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jhzab/dist_w2v | ['information retrieval', 'word embeddings'] | ['Asynchronous Training of Word Embeddings for Large Text Corpora'] | code/reducer.py code/alr_combine.py code/mapper.py gensim_as_dict dim_reduce load_wv_dict gensim_as_dict_with_words_seen indices eval_embedding randomly_drop_vectors concat_on_common_indexes print_from_queue deplete_queue get_data_from_stdin get_at_least_one_key filter_tokens get_data_from_json get_keys send_from_queue merge_whitespace remove_and_sanitize_tokens remove_and_sanitize_text remove_punctuation get_current_epoch convert_to_dict save_model load_model model_exists set_environ process_input create_new_model save_words_seen get_key list get_vector keys init_sims list items get_vector init_sims load init_sims vocab hstack isin TruncatedSVD from_dict list y evaluate_on_all fetch_google_analogy evaluate_analogy dict fetch_RW zip fetch_MEN X evaluate_similarity sample list keys list filter remove_punctuation merge_whitespace get_keys popleft print append print_from_queue print_from_queue stdin loads remove_and_sanitize_text stdin clear build_vocab_from_freq Word2Vec info info info list print tolist dumps dict keys split info sleep model_exists range info info stdin rstrip defaultdict error exit split | # Asynchronous Training of Word Embeddings for large text corpora ## How to use In the `code` directory is the script `execute_epochs.sh`. It handles/sets: * calling hadoop streaming * number of VCOREs per reducer * number of reducers * name of the job * number of epochs Additionally, the files `mapper.py` and `reducer.py` need to be modified. | 2,500 |
jia2lin3yuan1/2020-instanceSeg | ['instance segmentation', 'semantic segmentation'] | ['Deep Variational Instance Segmentation'] | utils/logger.py data/pascal.py layers/box_utils.py layers/modules/refine_loss.py dvis_network.py layers/__init__.py data/config_pascal.py utils/timer.py train.py data/__init__.py layers/interpolate.py layers/output_utils.py layers/functions/detection.py layers/modules/loss_main.py data/base_config.py refineNet.py utils/__init__.py layers/functions/__init__.py layers/modules/instance_loss.py utils/augmentations.py train_settings.py data/base_dataset.py data/coco.py utils/nvinfo.py layers/discritizer.py data/config_coco.py utils/functions.py layers/modules/classify_loss.py layers/modules/python_func.py backbone.py layers/modules/regularize_loss.py layers/modules/evaluate.py DarkNetBlock DarkNetBackbone darknetconvlayer ResNetBackboneGN VGGBackbone Bottleneck ResNetBackbone construct_backbone DVIS FPN Concat RoIAlign RoIExtractor RefineNet set_lr_value CustomDataParallel replace set_lr_scale compute_validation_loss plot_tfboard_figure NetLoss gradinator prepare_data no_inf_mean parse_args train str2bool getDefaultSetting Config overwrite_params_from_json change_backbone_resnet101_dcn setup_network_base_config change_backbone_darknet53 change_backbone_resnet50_dcn change_config_imgSize change_backbone_resnet50 overwrite_args_from_json enforce_size extract_target_from_image get_label_map Detection detection_collate FromImageAnnotationTransform FromBboxesAnnotationTransform AnnotationTransform COCODetection set_cfg set_dataset set_cfg set_dataset PASCALDetection dataset_specific_import index2d decode elemwise_box_iou intersect log_sum_exp change jaccard center_size mask_iou match sanitize_coordinates point_form encode crop elemwise_mask_iou MeanshiftCluster_0 MeanshiftCluster_2 vis_meanshift_result Discritizer extract_candidates MeanshiftCluster_1 extract_candidates_np extend_bboxes InterpolateModule undo_image_transformation Detect FocalLoss CrossEntropyLoss BinaryCrossEntropyLoss Evaluate PermuInvLoss pi_l1_loss PermuInvLossDilatedConv adjust_smooth_l1_loss BinaryLoss pi_exp_loss LossEvaluate create_pairwise_conv_kernel RoIAlign vis_mapped_GT RefineLoss adjust_smooth_l1_loss MumfordShahLoss QuantityLoss SwapChannels ToTensor ToAbsoluteCoords RandomBrightness PhotometricDistort enable_if RandomSaturation Resize RandomRot90 BaseTransform RandomSampleCrop ToPercentCoords RandomFlip Pad intersect SSDAugmentation Lambda Compose FastBaseTransform ConvertColor BackboneTransform Expand jaccard_numpy RandomHue ConvertFromInts RandomMirror RandomContrast do_nothing PrepareMasks ToCV2Image RandomLightingNoise make_net init_console MovingAverage ProgressBar SavePath Log LogVisualizer LogEntry _run_cmd nvsmi_available gpu_info visible_gpus enable total_time enable_all start reset disable disable_all stop print_stats env selected_layers type max add_layer set_defaults add_argument ArgumentParser setattr getattr show subplots undo_image_transformation size min axis colorbar choice range imshow tick_params numpy max backbone enumerate len add_figure batch_size save_folder MovingAverage lr_warmup_until tuple zero_grad SGD DVIS DataLoader save_weights cuda log exp_name max max_iter log_folder CustomDataParallel set_lr_value get_avg set_lr_scale name NetLoss len exit get_interrupt add delayed_settings append ceil sum range _get_kwargs SummaryWriter format replace DataSet classify_en plot_tfboard_figure get_model_complexity_info save_path grad LossEvaluate close init_weights load_weights resume lr item start_iter float net join time get_latest remove backward print add_scalar keep_latest min Log dict freeze_bn reset iteration disable_all step retain_grad makedirs param_groups param_groups copy copy copy split copy split copy split copy split setattr uint16 regionprops astype unique append coords bbox append FloatTensor eval eval replace print exit clamp size min expand max intersect expand_as clamp min max t size unsqueeze view size sum view size expand use_yolo_regressors size jaccard encode max range center_size log cat cat point_form max max min long clamp sanitize_coordinates size expand size expand_as show subplots set_title imshow argmax sorted bbox append label float regionprops len ones_like view cumsum size empty_case stack append merge_disconnected_regions float sum max range cat clamp stack min max subtract_means astype float32 normalize numpy array clip float relu adjust_smooth_l1_loss sigmoid seed label list reshape choice meshgrid zeros float abs range show subplots print min close imshow range minimum clip maximum intersect init sum _run_cmd int range len check_output decode add remove clear append perf_counter stop print start pop format print total_time find max len | # Deep Variational Instance Segmentation ``` ████████║ ██ ██ ██████████ █████████║ ██ █║ █ █ ║██║ █║ ██ █║ █ █ ║██║ █████████║ ██ █║ █ █ ║██║ ██║ ██ █║ █ █ ║██║ ██║ ███████║ █████ ██████████ █████████ ``` A simple, fully convolutional model for real-time instance segmentation. This is the code for our papers: | 2,501 |
