 prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp13 prototype_debate_HRED_NormOp_ClusterExp1 prototype_ubuntu_GaussPiecewise_NormOp_VHRED_Exp3 prototype_book_SkipThought_NormOp_VAE_Exp6 prototype_state prototype_twitter_GaussOnly_VHRED_NormOp_ClusterExp1 prototype_ubuntu_HRED prototype_twitter_GaussPiecewise_VHRED_NormOp_ClusterExp2 prototype_test prototype_twitter_LSTM_NormOp_ClusterExp2 prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp1 prototype_debate_GaussOnly_VHRED_NormOp_ClusterExp1 prototype_twitter_HRED_NormOp_ClusterExp4 prototype_debate_GaussPiecewise_VHRED_NormOp_ClusterExp8 prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp20 prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp19 prototype_policy_gradient prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp5 prototype_twitter_GaussPiecewise_VHRED_NormOp prototype_twitter_Gauss_VHRED_LSTMDecoder_NormOp prototype_ubuntu_LSTM prototype_twitter_GaussPiecewise_VHRED_NormOp_ClusterExp3 prototype_twitter_lstm prototype_book_SkipThought_NormOp_Baseline_Exp2 prototype_debate_GaussOnly_VHRED_NormOp_ClusterExp7 prototype_twitter_GaussOnly_VHRED_NormOp_ClusterExp2 prototype_test_variational prototype_twitter_HRED_NormOp prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp11 prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp15 prototype_twitter_HRED_NormOp_ClusterExp1 prototype_debate_GaussPiecewise_VHRED_NormOp_ClusterExp9 prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp14 prototype_ubuntu_HRED_NormOp_ClusterExp2 prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp3 prototype_debate_GaussPiecewise_VHRED_NormOp_ClusterExp2 prototype_twitter_GaussOnly_VHRED_NormOp_ClusterExp5 prototype_ubuntu_GaussPiecewise_NormOp_VHRED_Exp2 prototype_twitter_HRED_NoNormalization_ClusterExp1 prototype_debate_HRED_NormOp_ClusterExp2 prototype_book_SkipThought_NormOp_VAE_Exp1 prototype_reddit_SkipThought_NormOp_Baseline_Exp2 prototype_reddit_SkipThought_NormOp_Baseline_Exp1 prototype_debate_GaussPiecewise_VHRED_NormOp_ClusterExp5 prototype_twitter_GaussPiecewise_VHRED_NormOp_ClusterExp1 prototype_debate_GaussOnly_VHRED_NormOp_ClusterExp5 prototype_debate_GaussPiecewise_VHRED_NormOp_ClusterExp4 prototype_policy_gradient_toy prototype_twitter_LSTM_NormOp_ClusterExp1 prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp2 prototype_reddit_HRED_branching_2 prototype_debate_GaussPiecewise_VHRED_NormOp_ClusterExp1 prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp8 prototype_book_SkipThought_NormOp_Baseline_Exp3 prototype_reddit_SkipThought_NormOp_VAE_Exp2 prototype_twitter_VHRED_StandardBias prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp4 prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp9 prototype_reddit_SkipThought_NormOp_VAE_Exp4 prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp6 prototype_twitter_GaussPiecewise_VHRED_NormOp_BOW_ClusterExp16 prototype_book_SkipThought_NormOp_Baseline_Exp1 prototype_debate_GaussPiecewise_VHRED_NormOp_ClusterExp11 prototype_book_SkipThought_NormOp_VAE_Exp4 prototype_twitter_GaussOnly_VHRED_NormOp_ClusterExp3 prototype_debate_GaussPiecewise_VHRED_NormOp_ClusterExp3 prototype_ubuntu_HRED_NormOp_ClusterExp5 prototype_ubuntu_VHRED prototype_ubuntu_GaussPiecewise_NormOp_VHRED_Exp4 prototype_ubuntu_GaussPiecewise_NormOp_VHRED_Exp5 prototype_ubuntu_GaussPiecewise_NormOp_VHRED_Exp1 prototype_twitter_HRED_Large prototype_debate_GaussOnly_VHRED_NormOp_ClusterExp3 prototype_twitter_HRED prototype_ubuntu_HRED_NormOp_ClusterExp1 prototype_debate_GaussOnly_VHRED_NormOp_ClusterExp6 prototype_twitter_VHRED_Large_SkipConnections prototype_ubuntu_HRED_NormOp_ClusterExp6 prototype_debate_GaussPiecewise_VHRED_NormOp_ClusterExp7 prototype_twitter_GaussPiecewise_VHRED_NormOp_ClusterExp5 prototype_reddit_HRED_branching_1 prototype_book_SkipThought_NormOp_VAE_Exp3 prototype_reddit_SkipThought_NormOp_VAE_Exp6 prototype_twitter_HRED_NormOp_ClusterExp2 prototype_debate_GaussOnly_VHRED_NormOp_ClusterExp8 prototype_twitter_HRED_StandardBias prototype_ubuntu_HRED_NormOp_ClusterExp4 Maxout OrthogonalInit GrabProbs LayerNormalization sharedX Adam stable_log Adagrad RMSProp SoftMax NormalInit3D FeedforwardBatchNormalization RecurrentBatchNormalization NormalInit BatchedDot Adadelta ConvertTimedelta UniformInit DPrint NormalizationOperator build_data Vocabulary split main build_vocab build_data Vocabulary lemmatize _get_user_response main build_vocab split main build_data _get_user_response collate_fn ConversationDataset get_loader rnnmlp_r_exp0 rnnmlp_q_exp0 to_dict mlp_q_exp0 mlp_r_exp0 default_params recallat1_contextlen get_test_data regroup_chats main test_individually get_recall get task_done producer_process get_response format total_seconds now put info get join json info START debug act text error observe sleep len get join text dumps post raise_for_status info append task_done put put randint now choice loads strip find PULL socket bind Context recv_json socket send bind PUB mogrify Context PULL socket format bind total_seconds now put mean info append Context recv_json socket format send_json bind dumps info PUSH Context raise_for_status get __delitem__ __setitem__ put put text put PULL socket format bind now info Context recv_json get socket send bind PUB info mogrify Context stop_models Thread exit connect start clean connect pop append now total_seconds join list set intersection keys iteritems sorted format info append float append iteritems START warn put strip_emojis DRQA str list ranker FIXED intersection sleep append sum submit_job format no_duplicate set choice OPTIMAL nlp info float keys uuid4 BORED extend LEARNED sub unicode insert update list find append enumerate punctuation replace strip search lower sub netloc replace capitalize choice sub findall glob learn Kernel saveBrain Kernel respond get_priority loadBrain floatX items sharedX sqr get_value sqrt append add_argument ArgumentParser list model_compute_encoding reverse_utterances eos_sym zeros range len prototype_state basicConfig len compute_encodings ceil DialogEncoderDecoder append range debug build_encoder_function readlines bs float words_to_indices load int enumerate isfile split info isfile 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 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 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 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 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 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 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_book_SkipThought_NormOp prototype_book_SkipThought_NormOp prototype_book_SkipThought_NormOp prototype_book_SkipThought_NormOp prototype_book_SkipThought_NormOp prototype_book_SkipThought_NormOp prototype_book_SkipThought_NormOp prototype_book_SkipThought_NormOp prototype_book_SkipThought_NormOp prototype_state prototype_reddit_SkipThought_NormOp prototype_reddit_SkipThought_NormOp prototype_reddit_SkipThought_NormOp prototype_reddit_SkipThought_NormOp prototype_reddit_SkipThought_NormOp prototype_reddit_SkipThought_NormOp prototype_reddit_SkipThought_NormOp prototype_reddit_SkipThought_NormOp prototype_state prototype_state prototype_state 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 