jiangqy/DCMH-CVPR2017 | ['cross modal retrieval'] | ['Deep Cross-Modal Hashing'] | DCMH_matlab/DCMH_matlab/matconvnet/utils/import-caffe.py DCMH_tensorflow/DCMH_tensorflow/net_structure_txt.py DCMH_matlab/DCMH_matlab/matconvnet/utils/layers.py DCMH_matlab/DCMH_matlab/matconvnet/doc/matdocparser.py DCMH_matlab/DCMH_matlab/matconvnet/utils/proto/caffe_b590f1d_pb2.py DCMH_matlab/DCMH_matlab/matconvnet/utils/proto/caffe_6e3916_pb2.py DCMH_matlab/DCMH_matlab/matconvnet/utils/proto/vgg_caffe_pb2.py DCMH_matlab/DCMH_matlab/matconvnet/utils/proto/caffe_0115_pb2.py DCMH_matlab/DCMH_matlab/matconvnet/doc/matdoc.py DCMH_tensorflow/DCMH_tensorflow/load_data.py DCMH_matlab/DCMH_matlab/matconvnet/utils/proto/caffe_old_pb2.py DCMH_tensorflow/DCMH_tensorflow/output_result.py DCMH_tensorflow/DCMH_tensorflow/utils/calc_hammingranking.py DCMH_matlab/DCMH_matlab/matconvnet/utils/proto/caffe_pb2.py DCMH_tensorflow/DCMH_tensorflow/net_structure_img.py DCMH_tensorflow/DCMH_tensorflow/DCMH_demo.py extract render_L render_V render_P render_DIVL render_DH render_BL render_SL render_S render Context render_DI Frame render_B findNextFunction getFunctionDoc readText MatlabFunction render_DL clean Lexer P PL Parser BH EOF DI L DL SL BL DH DIVL Terminal S DIV NonTerminal B V Symbol blobproto_to_array versiontuple dict_to_struct_array keyboard tolist escape bilinear_interpolate getopts find rowcell CaffeInnerProduct ConversionError CaffeScale CaffeBatchNorm CaffeLayer CaffeCrop CaffeConcat CaffeConv CaffePooling CaffeData reorder CaffeElementWise getFilterOutputSize CaffeSoftMaxLoss CaffeReLU getFilterTransform CaffeModel CaffeTransform CaffeDeconvolution row dictToMatlabStruct rowarray CaffeBlob transposeTransform CaffeDropout CaffeLRN CaffeEltWise composeTransforms CaffeSoftMax HingeLossParameter BlobProto BlobProtoVector NetStateRule LayerParameter PowerParameter FillerParameter ArgMaxParameter V0LayerParameter InnerProductParameter ConvolutionParameter SolverState EltwiseParameter SliceParameter WindowDataParameter DummyDataParameter HDF5OutputParameter TanHParameter TransformationParameter SoftmaxParameter ConcatParameter DataParameter SolverParameter MVNParameter ContrastiveLossParameter NetState NetParameter PoolingParameter DropoutParameter Datum SigmoidParameter AccuracyParameter MemoryDataParameter LRNParameter ReLUParameter ImageDataParameter InfogainLossParameter HDF5DataParameter ThresholdParameter ReductionParameter HingeLossParameter BlobProto BlobProtoVector NetStateRule LayerParameter PowerParameter FillerParameter ArgMaxParameter V0LayerParameter InnerProductParameter ConvolutionParameter SolverState EltwiseParameter LossParameter SliceParameter WindowDataParameter DummyDataParameter HDF5OutputParameter TanHParameter TransformationParameter SoftmaxParameter ConcatParameter DataParameter SPPParameter ParamSpec EmbedParameter SolverParameter MVNParameter ContrastiveLossParameter NetState NetParameter PoolingParameter DropoutParameter Datum SigmoidParameter BlobShape ExpParameter AccuracyParameter LogParameter ThresholdParameter TileParameter MemoryDataParameter LRNParameter ReLUParameter ImageDataParameter ReshapeParameter InfogainLossParameter V1LayerParameter HDF5DataParameter PReLUParameter FlattenParameter PythonParameter ReductionParameter HingeLossParameter BlobProto BlobProtoVector NetStateRule LayerParameter PowerParameter FillerParameter ArgMaxParameter V0LayerParameter InnerProductParameter ConvolutionParameter SolverState EltwiseParameter LossParameter SliceParameter BatchNormParameter WindowDataParameter DummyDataParameter HDF5OutputParameter TanHParameter TransformationParameter SoftmaxParameter ConcatParameter DataParameter SPPParameter ParamSpec EmbedParameter SolverParameter MVNParameter ContrastiveLossParameter NetState NetParameter BiasParameter PoolingParameter DropoutParameter Datum SigmoidParameter BlobShape ExpParameter AccuracyParameter LogParameter ThresholdParameter TileParameter MemoryDataParameter LRNParameter ReLUParameter ImageDataParameter ELUParameter ReshapeParameter InfogainLossParameter ScaleParameter V1LayerParameter HDF5DataParameter PReLUParameter FlattenParameter PythonParameter NetParameter LayerConnection BlobProto BlobProtoVector LayerParameter FillerParameter Datum SolverParameter SolverState BlobProto BlobProtoVector PowerParameter LayerParameter FillerParameter V0LayerParameter InnerProductParameter ConvolutionParameter SolverState WindowDataParameter HDF5OutputParameter ConcatParameter DataParameter SolverParameter NetParameter PoolingParameter DropoutParameter Datum MemoryDataParameter LRNParameter ImageDataParameter InfogainLossParameter HDF5DataParameter NetParameter EvalHistoryIter LayerConnection BlobProto BlobProtoVector LayerParameter FillerParameter Datum EvalHistory SolverParameter SolverState test_validation generate_text_code calc_neighbor train_txt_net split_data train_img_net generate_image_code calc_loss loading_data get_meanpix img_net_strucuture _pool_layer _conv_layer unprocess preprocess _full_conv txt_net_strucuture calc_map calc_hammingDist search clean match strip group append getFunctionDoc findNextFunction print print children render_SL print pop render_DH print Frame push render_DIVL children render_DI print children render_L print pop children render_L isa Frame render_B push pop children Frame push render_DIVL children render_BL render_V render_S render_DL isa render_P print Context render_DIVL dim tolist hasattr list empty keys RepeatedScalarFieldContainer isinstance update print f_locals interact copy reshape asarray astype clip hasattr list ndarray isinstance append empty keys CaffeTransform CaffeTransform list permutation setdiff1d float64 astype calc_neighbor eval range run list permutation setdiff1d reshape astype float32 calc_neighbor eval range run astype exp ones transpose matmul power sum log transpose astype float32 sign eval repeat zeros range len reshape transpose astype float32 sign eval zeros range calc_map transpose File close convert_to_tensor local_response_normalization _pool_layer relu Variable reshape squeeze _conv_layer matmul _full_conv startswith append loadmat random_normal enumerate Variable conv2d pad append Variable conv2d append pad loadmat relu Variable squeeze transpose conv2d bias_add random_normal dot transpose asarray astype float32 where argsort mean linspace sum range calc_hammingDist | ---
# Source code for Deep Cross-Modal Hashing
---
## Introduction
* This package contains the source code for the following paper:
* Qing-Yuan Jiang and Wu-Jun Li. **Deep Cross-Modal Hashing**. *CVPR-2017*.
* Author: [Qing-Yuan Jiang](https://jiangqy.github.io/) and [Wu-Jun Li](http://cs.nju.edu.cn/lwj)
* Contact: qyjiang24#gmail.com or liwujun#nju.edu.cn
* We recommend you use matlab version to run DCMH algorithm.
| 2,502 |
jiangxiluning/FOTS.PyTorch | ['scene text detection', 'text spotting', 'scene text recognition'] | ['FOTS: Fast Oriented Text Spotting with a Unified Network'] | FOTS/utils/post_processor.py FOTS/model/keys.py FOTS/base/base_model.py FOTS/utils/util.py FOTS/rroi_align/modules/rroi_align.py FOTS/data_loader/data_module.py FOTS/data_loader/__init__.py FOTS/utils/eval_tools/icdar2015/script.py tests/test_model.py FOTS/data_loader/datautils.py FOTS/data_loader/data_loaders.py FOTS/model/ghm.py FOTS/data_loader/synthtext_dataset.py FOTS/trainer/__init__.py FOTS/trainer/trainer.py FOTS/utils/lanms/__init__.py train.py FOTS/model/modules/shared_conv.py FOTS/model/modules/crnn/crnn.py FOTS/rroi_align/main.py FOTS/base/base_trainer.py FOTS/utils/eval_tools/icdar2015/eval.py FOTS/data_loader/icdar_dataset.py FOTS/model/modules/roi_align/_ext/crop_and_resize/__init__.py FOTS/model/model.py FOTS/logger/__init__.py FOTS/model/modules/crnn/__init__.py FOTS/rroi_align/functions/rroi_align.py FOTS/rroi_align/build.py FOTS/utils/eval_tools/icdar2015/rrc_evaluation_funcs_1_1.py FOTS/model/modules/roi_align/build.py FOTS/utils/eval_tools/icdar2015/rrc_evaluation_funcs.py FOTS/utils/detect.py FOTS/data_loader/utils.py FOTS/model/loss.py FOTS/model/modules/roi_align/crop_and_resize.py FOTS/utils/bbox.py eval.py FOTS/data_loader/transforms.py FOTS/logger/logger.py FOTS/model/metric.py FOTS/model/modules/roi_rotate.py FOTS/model/modules/roi_align/roi_align.py FOTS/utils/lanms/__main__.py FOTS/base/__init__.py FOTS/base/base_data_loader.py main load_model main BaseDataLoader BaseModel BaseTrainer get_images load_annoataion line_verticle shrink_poly crop_area polygon_area point_dist_to_line fit_line restore_rectangle_rbox line_cross_point image_label generate_rbox check_and_validate_polys collate_fn normalize_iamge sort_rectangle restore_rectangle rectangle_from_parallelogram OCRDataLoaderFactory SynthTextDataLoaderFactory SynthTextDataModule ICDARDataModule ICDARDataset SynthTextDataset Transform rotate_vertices get_rotate_mat shrink_poly is_cross_text move_points rotate_all_pixels find_min_rect_angle get_score_geo rotate_img get_boundary cal_error crop_img cal_distance adjust_height Logger _expand_binary_labels GHMR GHMC DetectionLoss get_dice_loss RecognitionLoss get_geo_loss FOTSLoss fots_metric FOTSModel Recognizer Detector ROIRotate DummyLayer HLayer SharedConv HeightMaxPool BidirectionalLSTM CRNN CropAndResize CropAndResizeFunction RoIAlign _import_symbols get_extensions RRoiAlignFunction _RRoiAlign Trainer Toolbox restore_polys resize_img get_rotate_mat get_boxes detect_dataset adjust_ratio detect plot_boxes is_valid_poly load_pil PostProcessor ensure_dir StringLabelConverter show_box visualize transform_output evaluate_method default_evaluation_params evaluation_imports eval validate_data 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 validate_point_inside_bounds load_zip_file_keys validate_clockwise_points validate_lines_in_file decode_utf8 print_help get_tl_dict_values get_tl_dict_values_from_array 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 merge_quadrangle_n9 test_icdar_dataset test_transform test_roi test_affine test_rect_synth800k test_rroi test_rect_icdar test_synthtext_dataset str ICDARDataModule gpus FOTSModel name absolute Trainer Path mkdir WandbLogger ModelCheckpoint SynthTextDataModule fit load config EasyDict model glob eval output_dir input_dir to load_from_checkpoint open str format setup pretrain ICDARDataModule FOTSModel name gpus absolute Trainer Path mkdir info WandbLogger ModelCheckpoint SynthTextDataModule fit join format glob extend append range len print append clip polygon_area min astype choice shape int32 zeros range max clip arctan2 polyfit print norm arccos line_verticle fit_line dot line_cross_point sum arctan print argmin argmax concatenate reshape transpose zeros array fillPoly flatten line_cross_point sort_rectangle ones argmin append minAreaRect sum range fit_line enumerate norm point_dist_to_line min argwhere zeros array rectangle_from_parallelogram join load_annoataion replace crop_area ones float astype float32 copy choice shape generate_rbox check_and_validate_polys resize zeros imread max list extend from_numpy dict stack cat permute zip append tensor range enumerate len array cal_distance move_points min copy cal_distance dot T get_rotate_mat min max get_boundary cal_distance rotate_vertices list sorted min pi append cal_error float max range len reshape area convex_hull int height is_cross_text resize rand BILINEAR shape width zeros crop arange concatenate reshape dot shape meshgrid array int height rand BILINEAR copy around resize rotate_vertices height rand BILINEAR pi rotate shape width zeros array enumerate int rotate_vertices arange get_rotate_mat reshape fillPoly astype float32 minAreaRect shape rotate_all_pixels find_min_rect_angle get_boundary int32 meshgrid zeros append enumerate new_full size squeeze expand sum min cos split eval default_evaluation_params dir _wrap_function getattr append callable join glob print dirname abspath size BILINEAR resize Compose range get_rotate_mat concatenate dot array append is_valid_poly range restore_polys astype copy shape argwhere zeros merge_quadrangle_n9 resize_img numpy get_boxes polygon Draw sorted format print extend detect listdir enumerate open makedirs int max uint8 imwrite polylines reshape putText min PCA astype shape FONT_HERSHEY_PLAIN fit_transform range int str imwrite with_suffix polylines putText stem astype IMREAD_COLOR FONT_HERSHEY_PLAIN mkdir Path zip imread load_zip_file validate_lines_in_file compute_ap list namedtuple area int8 keys rectangle_to_polygon include_in_dictionary_transcription Rectangle get_intersection_over_union get_intersection append zeros float empty polygon_from_points range len append match group enumerate transform_output evaluate_method 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 list items write dumps close dict print_help evaluate_method_fn ZipFile makedirs update default_evaluation_params_fn validate_data_fn print exit dict validate_point_inside_bounds validate_clockwise_points append float range len append int range len argsort get_tl_dict_values append range len decode_utf8 import_module load_zip_file get_tl_line_values_from_file_contents items nms_impl array copy roi_rotate uint8 ToTensor astype float32 waitKey imshow numpy permute imread ROIRotate to_tensor range print reshape astype float32 show_box minAreaRect imread array range len T print reshape shape show_box minAreaRect imread loadmat array range len imwrite Transform transform Polygon print transpose tolist PolygonsOnImage shape draw_on_image append imread loadmat range warpAffine radians uint8 grid_sample float64 size ToTensor waitKey param2theta affine_grid astype imshow Tensor imread numpy to_tensor print Transform SynthTextDataset print DataLoader Transform ICDARDataset imwrite tensor cuda view shape permute ceil minAreaRect append imread sum range asarray copy sqrt _RRoiAlign swapaxes item enumerate backward print atan2 roipool randint | # News!!! Recognition branch now is added into model. The whole project has beed optimized and refactored. - [x] ICDAR Dataset - [x] SynthText 800K Dataset - [x] detection branch (verified on the training set, It works!) - [x] recognition branch (verified) - [x] eval - [x] multi-gpu training - [x] reasonable project structure - [x] wandb - [x] pytorch_lightning | 2,503 |