get join list deepcopy initialize_features choice info sample randint range append enumerate int extend items update word_tokenize add_word Vocabulary Counter build_data vocab_threshold add_argument map dumps valid_proportion test_proportion ArgumentParser info parse_args number_examples build_vocab word_tokenize map items strip lemmatize int items replace strip lemmatize _get_user_response len MongoClient sub rllchat_mturk find append items append str time lemmatize oversample map stack filter warning zip long enumerate DataLoader ConversationDataset default_params default_params default_params default_params info iteritems info append enumerate len warning str list iteritems tolist map unicode intersection append sum choice set nlp info float was_it_bored index extend rank_candidate_responses argmax iteritems sort min map choice sum range len argmax iteritems sort min map choice sum range len recallat1_contextlen get_test_data xlabel regroup_chats close ylabel bar title array savefig legend xticks test_individually get_recall | # NIPS Challenge Docker container This repository contains the Dockerfile and setup code to run chat bot in docker instance. ## Live Deployment Telegram bot **@conv_test**. Make sure to have an username registered in Telegram, and then start conversation with `\begin`. ## File description - **bot.py** : Main entry point of the chat bot, message selection logic can be implemented here. - **models/** : Folder where model code is stored - **data/** : Folder where data files are stored - **config.py** : Configuration script, which has the location of data files as well as bot tokens. Replace the bot token with your ones to test. - **models/wrappper.py** - Wrapper function which calls the models. Must implement `get_response` def. | 790 |
NicolasZucchet/Photorealistic-Style-Transfer | ['style transfer', 'semantic segmentation'] | ['A Neural Algorithm of Artistic Style', 'Deep Photo Style Transfer', 'Automated Deep Photo Style Transfer'] | toolbox/__init__.py models/model.py metrics/metrics.py toolbox/optimizers.py args.py metrics/metrics_listener.py models/__init__.py toolbox/compute_content_losses.py models/base_models.py metrics/__init__.py toolbox/google_image_scrapper.py main.py toolbox/segmentation.py models/losses.py models/closed_form_matting.py toolbox/image_preprocessing.py install.py toolbox/path_setup.py toolbox/experiment.py toolbox/plotter.py toolbox/parameters.py parse_args main create_experience run_experience save_meters AverageMeter make_meters SumMeter ValueMeter Empty_listener Listener init_listener get_listener QuickModel Normalization get_base_model compute_laplacian _rolling_block closed_form_matting_with_trimap closed_form_matting_with_prior closed_form_matting_with_scribbles main AugmentedStyleLoss StyleLoss gram_matrix ExperimentLosses ContentLoss NeuralStyle get_model_and_losses ContentLoss main Calculator Experiment main parse_args GoogleImageScrapper get_all_masks image_to_tensor image_loader get_masks plt_images masks_loader is_nonzero tensor_to_image masks_to_tensor extract_masks resize_masks EmptyScheduler get_optim_parameters Adjust_lr get_optimizer_scheduler Experiment_parameters string_images rgb_to_hexa get_path_images create_path download_models prepare_experiment download_image save_plot save_output_ merge_mask get_id_from_RGBcolor get_segmentation get_all_topics plot_segmented_images Counter get_id_from_HEXcolor generate_segmentation_cli save_segmentation generate_plots save_images get_experiment_parameters get_experiment save_all configure_logger emptyLogger get_optimizer __delattr__ format res_dir content_image ghost add_argument exit rmtree work_dir ArgumentParser quick save_name style_image imsize makedirs no_metrics format configure_logger get_experiment_parameters res_dir getLogger content_image get_optimizer now get_experiment disp info get_listener parse_args emptyLogger get_model_and_losses save_name clamp_ step str epoch generate_plots save_model save_images res_dir print content_image plt_images run_experience input_image create_experience clamp_ save_all style_image info print items value Empty_listener Listener make_meters add_meters strides _rolling_block reshape inv astype ravel mean shape coo_matrix eye bool sum einsum minimum compute_laplacian reshape maximum shape info spsolve ravel diags imwrite image closed_form_matting_with_scribbles ArgumentParser trimap basicConfig solve_fg exit IMREAD_COLOR solve_foreground_background parse_args imread concatenate IMREAD_GRAYSCALE closed_form_matting_with_trimap error add_argument output print_help scribbles t size mm view children getLogger Sequential device ReLU reg_weight style_weight StyleLoss to range detach add_content_loss format base_model add_module eval info ContentLoss isinstance Conv2d add_style_loss ExperimentLosses BatchNorm2d content_weight len Calculator distance_matrix searchterm g GoogleImageScrapper append array open get_all_masks get_masks resize_masks masks_to_tensor unsqueeze Compose open clear subplot show close tight_layout tensor_to_image imshow title figure parameters ExponentialLR StepLR getLogger EmptyScheduler Adjust_lr Adam SGD RMSprop LBFGS ReduceLROnPlateau info mkdir print download create_path create_path download get_path_images range len int set_major_locator format subplots set_title plot join name res_dir set_xlabel copy MultipleLocator array savefig logged float max deepcopy res_dir content_image _imsave array string_images prepare_experiment print imread imsave subplot imshow figure imread enumerate len all range len all range len rgb_to_hexa Counter add get_id_from_HEXcolor top range len get_id_from_RGBcolor size squeeze clone zeros numpy range enumerate print zeros to numpy range basicConfig save_model_path save_experiment_path save save_listener_path clear show save_plot subplots reshape close subplot tight_layout tensor_to_image imshow title savefig figure | # Photorealistic-Style-Transfer Amaury Sudrie, Victor Ruelle, Nicolas Zucchet for course project (MAP583, École polytechnique, France) ## Objective Based on the https://github.com/ray075hl/DeepPhotoStyle_pytorch implementation of the Deep Photo Style Transfer paper (Luan et. al), we aim to explore new applications and modifications of deep photo styletransfer. To perform photo style transfer, semantic segmentation is used. The quality of the transfer then depends on the style photo. We are mainly concerned with how to find a good style photo with respect to user style will and we aim to automate the whole process. ## Pipeline The pipeline we use is `new-pipeline.ipynb`. Be sure to run `python install.py` before, in order to both install dependencies and download the segmentation model. ## Credits Papers we took inspiration from: - Deep Photo Style Transfer https://arxiv.org/abs/1703.07511 - Neural Style Transfer https://arxiv.org/abs/1508.06576 | 791 |