jiangxiluning/MASTER-TF | ['scene text recognition'] | ['MASTER: Multi-Aspect Non-local Network for Scene Text Recognition'] | src/tools/eval_net.py train.py src/model/utils.py src/model/transformer.py src/dataset/generator_enqueuer.py src/dataset/lmdb_data_generator.py src/model/backbone.py src/model/gcb.py src/dataset/utils.py eval_iiit5k.py src/model/model.py src/model/transformer_tf.py src/dataset/dataset.py src/dataset/benchmark_data_generator.py src/tools/train_net.py tests/test_units.py src/model/metrics.py main main parse_output prepare_outputs setup_logger generator_folder generator_lmdb LmdbDataset GeneratorEnqueuer generator get_vocabulary resize_width rotate_img strLabelConverterForTransformer conv33 BasicBlock Resnet31 GolbalContextBlock WordAccuary MasterModel create_look_ahead_mask PositionwiseFeedForward clones MultiHeadAttention get_angles Decoder create_padding_mask positional_encoding SublayerConnection DecoderLayer point_wise_feed_forward_network scaled_dot_product_attention create_look_ahead_mask MultiHeadAttention get_angles Decoder create_padding_mask positional_encoding DecoderLayer greedy_decoding Predictor train CustomSchedule get_optimizer get_dataset test_hashtable test_decoder test_savedModel test_master test_dataset test_training test_accuarcy test_loadModel test_benchmark_dataset test_backbone load EasyDict Predictor open as_posix format mkdir as_posix rmtree Path copytree root exists as_posix remove stdout add parse_output prepare_outputs as_posix experimental_run_functions_eagerly setup_logger train edict joinpath Path exception open begin int decode get arange format close IMREAD_COLOR imdecode warning IMREAD_GRAYSCALE encode frombuffer max_length open list range dict ascii_lowercase zip append ascii_letters digits len warpAffine cos deg2rad getRotationMatrix2D shape dot sin abs array resize_with_pad power float32 get_angles cos sin float32 cast equal ones band_part float32 matmul sqrt cast softmax constant Assert fill range equal CustomSchedule name Adam SGD lr items list train LmdbDataset dict eval dataset WordAccuary concat assign save list checkpoints epochs assign_add pprint append distributed_eval_step range update format partial debug distributed_train_step OneDeviceStrategy info enumerate compute items int slack_api notify write dict reduce_mean reset MirroredStrategy get_notifier notifier numpy steps load EasyDict print get_dataset enumerate load EasyDict normal print Resnet31 shape resnet load EasyDict normal constant trainable_variables list Adadelta model print gradient apply_gradients zip MasterModel randint load EasyDict normal decode model print decode_tensor MasterModel WordAccuary update reset zip print generator_lmdb list constant KeyValueTensorInitializer print uniform lookup StaticHashTable keys values checkpoint_to_saved_model load decode normal print | # MASTER-TensorFlow  <div align=center> <img src="https://github.com/wenwenyu/MASTER-pytorch/blob/main/assets/logo.jpeg" width="200" height="200" /> </div> TensorFlow reimplementation of ["MASTER: Multi-Aspect Non-local Network for Scene Text Recognition"](https://arxiv.org/abs/1910.02562) (Pattern Recognition 2021). This project is different from our original implementation that builds on the privacy codebase FastOCR of the company. You can also find PyTorch reimplementation at [MASTER-pytorch](https://github.com/wenwenyu/MASTER-pytorch) repository, and the performance is almost identical. (PS. Logo inspired by the Master Oogway in Kung Fu Panda) ## News * 2021/07: [MASTER-mmocr](https://github.com/JiaquanYe/MASTER-mmocr), reimplementation of MASTER by mmocr. [@Jiaquan Ye](https://github.com/JiaquanYe) | 2,504 |
jianz94/e-lstm-d | ['link prediction', 'time series'] | ['E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction'] | train_model.py baselines/DDNE.py utils.py model.py e_lstm_d get_err_rate load_data get_auc build_refined_loss DDNE | # e-lstm-d This is a TensorFlow implementation of the paper: [E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction](https://arxiv.org/abs/1902.08329). The baselines used in the paper will be released as a toolbox soon. # Requirements - tensorflow (1.3.0) - keras (2.2.4) - scikit-learn (0.19.0) - numpy (1.14.2) # run the demo #### The framework of E-LSTM-D We provide the framework of E-LSTM-D and the detailed structure of it when applied on LKML. | 2,505 |
jiao0805/bts4 | ['depth estimation', 'monocular depth estimation'] | ['From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation'] | pytorch/bts_main.py tensorflow/bts_main.py tensorflow/run_bts_eval_schedule.py pytorch/bts_test.py pytorch/distributed_sampler_no_evenly_divisible.py tensorflow/average_gradients.py tensorflow/custom_layer/_local_planar_guidance_grad.py pytorch/run_bts_eval_schedule.py pytorch/bts_dataloader.py tensorflow/bts_eval.py tensorflow/bts_live_3d.py tensorflow/bts_sequence.py pytorch/bts_live_3d.py utils/eval_with_pngs.py tensorflow/bts.py tensorflow/resnet_v1.py utils/download_from_gdrive.py utils/extract_official_train_test_set_from_mat.py tensorflow/bts_dataloader.py pytorch/bts.py pytorch/bts_eval.py tensorflow/bts_test.py bts weights_init_xavier BtsModel reduction_1x1 silog_loss upconv local_planar_guidance encoder bn_init_as_tf atrous_conv _is_numpy_image preprocessing_transforms ToTensor BtsDataLoader _is_pil_image DataLoadPreprocess convert_arg_line_to_args compute_errors test eval get_num_lines toc load_model np_to_qimage Window GLWidget tic edges qimage_to_np main_worker enable_print online_eval convert_arg_line_to_args normalize_result colorize compute_errors block_print set_misc main get_num_lines convert_arg_line_to_args test get_num_lines DistributedSamplerNoEvenlyDivisible run_eval average_gradients BtsModel BtsDataloader convert_arg_line_to_args compute_errors test eval main get_num_lines toc load_model np_to_qimage Window GLWidget