Nikolay1998/relaynet_pytorch | ['semantic segmentation'] | ['ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network'] | build/lib/relaynet_pytorch/net_api/sub_module.py train.py build/lib/relaynet_pytorch/relay_net.py build/lib/relaynet_pytorch/data_utils.py networks/net_api/losses.py build/lib/relaynet_pytorch/net_api/losses.py networks/net_api/sub_module.py setup.py networks/data_utils.py build/lib/relaynet_pytorch/solver.py networks/relay_net.py main.py networks/solver.py label_img_to_rgb ImdbData get_imdb_data ReLayNet per_class_dice create_exp_directory Solver CrossEntropyLoss2d dice_coeff DiceLoss CombinedLoss DiceCoeff DecoderBlock EncoderBlock BasicBlock ClassifierBlock ImdbData get_imdb_data ReLayNet per_class_dice create_exp_directory Solver CrossEntropyLoss2d dice_coeff DiceLoss CombinedLoss DiceCoeff DecoderBlock EncoderBlock BasicBlock ClassifierBlock squeeze transpose unique list asarray reshape squeeze File shape tile sum numpy range matmul makedirs Variable zero_ zip forward is_cuda enumerate astype float32 loadmat | # relaynet_pytorch PyTorch Implementation of ReLayNet. There are still some bugs and issues in the code, we are working on fixing them. Coded by Abhijit Guha Roy and Shayan Siddiqui (https://github.com/shayansiddiqui) If you use this code for any academic purpose, please cite: A. Guha Roy, S. Conjeti, S.P.K.Karri, D.Sheet, A.Katouzian, C.Wachinger, and N.Navab, "ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks," Biomed. Opt. Express 8, 3627-3642 (2017) Link: https://arxiv.org/abs/1704.02161 Enjoy!! :) | 792 |
NingMiao/CGMH | ['text generation'] | ['CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling'] | key_gen/utils.py paraphrase/paraphrase.py paraphrase/reader.py paraphrase/config.py correction/config.py key_gen/reader.py utils/dict_emb/emb_make.py correction/correction.py correction/reader.py correction/utils.py utils/dict_emb/dict_use.py paraphrase/utils.py utils/dict_emb/dict_make.py key_gen/key_gen.py key_gen/config.py config main data_type run_epoch PTBModel read_data_use dataset read_data array_data sigma_word reverse_seq just_acc similarity_word sentence_embedding cut_from_point similarity_batch_skipthoughts similarity_batch_word sample_from_candidate choose_action generate_change_candidate normalize keyword_pos2sta_vec sen2mat sen2sta_vec write_log generate_candidate_input sigma_skipthoughts similarity_skipthoughts config main data_type run_epoch PTBModel read_data array_data read_data_use choose_key dataset sen2mat sigma_word generate_candidate_input choose_action cut_from_point sigma_skipthoughts similarity_batch_skipthoughts reverse_seq similarity_skipthoughts similarity_batch_word just_acc sample_from_candidate write_log normalize keyword_pos2sta_vec similarity_word sentence_embedding config main data_type run_epoch PTBModel read_data_use dataset read_data array_data sen2mat sigma_word generate_candidate_input choose_action cut_from_point sigma_skipthoughts similarity_batch_skipthoughts reverse_seq similarity_skipthoughts similarity_batch_word just_acc sample_from_candidate write_log normalize keyword_pos2sta_vec similarity_word sentence_embedding dict_use _output_prob _cost run str trainable_variables exists record_time system use_log_path Saver use_output_path global_variables_initializer backward_log_path forward_log_path astype shuffle int32 append array range len load array_data open zeros dict_size range zeros dict_size range num_steps dict_size array range len random range array encode join sum id2sen join encode id2sen append sum append random array sen2mat T norm dot mean max diag append range len list strip len extend run zeros keyword_pos2sta_vec range split singular suggest plural extend present past infinitive append present_participle past_particip random ConfigProto load len array_data open sort shuffle append range len copy min astype float32 | # Constrained Sentence Generation via Metropolis-Hastings Sampling ## Introduction ## CGMH is a sampling based model for constrained sentence generation, which can be used in keyword-to-sentence generation, paraphrase, sentence correction and many other tasks. ## Examples ## - Running example for parahrase: (All rejected proposal is omitted) what movie do you like most . -> which movie do you like most . (`replace` what `with` which) -> which movie do you like . (`delete` most) -> which movie do you like best . (`insert` best) -> which movie do you think best . (`replace` like `with` think) -> | 793 |
NingMiao/KerBS | ['text generation'] | ['Kernelized Bayesian Softmax for Text Generation'] | KerBS_hooks.py KerBS_top.py misc.py reallocate.py HvdReallocateHook kerbs_top theta_logit advanced_softmax advanced_add_to_collections debug_print advanced_get_collection get_new_sense_allocate reduce_max exp ndims reduce_sum dtype norm exp less_equal float32 expand_dims cast less saturate_cast abs tensordot float16 saturate_cast log SparseTensor get_variable list ones transpose matmul shape int64 theta_logit advanced_softmax expand_dims range advanced_add_to_collections astype constant reshape float32 extend len print info add_to_collections dict get_collection zip format argsort info zeros range len | # Kernelized Bayesian Softmax for Text Generation (KerBS) KerBS is a powerful substitute for Softmax. Please refer to our [paper](https://arxiv.org/abs/1911.00274) or [poster](https://github.com/NingMiao/KerBS/blob/master/poster/poster_KerBS.pdf) for details. ## Requirements - python - `==3.4` - python packages - TensorFlow `== 1.4.0` (Other versions are not tested.) - numpy - pickle - horovod (Running without horovod needs some slight modifications.) | 794 |
NirantK/Hinglish | ['sentiment analysis'] | ['HinglishNLP: Fine-tuned Language Models for Hinglish Sentiment Detection'] | train.py run_language_modeling.py hinglish.py nb-stripout.py hinglishutils.py HinglishTrainer modify_transformer_config save_model load_lm_model flat_prf format_time evaluate_data_for_one_epochs evaulate_and_save_prediction_results flat_accuracy set_seed add_padding create_attention_masks tokenize_the_sentences get_preds_from_model prep_input check_for_gpu get_files_from_gdrive make_dataloaders run_valid load_sentences_and_labels train_model load_masks_and_inputs print_confusion_matrix clean clean_nb clean_cell_outputs clean_cell_docs clean_cell_code TextDataset set_seed evaluate LineByLineTextDataset train _sorted_checkpoints mask_tokens main _rotate_checkpoints load_and_cache_examples train get_ticklabels xlabel ylabel figure set_ticklabels DataFrame heatmap download extractall open apply flatten flatten int round from_json_file fit read_json transform LabelEncoder read_json tolist to_csv flatten DataLoader eval TensorDataset get_preds_from_model prep_input save inverse_transform SequentialSampler DataFrame append tuple numpy print pad_sequences encode tensor append seed manual_seed_all manual_seed DataLoader TensorDataset SequentialSampler RandomSampler tensor train_test_split append pad_sequences print from_pretrained encode append save_pretrained to_csv save makedirs from_pretrained list cuda named_parameters time model backward print train clip_grad_norm_ zero_grad range parameters run_valid append to step log enumerate len evaluate_data_for_one_epochs eval time log tuple numpy flat_accuracy flat_prf clean_cell_outputs line_by_line join sorted format glob match output_dir append format save_total_limit rmtree _sorted_checkpoints info max len pad_token_id bool mask_token convert_tokens_to_ids clone randint shape masked_fill_ eq tensor mlm_probability full len gradient_accumulation_steps resize_token_embeddings get_linear_schedule_with_warmup clip_grad_norm_ zero_grad DataLoader DataParallel DistributedDataParallel max_grad_norm output_dir device save max initialize set_seed logging_steps load_state_dict master_params to state_dict SummaryWriter format _rotate_checkpoints 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 makedirs tqdm parameters step train_batch_size len DataLoader DataParallel output_dir device tensor max eval_batch_size exp per_gpu_eval_batch_size to SequentialSampler format eval info join n_gpu makedirs tqdm load_and_cache_examples len enable_attach from_pretrained config_name should_continue block_size warning ArgumentParser device do_train output_dir save eval_all_checkpoints setLevel basicConfig model_class set_seed list set_device device_count _sorted_checkpoints parse_args to WARN config_class update init_process_group tokenizer_name save_pretrained info fp16 wait_for_attach train join n_gpu evaluate model_name_or_path print add_argument min barrier max_len dict bool load_and_cache_examples local_rank makedirs HinglishTrainer | # Hinglish Tools and Data [Data and Model files](https://drive.google.com/drive/folders/12qEbxbefBY24-YqahVV0v7q_IFyxz3L8?usp=sharing)  Approach | LM Perplexity | Classifier F1 | ---| --- | ---| BERT|8.2 | 0.63| DistilBERT|6.5 | 0.63| ULMFIT | 21 | 0.61| RoBERTa| 7.54 | 0.64| | 795 |
NoMorningstar/style-transfer-1 | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | demo.py style.py st_api main gpu_count init _compute_reprs _compute_style_grad StyleTransfer style_optfn main _compute_content_grad check_output update range StyleTransfer items list transfer_style acquire sleep get_generated release style_img st_api content_img waitKey destroyWindow COLOR_RGB2BGR imshow init load_image cvtColor sum sgemm sum T copy set sgemm forward list _compute_reprs backward reshape float64 _compute_style_grad astype reversed _compute_content_grad keys enumerate model gpu_id StyleTransfer basicConfig set_device default_timer img_as_ubyte imsave format lower transfer_style info set_mode_cpu output set_mode_gpu get_generated | # style-transfer ## Introduction This repository contains a pyCaffe-based implementation of "A Neural Algorithm of Artistic Style" by L. Gatys, A. Ecker, and M. Bethge, which presents a method for transferring the artistic style of one input image onto another. You can read the paper here: http://arxiv.org/abs/1508.06576. Neural net operations are handled by Caffe, while loss minimization and other miscellaneous matrix operations are performed using numpy and scipy. L-BFGS is used for minimization. ## Requirements - Python >= 2.7 - CUDA >= 6.5 (highly recommended) - Caffe CUDA will enable GPU-based computation in Caffe. ## Download | 796 |
NoSyu/SSREM | ['response generation'] | ['Speaker Sensitive Response Evaluation Model'] | src/models/__init__.py src/utils/__init__.py src/solvers/__init__.py src/utils/load_save.py src/configs.py src/solvers/convscorenegv1_solver.py src/utils/vocab.py src/solvers/convscore_solver.py src/train.py src/solvers/solver.py src/models/ssrem.py src/utils/convert.py src/utils/data_loader.py src/utils/tensorboard.py src/eval1.py src/retrain.py Config get_config str2bool load_pickle load_pickle load_pickle ConvScoreSSREM SolverConvScoreSSREM SolverConvScore Solver pad to_var to_tensor pad_and_pack UttersDataset get_loader2 get_loader load_pickle TensorboardWriter Vocab update add_argument ArgumentParser vars parse_args cuda is_available cpu isinstance stack max UttersDataset DataLoader UttersDataset DataLoader | # Speaker Sensitive Response Evaluation Model ## Overview This repo provides the implementation of Speaker Sensitive Response Evaluation Model (SSREM). ## Tested Environment - Python 3.6.3 - Pytorch 1.3 ## How to run In `src` folder, we make bash script file to train and evaluate SSREM. All arguments for the bash files are passed into argparse in `configs.py`. - `Run_train.sh`: a bash script file to train SSREM | 797 |
NoemieJaquier/GaBOtorch | ['gaussian processes'] | ['High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds'] | BoManifolds/BO_test_functions/nested_test_functions_sphere.py BoManifolds/pymanopt_addons/problem.py BoManifolds/pymanopt_addons/tools/multi.py examples/bo_sphere/constrained_benchmark_examples/gabo_sphere_inequality_constraints.py examples/kernels/sphere/sphere_kernels.py BoManifolds/nested_mappings/nested_spd_optimization.py examples/kernels/sphere/sphere_gaussian_kernel_parameters.py examples/bo_sphere/benchmark_examples/bo_euclidean_sphere.py examples/kernels/spd/spd_kernels.py BoManifolds/pymanopt_addons/tools/autodiff/__init__.py BoManifolds/pymanopt_addons/tools/testing.py examples/kernels/spd/spd_gaussian_kernel_parameters.py BoManifolds/BO_test_functions/test_functions_spd.py examples/hd_bo_sphere/benchmark_examples/hd_gabo_sphere.py BoManifolds/Riemannian_utils/spd_utils.py BoManifolds/Riemannian_utils/utils.py examples/bo_spd/benchmark_examples/gabo_spd.py BoManifolds/nested_mappings/nested_spheres_optimization.py BoManifolds/Riemannian_utils/sphere_utils_torch.py examples/bo_sphere/constrained_benchmark_examples/gabo_sphere_equality_constraints.py BoManifolds/manifold_optimization/augmented_Lagrange_method.py BoManifolds/kernel_utils/kernels_sphere.py BoManifolds/manifold_optimization/manifold_gp_fit.py BoManifolds/kernel_utils/kernels_nested_sphere.py BoManifolds/nested_mappings/nested_spd_utils.py examples/bo_spd/benchmark_examples/bo_euclidean_spd.py BoManifolds/Riemannian_utils/sphere_constraint_utils.py BoManifolds/kernel_utils/kernels_spd.py BoManifolds/Riemannian_utils/spd_utils_torch.py BoManifolds/pymanopt_addons/tools/autodiff/_backend.py BoManifolds/Riemannian_utils/sphere_utils.py BoManifolds/manifold_optimization/constrained_trust_regions.py BoManifolds/plot_utils/bo_plots.py BoManifolds/Riemannian_utils/spd_constraints_utils.py examples/bo_sphere/constrained_benchmark_examples/gabo_sphere_bound_constraints.py BoManifolds/pymanopt_addons/tools/__init__.py examples/bo_sphere/benchmark_examples/gabo_sphere.py BoManifolds/BO_test_functions/nested_test_functions_spd.py examples/bo_spd/benchmark_examples/bo_cholesky_spd.py BoManifolds/manifold_optimization/approximate_hessian.py BoManifolds/manifold_optimization/robust_trust_regions.py examples/hd_bo_spd/benchmark_examples/hd_gabo_spd.py BoManifolds/plot_utils/manifolds_plots.py BoManifolds/pymanopt_addons/tools/autodiff/_autograd.py BoManifolds/Riemannian_utils/sphere_constraints_utils_torch.py BoManifolds/plot_utils/shapes_plots.py BoManifolds/kernel_utils/kernels_nested_spd.py BoManifolds/manifold_optimization/numpy_list_converter.py BoManifolds/euclidean_optimization/euclidean_constrained_optimize.py BoManifolds/manifold_optimization/manifold_optimize.py setup.py BoManifolds/pymanopt_addons/tools/autodiff/_pytorch.py BoManifolds/Riemannian_utils/spd_constraints_utils_torch.py BoManifolds/BO_test_functions/test_functions_sphere.py BoManifolds/nested_mappings/nested_spheres_utils.py BoManifolds/pymanopt_addons/tools/autodiff/_tensorflow.py BoManifolds/pymanopt_addons/tools/autodiff/_theano.py BoManifolds/nested_mappings/nested_spd_constraints_utils.py projected_function_spd cholesky_embedded_function_wrapped optimum_projected_function_spd