tic edges qimage_to_np get_tensors_in_checkpoint_file convert_arg_line_to_args build_tensors_in_checkpoint_file main train get_num_lines test_sequence main main convert_arg_line_to_args test get_num_lines resnet_v1_152 NoOpScope resnet_v1_101 bottleneck resnet_v1_200 resnet_v1_50 resnet_v1 resnet_v1_block run_eval _local_planar_guidance_grad_cc download_file_from_google_drive convert_arg_line_to_args compute_errors test eval main convert_image BatchNorm2d eval isinstance isinstance Conv2d xavier_uniform_ zeros_ bias weight split maximum mean sqrt log10 abs log readlines close open rstrip checkpoint_path BtsDataLoader filenames_file DataParallel cuda exists str sorted add load_state_dict append imread range SummaryWriter format astype BtsModel set mean eval flush load int remove join time isdir add_scalar print gt_path float32 output_directory model_name get_num_lines filenames_file garg_crop max_depth_eval len logical_and shape append do_kb_crop range format mean eigen_crop int min_depth_eval print compute_errors float32 zeros get_num_lines load checkpoint_path BtsModel DataParallel eval load_state_dict cuda time print time format copy convertToFormat Format_ARGB32 sobel Signal devnull open __stdout__ cmapper get_cmap log10 bn_no_track_stats named_children fix_first_conv_block print apply named_parameters any fix_first_conv_blocks data new_group multiprocessing_distributed garg_crop cuda max_depth_eval logical_and shape do_kb_crop range format item enumerate int min_depth_eval print compute_errors tqdm all_reduce cpu zeros eigen_crop data bool set_misc checkpoint_path batch_size multiprocessing_distributed model BtsDataLoader zero_grad where DataParallel DistributedDataParallel save forward cuda exists set_device do_online_eval log_directory apply silog_loss log_freq rank load_state_dict sleep to sum range SummaryWriter format enable_print normalize_result init_process_group param_groups BtsModel eval_summary_directory distributed eval retrain item num_epochs flush add_image load int join time learning_rate online_eval enumerate backward print AdamW Variable add_scalar system set_epoch isnan block_print model_name isfile cpu zeros train step gpu len eval_freq world_size basename checkpoint_path format spawn print multiprocessing_distributed do_online_eval log_directory system device_count dirname model_name main_worker empty_cache gpu uint16 imwrite save_lpg list log10 imsave replace mkdir tqdm data_path amax print system now concat reduce_mean zip append expand_dims initializer getmtime get_next Saver Session run make_initializable_iterator start_queue_runners global_variables_initializer FileWriter ConfigProto local_variables_initializer Coordinator BtsDataloader bts_parameters test start_queue_runners bts_parameters Coordinator Saver global_variables_initializer local_variables_initializer run sorted NewCheckpointReader get_tensor append get_variable_to_shape_map list add set append get_tensor_by_name enumerate train Saver Session run placeholder image_path append start_queue_runners glob BtsModel ConfigProto local_variables_initializer join constant print sort float32 Coordinator global_variables_initializer len test_sequence get get_confirm_token save_response_content Session pred_path filter walk len int imwrite uint16 astype zeros makedirs | # BTS [](https://paperswithcode.com/sota/monocular-depth-estimation-on-kitti-eigen?p=from-big-to-small-multi-scale-local-planar) [](https://paperswithcode.com/sota/monocular-depth-estimation-on-nyu-depth-v2?p=from-big-to-small-multi-scale-local-planar) From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation [arXiv](https://arxiv.org/abs/1907.10326) [Supplementary material](https://arxiv.org/src/1907.10326v4/anc/bts_sm.pdf) ## Video Demo 1 [](https://www.youtube.com/watch?v=2fPdZYzx9Cg) ## Video Demo 2 [](https://www.youtube.com/watch?v=1J-GSb0fROw) | 2,506 |
jiayan97/linknet-pytorch | ['graph generation', 'scene graph generation'] | ['LinkNet: Relational Embedding for Scene Graph'] | dataloaders/blob.py lib/surgery.py lib/draw_rectangles/setup.py lib/get_dataset_counts.py dataloaders/image_transforms.py lib/object_detector.py models/train_detector.py lib/sparse_targets.py lib/rel_model_ucsd.py lib/rel_model_linknet.py lib/lstm/highway_lstm_cuda/build.py lib/resnet.py lib/evaluation/sg_eval.py models/eval_rels.py lib/fpn/roi_align/functions/roi_align.py lib/rel_model_stanford.py lib/get_union_boxes.py lib/fpn/anchor_targets.py lib/fpn/roi_align/_ext/roi_align/__init__.py lib/fpn/proposal_assignments/proposal_assignments_rel.py lib/evaluation/sg_eval_all_rel_cates.py lib/word_vectors.py models/_visualize.py lib/fpn/proposal_assignments/rel_assignments.py lib/lstm/highway_lstm_cuda/alternating_highway_lstm.py models/eval_rel_count.py dataloaders/mscoco.py lib/fpn/box_intersections_cpu/setup.py lib/fpn/proposal_assignments/proposal_assignments_det.py lib/lstm/highway_lstm_cuda/_ext/highway_lstm_layer/__init__.py models/train_rels.py lib/fpn/nms/build.py lib/pytorch_misc.py lib/fpn/roi_align/build.py lib/rel_model.py lib/fpn/roi_align/modules/roi_align.py lib/lstm/decoder_rnn.py lib/evaluation/sg_eval_slow.py dataloaders/visual_genome.py config.py lib/evaluation/test_sg_eval.py lib/fpn/proposal_assignments/proposal_assignments_gtbox.py lib/fpn/proposal_assignments/proposal_assignments_postnms.py lib/fpn/box_utils.py misc/motifs.py lib/fpn/nms/functions/nms.py lib/fpn/generate_anchors.py stanford_path path ModelConfig random_crop Grayscale Contrast Brightness SquarePad RandomOrder Sharpness Hue CocoDataLoader coco_collate CocoDetection VGDataLoader load_image_filenames VG load_graphs assertion_checks vg_collate load_info box_filter get_counts union_boxes UnionBoxesAndFeats Result gather_res filter_roi_proposals load_resnet filter_det load_vgg RPNHead ObjectDetector transpose_packed_sequence_inds arange Flattener intersect_2d get_ranking enumerate_imsize const_row to_variable pairwise batch_map diagonal_inds right_shift_packed_sequence_inds to_onehot gather_nd argsort_desc optimistic_restore cache np_to_variable random_choose clip_grad_norm update_lr save_net batch_index_iterator de_chunkize load_net print_para accuracy nonintersecting_2d_inds enumerate_by_image unravel_index RelModel LinearizedContext _sort_by_score GlobalContextEncoding RelationalEmbedding LinearizedContext RelModelLinknet RelModelStanford GlobalContextEncoding RelationalEmbedding RelModel _sort_by_score LinearizedContext ResNet resnet_l4 vgg_fc Bottleneck resnet_l123 resnet101 FrequencyBias filter_dets reporthook load_word_vectors obj_edge_vectors _triplet evaluate_from_dict BasicSceneGraphEvaluator evaluate_recall _compute_pred_matches _triplet evaluate_from_dict BasicSceneGraphEvaluator evaluate_recall _compute_pred_matches _triplet