optimum_nested_function_sphere nested_function_sphere product_of_sines_function_spd optimum_ackley_spd optimum_product_of_sines_spd rosenbrock_function_spd get_rosenbrock_base optimum_bimodal_spd cholesky_function_wrapped ackley_function_spd get_bimodal_parameters get_product_of_sines_base bimodal_function_spd optimum_rosenbrock_spd get_ackley_base optimum_rosenbrock_sphere product_of_sines_function_sphere bimodal_function_sphere optimum_ackley_sphere rosenbrock_function_sphere get_bimodal_parameters ackley_function_sphere optimum_bimodal_sphere optimum_product_of_sines_sphere gen_batch_initial_conditions gen_candidates_scipy joint_optimize NestedSpdAffineInvariantGaussianKernel NestedSpdLogEuclideanGaussianKernel NestedSphereGaussianKernel SpdLogEuclideanGaussianKernel SpdAffineInvariantGaussianKernel SpdAffineInvariantLaplaceKernel SpdFrobeniusGaussianKernel SphereGaussianKernel SphereLaplaceKernel get_hessianfd AugmentedLagrangeMethod ConstrainedTrustRegions StrictConstrainedTrustRegions OptimizationIteration fit_gpytorch_manifold joint_optimize_manifold gen_batch_initial_conditions_manifold gen_candidates_manifold set_params_with_list_of_array module_to_list_of_array TrustRegions max_eigenvalue_nested_spd_constraint min_eigenvalue_nested_spd_constraint random_nested_spd_with_spd_eigenvalue_constraints optimize_reconstruction_parameters_nested_spd min_log_euclidean_distance_reconstruction_cost min_affine_invariant_distance_reconstruction_cost projection_from_nested_spd_to_spd projection_from_spd_to_nested_spd optimize_reconstruction_parameters_nested_sphere min_error_reconstruction_cost projection_from_sphere_to_next_subsphere projection_from_subsphere_to_sphere projection_from_sphere_to_subsphere projection_from_subsphere_to_next_sphere projection_from_sphere_to_nested_sphere bo_plot_function_sphere bo_plot_acquisition_sphere bo_plot_function_sphere_planar bo_plot_gp_spd_planar bo_plot_gp_spd bo_plot_gp_sphere_planar bo_plot_gp_sphere bo_plot_acquisition_spd bo_plot_function_spd plot_gaussian_on_sphere plot_sphere_tangent_plane plot_sphere plot_spd_cone plot_plane plot_ellipse3d Problem multiexp multitransp multiprod multisym multieye multiskew multilog rgrad egrad2rgrad ehess2rhess rhess make_enum ndarraySequenceMixin _hessian_vector_product AutogradBackend assert_backend_available Backend PytorchBackend TensorflowBackend TheanoBackend min_eigenvalue_constraint max_eigenvalue_constraint_cholesky min_eigenvalue_constraint_cholesky max_eigenvalue_constraint post_processing_init_spd_torch post_processing_spd_cholesky_torch max_eigenvalue_constraint_torch min_eigenvalue_constraint_torch expmap project_to_eigenvalue_domain in_domain_eig vector_to_symmetric_matrix_mandel tensor_matrix_product logmap expmap_mandel_vector affine_invariant_distance mean mean_mandel_vector parallel_transport_operator parallel_transport_operator_mandel_vector in_domain spd_sample logmap_mandel_vector symmetric_matrix_to_vector_mandel affine_invariant_distance_torch logm_torch symmetric_matrix_to_vector_mandel_torch vector_to_symmetric_matrix_mandel_torch sqrtm_torch frobenius_distance_torch post_processing_init_sphere_torch norm_one_constraint expmap logmap get_axisangle parallel_transport_operator sphere_distance rotation_from_sphere_points karcher_mean_sphere rotation_from_sphere_points_torch sphere_distance_torch vector_to_skew_matrix rotation_matrix_from_axis_angle post_processing_init fxl sample_sphere_constrained fyu fzl fzu fyl sample_sphere_constrained y_great_circle domain_constraint sample_sphere_constrained align_y_axis plot_training_test_data_spd_cone plot_gaussian_process align_y_axis plot_gaussian_process_prediction test_function symmetric_matrix_to_vector_mandel_torch vector_to_symmetric_matrix_mandel_torch projection_from_spd_to_nested_spd optimum_function count_nonzero dtype T tril_indices size dot _n zeros to numpy symmetric_matrix_to_vector_mandel tensor numpy optimum_function int dtype exp sum vector_to_symmetric_matrix_mandel pi symmetric_matrix_to_vector_mandel sqrt _n numpy log get_ackley_base numpy get_ackley_base int dtype vector_to_symmetric_matrix_mandel get_rosenbrock_base range _n numpy log symmetric_matrix_to_vector_mandel exp ones get_rosenbrock_base _n numpy int dtype det vector_to_symmetric_matrix_mandel inv pi sqrt _n get_bimodal_parameters numpy log symmetric_matrix_to_vector_mandel get_bimodal_parameters numpy ones int eye _n int dtype vector_to_symmetric_matrix_mandel sin _n get_product_of_sines_base numpy prod log symmetric_matrix_to_vector_mandel exp ones pi _n get_product_of_sines_base numpy count_nonzero dtype T tril_indices size dot _n zeros to numpy symmetric_matrix_to_vector_mandel dtype sum exp pi sqrt zeros numpy zeros numpy dtype numpy zeros sum ones zeros exp numpy dtype det inv pi sqrt get_bimodal_parameters numpy log get_bimodal_parameters numpy zeros sqrt dtype sin zeros numpy prod exp ones pi zeros numpy get gen_candidates_scipy min append gen_batch_initial_conditions cat minimize Bounds fix_features requires_grad_ shape acquisition_function zeros numpy range get warn norm transp grad retr range len range range Product int time module_to_array_func Problem min module_from_array_func warn named_parameters MethodType append range get gen_batch_initial_conditions_manifold min gen_candidates_manifold append cat Problem pre_processing_manifold solve MethodType requires_grad_ shape acquisition_function post_processing_manifold zeros tensor range get warn update get is_tensor hasattr named_parameters_and_constraints named_parameters OrderedDict full nelement append numpy TorchAttr detach items view named_parameters OrderedDict requires_grad_ copy_ tensor range eigenvalues projection_from_nested_spd_to_spd eigenvalues projection_from_nested_spd_to_spd tensor random_spd_fct projection_from_spd_to_nested_spd affine_invariant_distance_torch unsqueeze projection_from_nested_spd_to_spd zeros range unsqueeze projection_from_nested_spd_to_spd zeros frobenius_distance_torch range Product Problem transform view min solve from_numpy Interval AugmentedLagrangeMethod append bmm list view transpose shape unsqueeze repeat dtype T sqrtm_torch unsqueeze zeros mm range cat sphere_distance_torch Product Problem min solve pi Interval append list T ones_like view zeros_like sphere_distance_torch shape rotation_from_sphere_points_torch unsqueeze repeat sin append list T ones_like view zeros_like shape rotation_from_sphere_points_torch unsqueeze repeat sin append projection_from_sphere_to_nested_sphere append projection_from_sphere_to_next_subsphere range len T zeros_like ones cos rotation_from_sphere_points_torch unsqueeze append range projection_from_subsphere_to_next_sphere len cos pi linspace max ones inferno shape scatter sin range set_pane_color view_init size set_xlim set_zlim min outer zeros plot_surface numpy Tensor set_ylim cos add_subplot pi linspace tick_params max ones set_xlabel inferno shape scatter sin range set_pane_color size set_zlabel min locator_params outer set_ylabel zeros plot_surface numpy Tensor cos pi linspace max ones inferno shape scatter sin double range set_pane_color view_init size set_xlim set_zlim min outer zeros plot_surface numpy set_ylim cos pi linspace tick_params max ones set_xlabel inferno shape scatter sin double range set_pane_color view_init size set_xlim set_zlim set_zlabel min locator_params outer set_ylabel zeros plot_surface numpy set_ylim model cos add_subplot pi linspace tick_params max ones set_xlabel inferno shape scatter sin double range set_pane_color size set_zlabel min locator_params outer set_ylabel zeros plot_surface numpy pi vstack linspace tensor max tril_indices ones inferno scatter rotation_matrix_from_axis_angle double range set_pane_color view_init min plot_spd_cone cross dot cholesky zeros plot_surface numpy array pi vstack linspace tensor max tril_indices ones inferno shape scatter rotation_matrix_from_axis_angle double range set_pane_color view_init sqrt min plot_spd_cone cross dot cholesky zeros plot_surface numpy array model pi vstack linspace max tril_indices ones inferno shape scatter rotation_matrix_from_axis_angle double range set_pane_color view_init sqrt min plot_spd_cone cross dot cholesky zeros plot_surface numpy array print ones size set_xlim cos outer pi set_zlim linspace sin plot_surface set_ylim T plot reshape get_axisangle dot rotation_matrix_from_axis_angle plot_surface array expmap plot pi dot sqrtm vstack linspace real plot set_xlim pi cross dot set_zlim vstack linspace rotation_matrix_from_axis_angle plot_surface array set_ylim T ones reshape size eig cos outer pi dot stack linspace real sin plot_surface T plot reshape get_axisangle dot rotation_matrix_from_axis_angle plot_surface array eigh expand_dims log eigh expand_dims exp jacobian grad eigvals vector_to_symmetric_matrix_mandel eigvals vector_to_symmetric_matrix_mandel int T tril_indices dot eigvals zeros int T tril_indices dot eigvals zeros eigenvalues eigenvalues symeig view vector_to_symmetric_matrix_mandel_torch eigenvalues range shape inverse mm diag eigenvectors int list T tril_indices view eig range shape inverse zeros mm diag eigenvectors remove reshape transpose dot shape range len range diagonal copy concatenate int cumsum diag copy range dot eig solve inv dot eig solve inv vector_to_symmetric_matrix_mandel vector_to_symmetric_matrix_mandel dot eig inv vector_to_symmetric_matrix_mandel range expmap logmap logmap_mandel_vector range expmap_mandel_vector T randn ones rand max_eig dot _n qr min_eig diag eigvals eig symeig eigenvalues log eigenvectors symeig sqrt eigenvalues eigenvectors bmm view transpose eigenvalues log unsqueeze inverse cholesky append zeros sum range cat append unsqueeze cat int list view cumsum shape append zeros range diag list view shape append range diag cat sqrt sum cos sin minimum T arccos cos maximum sin zeros dot T min max T cos logmap dot sqrt eye sin sum mean range expmap logmap sqrt array T norm arccos min dot eye sin max view clamp transpose squeeze unsqueeze cat norm T clamp unsqueeze eye sin mm acos vector_to_skew_matrix norm array sqrt arange randn dtype ones mm min Tensor type max rand cos dot rotation_from_sphere_points sin zeros transform inverted get_ylim set_ylim Axes3D plot set_pane_color view_init sqrt title plot_spd_cone plot sqrt title figure fill_between array range det inv array logmap Axes3D set_pane_color view_init title scatter range plot_sphere | # GaBOtorch This repository contains the source code to perform Geometry-aware Bayesian Optimization (GaBO) and High-Dimensional Geometry-aware Bayesian Optimization (HD-GaBO) on Riemannian manifolds. # Dependencies This code runs with Python>=3.6. It requires the following packages: - numpy - scipy - matplotlib - pymanopt - torch - gpytorch | 798 |
Non-biri/ml-agents-0.9.3 | ['unity'] | ['Unity: A General Platform for Intelligent Agents'] | ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_input_pb2.py ml-agents/mlagents/trainers/components/reward_signals/curiosity/model.py ml-agents-envs/mlagents/envs/communicator_objects/unity_to_external_pb2.py ml-agents/mlagents/trainers/components/reward_signals/reward_signal.py gym-unity/gym_unity/envs/__init__.py ml-agents/mlagents/trainers/learn.py ml-agents-envs/mlagents/envs/communicator_objects/custom_observation_pb2.py ml-agents/mlagents/trainers/meta_curriculum.py ml-agents/mlagents/trainers/tests/test_barracuda_converter.py ml-agents/mlagents/trainers/ppo/models.py gym-unity/gym_unity/__init__.py 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-envs/mlagents/envs/communicator.py ml-agents-envs/mlagents/envs/communicator_objects/custom_reset_parameters_pb2.py ml-agents/mlagents/trainers/tests/test_ppo.py ml-agents-envs/mlagents/envs/tests/test_rpc_communicator.py ml-agents/mlagents/trainers/components/reward_signals/__init__.py ml-agents-envs/setup.py ml-agents/mlagents/trainers/tests/mock_brain.py ml-agents-envs/mlagents/envs/action_info.py ml-agents-envs/mlagents/envs/rpc_communicator.py ml-agents/mlagents/trainers/tests/test_bcmodule.py ml-agents/mlagents/trainers/tests/test_trainer_controller.py ml-agents/mlagents/trainers/components/reward_signals/reward_signal_factory.py ml-agents/setup.py ml-agents/mlagents/trainers/barracuda.py ml-agents-envs/mlagents/envs/tests/test_envs.py ml-agents-envs/mlagents/envs/env_manager.py ml-agents/mlagents/trainers/ppo/trainer.py ml-agents-envs/mlagents/envs/tests/test_timers.py ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_output_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_initialization_output_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/unity_input_pb2.py ml-agents/mlagents/trainers/tests/test_meta_curriculum.py ml-agents/mlagents/trainers/components/reward_signals/curiosity/signal.py ml-agents-envs/mlagents/envs/subprocess_env_manager.py ml-agents/mlagents/trainers/bc/trainer.py ml-agents/mlagents/trainers/components/reward_signals/curiosity/__init__.py ml-agents/mlagents/trainers/curriculum.py ml-agents-envs/mlagents/envs/communicator_objects/agent_action_proto_pb2.py ml-agents/mlagents/trainers/tests/test_policy.py ml-agents/mlagents/trainers/ppo/policy.py ml-agents-envs/mlagents/envs/communicator_objects/space_type_proto_pb2.py ml-agents/mlagents/trainers/tests/test_learn.py ml-agents-envs/mlagents/envs/communicator_objects/brain_parameters_proto_pb2.py ml-agents/mlagents/trainers/tests/test_demo_loader.py ml-agents/mlagents/trainers/components/bc/__init__.py ml-agents/mlagents/trainers/models.py ml-agents/mlagents/trainers/__init__.py ml-agents-envs/mlagents/envs/communicator_objects/agent_info_proto_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/environment_parameters_proto_pb2.py ml-agents/mlagents/trainers/tests/test_simple_rl.py ml-agents-envs/mlagents/envs/policy.py ml-agents/mlagents/trainers/exception.py gym-unity/gym_unity/tests/test_gym.py ml-agents/mlagents/trainers/components/reward_signals/extrinsic/__init__.py ml-agents/mlagents/trainers/buffer.py ml-agents/mlagents/trainers/bc/online_trainer.py ml-agents-envs/mlagents/envs/communicator_objects/engine_configuration_proto_pb2.py ml-agents/mlagents/trainers/ppo/__init__.py ml-agents/mlagents/trainers/tensorflow_to_barracuda.py ml-agents-envs/mlagents/envs/communicator_objects/unity_to_external_pb2_grpc.py ml-agents-envs/mlagents/envs/mock_communicator.py ml-agents/mlagents/trainers/tests/test_rl_trainer.py ml-agents-envs/mlagents/envs/timers.py gym-unity/setup.py ml-agents-envs/mlagents/envs/communicator_objects/unity_message_pb2.py