iou BasicSceneGraphEvaluator eval_relation_recall _relation_recall _triplet eval_relation_recall _relation_recall iou anchor_target_layer nms_overlaps bbox_loss bbox_intersections center_size point_form bbox_overlaps bbox_preds generate_anchors _scale_enum _whctrs _ratio_enum generate_base_anchors _mkanchors _nms_single_im apply_nms _sel_inds proposal_assignments_det proposal_assignments_gtbox RoIAlignFunction RoIAlign RoIAlignAvg RoIAlignMax _import_symbols DecoderRNN get_dropout_mask _AlternatingHighwayLSTMFunction AlternatingHighwayLSTM block_orthogonal _import_symbols id_to_str increment_recursive meme_length val_batch gimme_the_dist predict val_epoch val_batch train_epoch train_batch val_epoch train_batch train_epoch get_optim val_batch val_batch draw_box load_unscaled get_cmap val_epoch int size min crop astype int32 randint max column_stack append reduce Blob tuple size join format append exists enumerate File astype len append zeros bbox_overlaps range column_stack load sorted open append reduce Blob copy zip zeros range len ones_like fill_diagonal where column_stack cat data Variable sort squeeze clone apply_nms nonzero zero_ cpu max get_device cuda apply_nms cat resnet101 vgg16 items join format print size set copy_ keys state_dict next tee size topk squeeze long size long arange fill_ items list File create_dataset items list asarray format print size File from_numpy copy_ range format print size f append batch_index_iterator Variable cuda LongTensor is_available join sorted format items named_parameters append topk size t eq mul_ expand_as append sum max ones diag where column_stack all Variable type cuda size dim range clone int numpy enumerate size long arange int enumerate append clone size min contiguous choice get_device cuda concatenate cumsum copy append range len append range zip items norm format sorted print size mul_ float print param_groups format sorted transpose_packed_sequence_inds sort new enumerate_by_image get_device append cuda load_url ResNet load_state_dict resnet101 resnet101 layer4 classifier vgg16 view sort size numpy max get format print normal_ load_word_vectors Tensor enumerate len decode save open str list basename view append range format isfile binary_type join isinstance print makedirs extend tqdm split len sum evaluate_recall intersect_2d ones len astype reduce union1d prod argsort_desc append float argmax max column_stack column_stack _triplet format print prod _compute_pred_matches column_stack int concatenate intersect_2d reshape any zip append list items min items list _triplet ones min astype copy _relation_recall append prod column_stack float32 range astype int32 iou astype intersect1d zip enumerate minimum maximum argmax max reshape vstack ravel array range enumerate int generate_anchors sum ones reshape where argmax bbox_overlaps column_stack smooth_l1_loss size center_size log cat center_size exp ndarray isinstance ndarray isinstance ndarray isinstance clamp size min expand max bbox_intersections ndarray isinstance expand_as view clamp size min expand max meshgrid stack arange generate_base_anchors vstack _ratio_enum array hstack sqrt _whctrs _mkanchors _whctrs _mkanchors _nms_single_im int size append cat IntTensor sort size contiguous min nms_apply long int max sort squeeze _sel_inds numpy nonzero cuda get_device append round bbox_overlaps range cat size min choice int sort size min clone contiguous random_choose enumerate_by_image nonzero long cat append dir getattr _wrap_function copy_ div clone data list product isinstance tuple size new orthogonal any zip max append evaluate_scene_graph_entry enumerate box_filter zeros array argsort format print enumerate time print_interval format train_batch print mean append train enumerate len data od_box_priors bbox_loss backward od_box_deltas od_box_targets rpn_scores squeeze size Series zero_grad rpn_box_deltas od_obj_labels clip_grad_norm step od_obj_dists cross_entropy val_batch evaluate concatenate print coco COCOeval summarize accumulate eval loadRes append enumerate coco numpy ReduceLROnPlateau adam Adam SGD rm_obj_dists values gce_obj_dists rel_dists gce_obj_labels sum rm_obj_labels num_gpus all_modes print_stats Normalize ScalarMappable size convert max resize format line print tuple text min rectangle textsize max tqdm reduce save query_pred load_unscaled column_stack list defaultdict query_gt draw_box set_trace range format union1d copy mkdir startswith keys int join Draw any len | # LinkNet *Now in experimental release, suggestions welcome*. This is a Pytorch reimplementation of [LinkNet](http://cn.arxiv.org/pdf/1811.06410v1) for Scene Graph Generation. Core code is [rel_model_linknet.py](https://github.com/jiayan97/linknet-pytorch/blob/master/lib/rel_model_linknet.py), built on top of [neural-motifs](https://github.com/rowanz/neural-motifs). ## Setup * Install Python3.6 & PyTorch3. ``` conda install pytorch=0.3.0 torchvision=0.2.0 cuda90 -c pytorch ``` * Download Visual Genome dataset, see data/stanford_filtered/README.md for details. * Compile everything, run ```make``` in the main directory. * Fix PYTHONPATH. ```export PYTHONPATH=/data/yjy/Workspace/linknet``` * Click [here](https://blog.csdn.net/weixin_38651565/article/details/87901172) for more detailed instructions. | 2,507 |
jiayi-wei/vqa-tf2 | ['visual question answering'] | ['Stacked Attention Networks for Image Question Answering'] | useless/preprocess_all_first.py useless/extract_img_feature.py eval.py preprocess.py model_2lstm.py data/prepro.py main.py model.py get_test_dict map_func_no_target plot_att save_result test get_data map_func get_data train map_func loss_function SAN_LSTM att_Module SAN_LSTM att_Module words_stat filter_data extract_feature tokenize1 tokenize2 main load_image get_top_answers feature_path max_length main filter_data encode_question preprocess_question apply_vocab_question encode_answer get_unique_img main get_top_answers tokenize build_vocab_question max_length load decode load decode load format from_tensor_slices print map open prefetch append len format set_title add_subplot axis tight_layout close imshow savefig figure resize array open join str format print plot_att append quit numpy range append numpy range SAN_LSTM join get_test_dict dump format model print latest_checkpoint Checkpoint save_result expect_partial get_data cast mkdir tokenizer_from_json open argmax enumerate batch loss_obj trainable_variables gradient get_data assign save CheckpointManager SAN_LSTM SparseCategoricalCrossentropy restore list Adam apply_gradients append range format reduce_any latest_checkpoint zip enumerate join int time is_nan print Checkpoint quit numpy get sorted format print append range len format print append enumerate len tokenize1 print tokenize2 append enumerate