ml-agents-envs/mlagents/envs/environment.py ml-agents-envs/mlagents/envs/communicator_objects/custom_action_pb2.py ml-agents/mlagents/trainers/bc/policy.py ml-agents-envs/mlagents/envs/simple_env_manager.py ml-agents-envs/mlagents/envs/base_unity_environment.py ml-agents/mlagents/trainers/bc/__init__.py ml-agents/mlagents/trainers/trainer_util.py ml-agents/mlagents/trainers/tests/test_trainer_util.py ml-agents-envs/mlagents/envs/communicator_objects/unity_output_pb2.py ml-agents/mlagents/trainers/components/reward_signals/extrinsic/signal.py ml-agents/mlagents/trainers/components/reward_signals/gail/__init__.py ml-agents-envs/mlagents/envs/sampler_class.py ml-agents-envs/mlagents/envs/exception.py gym-unity/gym_unity/envs/unity_env.py ml-agents/mlagents/trainers/components/reward_signals/gail/model.py ml-agents-envs/mlagents/envs/communicator_objects/header_pb2.py ml-agents/mlagents/trainers/rl_trainer.py ml-agents/mlagents/trainers/tests/test_reward_signals.py ml-agents-envs/mlagents/envs/brain.py ml-agents/mlagents/trainers/components/reward_signals/gail/signal.py ml-agents/mlagents/trainers/ppo/multi_gpu_policy.py ml-agents/mlagents/trainers/tests/test_multigpu.py ml-agents-envs/mlagents/envs/communicator_objects/demonstration_meta_proto_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/resolution_proto_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/__init__.py ml-agents/mlagents/trainers/demo_loader.py ml-agents-envs/mlagents/envs/__init__.py ml-agents/mlagents/trainers/components/bc/module.py ml-agents/mlagents/trainers/tests/test_trainer_metrics.py ml-agents-envs/mlagents/envs/tests/test_sampler_class.py ml-agents/mlagents/trainers/tests/test_buffer.py ml-agents-envs/mlagents/envs/communicator_objects/command_proto_pb2.py ml-agents/mlagents/trainers/trainer.py ml-agents-envs/mlagents/envs/socket_communicator.py ml-agents-envs/mlagents/envs/tests/test_subprocess_env_manager.py ml-agents/mlagents/trainers/bc/models.py ml-agents/mlagents/trainers/bc/offline_trainer.py ml-agents/mlagents/trainers/tf_policy.py ml-agents/mlagents/trainers/tests/test_bc.py ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_initialization_input_pb2.py ml-agents/mlagents/trainers/trainer_metrics.py UnityGymException ActionFlattener UnityEnv create_mock_vector_braininfo test_gym_wrapper test_multi_agent test_branched_flatten setup_mock_unityenvironment create_mock_brainparams BarracudaWriter fuse print_known_operations compress Build sort lstm write fuse_batchnorm_weights trim mean gru Model summary Struct parse_args to_json rnn BufferException Buffer Curriculum make_demo_buffer load_demonstration demo_to_buffer CurriculumError MetaCurriculumError TrainerError create_environment_factory create_sampler_manager run_training prepare_for_docker_run try_create_meta_curriculum main load_config MetaCurriculum EncoderType LearningModel AllRewardsOutput RLTrainer 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 TFPolicy UnityPolicyException UnityTrainerException Trainer TrainerController TrainerMetrics initialize_trainers BehavioralCloningModel OfflineBCTrainer OnlineBCTrainer BCPolicy BCTrainer BCModel BCModule RewardSignal create_reward_signal CuriosityModel CuriosityRewardSignal ExtrinsicRewardSignal GAILModel GAILRewardSignal PPOModel get_devices MultiGpuPPOPolicy PPOPolicy PPOTrainer get_gae discount_rewards create_buffer simulate_rollout create_mock_3dball_brain create_mock_banana_brain setup_mock_unityenvironment create_mock_braininfo create_mock_brainparams setup_mock_env_and_brains test_barracuda_converter test_bc_trainer_step test_bc_trainer_add_proc_experiences test_cc_bc_model test_dc_bc_model test_visual_cc_bc_model test_bc_trainer_end_episode test_bc_policy_evaluate dummy_config test_visual_dc_bc_model create_bc_trainer test_bcmodule_rnn_update test_bcmodule_update test_bcmodule_dc_visual_update dummy_config create_ppo_policy_with_bc_mock test_bcmodule_defaults test_bcmodule_rnn_dc_update test_buffer_sample construct_fake_buffer assert_array fakerandint test_buffer test_buffer_truncate location default_reset_parameters test_init_curriculum_bad_curriculum_raises_error test_init_curriculum_happy_path test_increment_lesson test_get_config test_load_demo test_load_demo_dir basic_options test_docker_target_path test_run_training test_init_meta_curriculum_happy_path test_increment_lessons_with_reward_buff_sizes default_reset_parameters MetaCurriculumTest test_increment_lessons measure_vals reward_buff_sizes test_set_all_curriculums_to_lesson_num test_get_config test_set_lesson_nums test_init_meta_curriculum_bad_curriculum_folder_raises_error more_reset_parameters test_create_model dummy_config test_average_gradients test_update basic_mock_brain test_take_action_returns_action_info_when_available basic_params test_take_action_returns_nones_on_missing_values test_take_action_returns_empty_with_no_agents test_trainer_increment_step test_rl_functions test_ppo_model_dc_vector_rnn test_ppo_model_cc_vector_rnn test_add_rewards_output test_ppo_policy_evaluate test_ppo_model_cc_visual dummy_config test_ppo_model_dc_vector test_ppo_model_dc_visual test_ppo_get_value_estimates test_ppo_model_cc_vector test_gail_dc_visual reward_signal_update reward_signal_eval test_extrinsic test_curiosity_cc test_gail_rnn test_gail_cc create_ppo_policy_mock test_curiosity_dc curiosity_dummy_config dummy_config test_curiosity_visual test_curiosity_rnn gail_dummy_config create_mock_all_brain_info create_rl_trainer dummy_config test_rl_trainer create_mock_brain create_mock_policy clamp test_simple_rl Simple1DEnvironment _check_environment_trains test_initialization_seed test_start_learning_trains_until_max_steps_then_saves basic_trainer_controller test_take_step_adds_experiences_to_trainer_and_trains dummy_config trainer_controller_with_take_step_mocks trainer_controller_with_start_learning_mocks test_start_learning_trains_forever_if_no_train_model TestTrainerMetrics test_initialize_online_bc_trainer test_initialize_ppo_trainer test_initialize_trainer_parameters_override_defaults dummy_offline_bc_config test_initialize_invalid_trainer_raises_exception dummy_bad_config dummy_config dummy_offline_bc_config_with_override dummy_online_bc_config ActionInfo BaseUnityEnvironment safe_concat_np_ndarray BrainInfo BrainParameters safe_concat_lists Communicator UnityEnvironment EnvManager StepInfo SamplerException UnityWorkerInUseException UnityException UnityCommunicationException UnityTimeOutException UnityEnvironmentException UnityActionException MockCommunicator Policy RpcCommunicator UnityToExternalServicerImplementation MultiRangeUniformSampler UniformSampler SamplerFactory SamplerManager GaussianSampler Sampler SimpleEnvManager SocketCommunicator worker EnvironmentResponse UnityEnvWorker StepResponse SubprocessEnvManager EnvironmentCommand TimerNode hierarchical_timer get_timer_root get_timer_tree reset_timers set_gauge timed GaugeNode TimerStack UnityToExternalServicer UnityToExternalStub add_UnityToExternalServicer_to_server test_initialization test_reset test_close test_step test_handles_bad_filename test_rpc_communicator_checks_port_on_create test_rpc_communicator_create_multiple_workers test_rpc_communicator_close 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 mock_env_factory SubprocessEnvManagerTest MockEnvWorker test_timers decorated_func main create_mock_vector_braininfo sample UnityEnv setup_mock_unityenvironment step create_mock_brainparams create_mock_vector_braininfo UnityEnv setup_mock_unityenvironment step create_mock_brainparams setup_mock_unityenvironment create_mock_vector_braininfo create_mock_brainparams UnityEnv Mock list Mock array range join isdir print replaceFilenameExtension add_argument exit verbose source_file ArgumentParser target_file sqrt topologicalSort list hasattr layers addEdge Graph print inputs set len list hasattr layers print filter match trim_model compile data layers print tensors float16 replace layers dumps layers isinstance print tensors inputs zip 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 Buffer reset_local_buffers number_visual_observations append_update_buffer append range enumerate make_demo_buffer load_demonstration join read suffix isdir endswith BrainParametersProto from_agent_proto DemonstrationMetaProto ParseFromString AgentInfoProto isfile append from_proto listdir _DecodeVarint32 start_learning int str format create_environment_factory create_sampler_manager initialize_trainers external_brains TrainerController put try_create_meta_curriculum reset_parameters load_config SubprocessEnvManager pop SamplerManager load_config list set_all_curriculums_to_lesson_num MetaCurriculum reset_parameters keys chmod format basename isdir glob copyfile copytree prepare_for_docker_run replace int Process join docopt getLogger print run_training start Queue info append randint setLevel range endswith len 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 list 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 format OfflineBCTrainer copy OnlineBCTrainer PPOTrainer get check_config rcls list_local_devices list zeros_like size reversed range append discount_rewards Mock list ones array range brain_name create_buffer brain sequence_length append range vector_action_space_size Buffer ones number_visual_observations append_update_buffer shape append sum range enumerate setup_mock_unityenvironment mock_env create_mock_braininfo create_mock_brainparams create_mock_brainparams create_mock_brainparams join remove _get_candidate_names convert _get_default_tempdir dirname abspath isfile next Mock BCTrainer simulate_rollout mock_env dirname abspath setup_mock_unityenvironment policy create_mock_braininfo create_mock_3dball_brain update_policy create_bc_trainer increment_step agents process_experiences step create_bc_trainer add_experiences end_episode agents process_experiences step create_bc_trainer add_experiences BCPolicy evaluate close reset MockCommunicator reset_default_graph UnityEnvironment reset_default_graph reset_default_graph reset_default_graph reset_default_graph mock_env dirname abspath PPOPolicy setup_mock_unityenvironment create_mock_braininfo create_ppo_policy_with_bc_mock close create_mock_3dball_brain update items list close create_ppo_policy_with_bc_mock create_mock_3dball_brain update items list close create_ppo_policy_with_bc_mock create_mock_3dball_brain update items list close create_mock_banana_brain create_ppo_policy_with_bc_mock update items list close create_mock_banana_brain create_ppo_policy_with_bc_mock flatten list range len append range Buffer get_batch construct_fake_buffer assert_array append_update_buffer make_mini_batch reset_agent array sample_mini_batch construct_fake_buffer append_update_buffer construct_fake_buffer truncate_update_buffer append_update_buffer Curriculum Curriculum Curriculum make_demo_buffer load_demonstration dirname abspath make_demo_buffer load_demonstration dirname abspath MagicMock basic_options MagicMock MetaCurriculum assert_has_calls MetaCurriculumTest increment_lessons assert_called_with MetaCurriculumTest increment_lessons assert_called_with assert_not_called MetaCurriculumTest set_all_curriculums_to_lesson_num MetaCurriculumTest dict update MetaCurriculumTest reset_default_graph MultiGpuPPOPolicy create_mock_brainparams reset_default_graph create_mock_brainparams update Mock reset_default_graph MultiGpuPPOPolicy create_mock_brainparams MagicMock TFPolicy basic_mock_brain basic_params BrainInfo get_action MagicMock TFPolicy basic_mock_brain basic_params BrainInfo get_action MagicMock TFPolicy basic_mock_brain ActionInfo basic_params BrainInfo get_action evaluate close reset MockCommunicator PPOPolicy reset_default_graph UnityEnvironment get_value_estimates items list close reset MockCommunicator PPOPolicy reset_default_graph UnityEnvironment reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph assert_array_almost_equal array discount_rewards Mock increment_step BrainParameters assert_called_with PPOTrainer AllRewardsOutput BrainParameters PPOTrainer add_rewards_outputs update PPOPolicy setup_mock_env_and_brains reset evaluate model simulate_rollout _execute_model prepare_update update_dict make_mini_batch create_ppo_policy_mock reward_signal_update reward_signal_eval reward_signal_update reward_signal_eval create_ppo_policy_mock dirname abspath create_ppo_policy_mock reward_signal_update reward_signal_eval create_ppo_policy_mock reward_signal_update reward_signal_eval create_ppo_policy_mock reward_signal_update reward_signal_eval create_ppo_policy_mock reward_signal_update reward_signal_eval create_ppo_policy_mock reward_signal_update reward_signal_eval create_ppo_policy_mock reward_signal_update reward_signal_eval create_mock_brainparams RLTrainer dummy_config create_mock_brain Mock list create_mock_all_brain_info create_rl_trainer values end_episode construct_curr_info episode_steps create_mock_braininfo create_mock_policy add_experiences Simple1DEnvironment _check_environment_trains TrainerController assert_called_with MagicMock basic_trainer_controller start_learning assert_called_once MagicMock assert_not_called trainer_controller_with_start_learning_mocks trainer_controller_with_start_learning_mocks start_learning MagicMock assert_called_once MagicMock basic_trainer_controller assert_called_once Mock MagicMock StepInfo current_all_brain_info advance outputs assert_not_called trainer_controller_with_take_step_mocks assert_called_once_with previous_all_brain_info dummy_offline_bc_config dummy_offline_bc_config_with_override BrainParametersMock BrainParametersMock dummy_online_bc_config dummy_config BrainParametersMock dummy_bad_config extend copy global_done get_timer_root reset_timers put _send_response reset_parameters StepResponse env_factory memory list action value external_brains payload items EnvironmentResponse text reset step perf_counter push reset method_handlers_generic_handler add_generic_rpc_handlers UnityEnvironment close MockCommunicator UnityEnvironment close MockCommunicator reset str local_done print agents step close reset MockCommunicator UnityEnvironment len UnityEnvironment close MockCommunicator close RpcCommunicator close RpcCommunicator close RpcCommunicator SamplerManager sample_all sampler_config_1 sampler_config_2 SamplerManager SamplerManager sample_all incorrect_uniform_sampler incorrect_sampler_config set_gauge replace endswith add set walk | Non-biri/ml-agents-0.9.3 | 799 |
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