preprocess_input read_file decode_jpeg resize join mkdir decode list from_tensor_slices image_features_extract_model print reshape set tqdm save zip feature_path numpy batch fit_on_texts texts_to_sequences save VGG19 max open seed str extract_feature pad_sequences Model append input range Tokenizer max_length dump format close shuffle get_top_answers load filter_data print write output to_json feature_path len system word_tokenize format print lower tokenize enumerate len get format print append sum len zeros enumerate len zeros min enumerate len max zeros enumerate len File encode_question preprocess_question apply_vocab_question encode_answer create_dataset get_unique_img build_vocab_question | # Tensorflow 2.0 Implementation of Stacked Attention Networks for Image Question Answering In this repo, I implement the [SAN] (https://arxiv.org/pdf/1511.02274.pdf) with TensorFlow 2.0 framework. Only the LSTM is utilized to extract text presentation. Only the VQA-v2 dataset is employed in my experiment. ### Requirements Python 3.7 Tensorflow 2.2.0 ### Prepare Data All necessary VQA-v2 data (image/question/answer) could be downloaded with the scripts in the "data" folder. ``` cd data ``` | 2,508 |
jiesutd/SubwordEncoding-CWS | ['word embeddings', 'chinese word segmentation'] | ['Subword Encoding in Lattice LSTM for Chinese Word Segmentation'] | utils/gazetteer.py utils/data.py utils/__init__.py model/crf.py model/latticelstm.py utils/metric.py model/charbilstm.py model/bilstmcrf.py model/charcnn.py model/__init__.py utils/functions.py main.py utils/alphabet.py utils/trie.py model/bilstm.py BiLSTM BiLSTM_CRF CharBiLSTM CharCNN log_sum_exp CRF LatticeLSTM convert_forward_gaz_to_backward WordLSTMCell MultiInputLSTMCell init_list_of_objects Alphabet Data read_instance build_pretrain_embedding read_seg_instance normalize_word norm2one read_instance_with_gaz read_instance_with_gaz_in_sentence load_pretrain_emb Gazetteer get_ner_BIO fmeasure_from_singlefile readTwoLabelSentence readSentence fmeasure_from_file reverse_style get_ner_BMES get_ner_fmeasure Trie TrieNode max view append list range append range init_list_of_objects len isdigit decode readlines len normalize_word append get_index split decode readlines len normalize_word append get_index range split decode readlines enumerateMatchList normalize_word split append get_index range len list readlines len enumerateMatchList normalize_word append get_index range split items list print len dict sqrt uniform norm2one empty load_pretrain_emb sqrt sum square dict get_ner_BIO list print set intersection get_ner_BMES range len index len str replace upper reverse_style append range len str replace upper reverse_style append range len append readlines split append readlines split print readSentence get_ner_fmeasure print readTwoLabelSentence get_ner_fmeasure | Subword Encoding in Lattice LSTM for Chinese Word Segmentation ==== Subword encoding for Word Segmentation using Lattice LSTM. Models and results can be found at our paper [Subword Encoding in Lattice LSTM for Chinese Word Segmentation](https://arxiv.org/pdf/1810.12594.pdf). Requirement: ====== Python: 2.7 PyTorch: 0.3.0 Input format: ====== | 2,509 |
jiforcen/co-occurrence | ['image retrieval'] | ['Co-occurrence of deep convolutional features for image search'] | cooccurrence/cooccurrences.py cooc_example.py plot_figures calc_spatial_cooc ini_cooc_filter show subplots get_yticklabels get_xticklabels set imshow setp tick_params sum ones pow repeat cuda range conv2d float | # Co-occurrences of Deep Convolutional Features [](https://opensource.org/licenses/MIT) This repository contains the implementation of our ***co-occurrence*** representation method from convolutional neural networks activation maps in PyTorch. This ***co-occurrence*** representation method is presented in the paper [***Co-Occurrence* of Deep Convolutional Features for image search**](https://arxiv.org/abs/2003.13827). ## Formulation We define that a *co-occurrence* happens when the value of two different activations, a(i,j,k) and a(u,v,w), are greater than a threshold, t, and both are spatially located inside of a given region. (See Figure 1. left). The aggregation of the activation values when a *co-occurrence* occur produce the *co-occurrence* tensor. This *co-occurrence* formulation can be implemented using the convolution operator and filters. (See Figure 1. Right).  **Figure 1:** In the left an ilustration of a *co-occurrence* in an activation tensor. In the right a graphical example of how can be implemented using the convolution operaton. ## *Direct co-occurrences* & *Trainable co-occurrences* One great advantage of this implementation is that *co-occurrence* filter can be learned inside a trainable pipeline, and this can lead to improved *co-occurrence* representations so we consider two tipes of *co-occurrence* representations. * ***Direct co-occurrences*:** activation are aggregated using a fixed *co-occurrences* filter. | 2,510 |
jihoonerd/A-Neural-Algorithm-of-Artistic-Style | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | download_vgg.py styletf/data/data_utils.py styletf/model/network.py styletf/model/train.py styletf/misc/sample_images.py main.py test/test_styletf.py styletf/utils.py args_parse calc_gram_matrix resize_image download_image StyleTFNetwork StyleTFTrain test_training test_extract_vgg_layer test_calc_total_loss parse_args add_argument ArgumentParser reshape transpose matmul urlretrieve mkdir print float32 convert_image_dtype read_file resize decode_image remove set_seed model resize_image StyleTFTrain download_image remove set_seed model content_image calc_style_loss input_image calc_content_loss style_image calc_total_loss resize_image StyleTFTrain download_image remove set_seed resize_image train StyleTFTrain download_image | # A Neural Algorithm of Artistic Style |Content|Style|Style Transfer| |---|---|---| |||| |||| |||| This repository implements the paper: **[A Neural Algorithm of Artistic Style](https://arxiv.org/abs/1508.06576)**. ## Features * Employed ***TensorFlow 2*** with performance optimization * Simple structure | 2,511 |
jihoyeo/TGNet-keras | ['time series'] | ['Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding'] | models/TGNet_NYC.py models/model.py utils.py main.py output.py get_thrs get_atypical_idx atypical_index event_metric save_test_output holiday_marker model_output_chk flatten_result output_analysis mape_trs load_np_data data_loader get_min_max rmse_trs invlog_mape inverse_min_max min_max invlog_rmse mape invlog_rmse_tr10 scaler mae_t1 logscale mae_t2 inverse_logscale rmse invlog_mape_tr10 maa_trs BaseModel deconv_block gn_block TGNet int reshape int int arange print to_csv mape model_output_chk flatten_result DataFrame mape_trs print mape range rmse_trs values drop sum array min list columns T concatenate DataFrame reshape holiday_marker print rename zeros expand_dims argmax range list map range concatenate get_thrs columns list concatenate DataFrame reshape holiday_marker map rename expand_dims argmax array range enumerate len asarray print reshape mape rmse append join int load_np_data get_min_max scaler len print print exp astype average sqrt square mean abs average divide average sqrt exp cast greater exp cast greater exp exp subtract subtract concatenate | # TGNet-keras Author's implementation of TGNet. Our model has about **20 times smaller number of trainable parameters** than a recent state-of-the-are demand forecasting model, [STDN](https://github.com/tangxianfeng/STDN), and competitive or better performances on NYC datasets. We do **not** use external data, such as meteorological data, event information, traffic flow, or news, and only focus on efficient extraction of complex spatiotemporal features in past demand patterns. If you want to combine external data sources in our model, you can do that. After a stack of layers, combine the feature maps by the same manner of drop-off volumes in this model. Our model not only learns autoregressive model of ordered sequence, but also **learns temporal contexts explicitly**. Finally, TGNet learns **conditional autoregressive model on temporal contexts** of forecasting-targtar time. The detail explanation is in our paper [(arxiv)](https://arxiv.org/abs/1905.10709) | 2,512 |
jik0730/Deep-Mixed-Effect-Model-using-Gaussian-Processes | ['gaussian processes', 'time series'] | ['Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare'] | src/data_loader.py train.py src/means.py src/model.py src/utils.py evaluate.py utils.py evaluate train_single_epoch train_and_evaluate evaluate set_logger fetch_dataloaders_PhysioNet PhysioNetDataset test RNN MLP MLP_embed RNN_embed Warping_mean DMEGP get_mean_params get_kernel_params view model squeeze size tqdm sqrt forward_mean range format view info item step enumerate format upper info enumerate float setFormatter getLogger addHandler Formatter setLevel INFO FileHandler PhysioNetDataset DataLoader PhysioNetDataset dtype print shape DataLoader append named_parameters append named_parameters | # Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare + Ingyo Chung (KAIST), Saehoon Kim (AITRICS), Juho Lee (AITRICS), Kwang Joon Kim (Yonsei University College of Medicine), Sungju Hwang (KAIST, AITRICS), and Eunho Yang (KAIST, AITRICS) This repository contains implementations for our **AAAI 2020** (to appear) paper "Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare". # Abstract We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and prevention. Our proposed framework targets at making personalized and reliable predictions from time-series data, such as Electronic Health Records (EHR), by modeling two complementary components: i) a shared component that captures global trend across diverse patients and ii) a patient-specific component that models idiosyncratic variability for each patient. To this end, we propose a composite model of a deep neural network to learn complex global trends from the large number of patients, and Gaussian Processes (GP) to probabilistically model individual time-series given relatively small number of visits per patient. We evaluate our model on diverse and heterogeneous tasks from EHR datasets and show practical advantages over standard time-series deep models such as pure Recurrent Neural Network (RNN). <!-- # Citation If you think this repo is helpful, please cite as ``` @inproceedings{yun2019trimming, title={Trimming the $$\backslash$ell\_1 $ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning}, | 2,513 |
jik876/hifi-gan | ['speech synthesis'] | ['HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis'] | env.py train.py meldataset.py models.py inference.py utils.py inference_e2e.py build_env AttrDict get_mel load_checkpoint scan_checkpoint inference main main load_checkpoint scan_checkpoint inference load_wav dynamic_range_decompression_torch dynamic_range_compression_torch mel_spectrogram get_dataset_filelist dynamic_range_decompression spectral_normalize_torch spectral_de_normalize_torch MelDataset dynamic_range_compression discriminator_loss generator_loss Generator MultiPeriodDiscriminator ResBlock1 DiscriminatorS DiscriminatorP feature_loss MultiScaleDiscriminator ResBlock2 main train load_checkpoint get_padding init_weights save_checkpoint scan_checkpoint plot_spectrogram apply_weight_norm join copyfile makedirs print format load glob join load_checkpoint checkpoint_file remove_weight_norm eval load_state_dict output_dir to listdir input_wavs_dir makedirs seed join print add_argument device loads ArgumentParser manual_seed is_available parse_args AttrDict inference input_mels_dir read dynamic_range_compression_torch dynamic_range_decompression_torch librosa_mel_fn print stft squeeze matmul sqrt pad unsqueeze spectral_normalize_torch device to sum zip mean zip append item mean append generator fmax_for_loss discriminator_loss msd checkpoint_path generator_loss mpd zero_grad l1_loss training_epochs fmax DataLoader unsqueeze sampling_rate save_checkpoint fmin device MelDataset max seed mel_spectrogram squeeze get_dataset_filelist load_state_dict chain to range detach SummaryWriter format init_process_group num_mels eval hop_size manual_seed enumerate join time learning_rate ExponentialLR segment_size isdir backward print load_checkpoint AdamW win_size Variable add_scalar int set_epoch feature_loss parameters empty_cache scan_checkpoint n_fft step makedirs build_env config int checkpoint_path num_gpus spawn batch_size device_count train subplots draw close colorbar imshow normal_ __name__ __name__ weight_norm print format save | # HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis ### Jungil Kong, Jaehyeon Kim, Jaekyoung Bae In our [paper](https://arxiv.org/abs/2010.05646), we proposed HiFi-GAN: a GAN-based model capable of generating high fidelity speech efficiently.<br/> We provide our implementation and pretrained models as open source in this repository. **Abstract :** Several recent work on speech synthesis have employed generative adversarial networks (GANs) to produce raw waveforms. Although such methods improve the sampling efficiency and memory usage, their sample quality has not yet reached that of autoregressive and flow-based generative models. In this work, we propose HiFi-GAN, which achieves both efficient and high-fidelity speech synthesis. | 2,514 |
jinfengr/neural-tweet-search | ['information retrieval'] | ['Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search'] | train.py data_preprocess.py attention_model.py utils.py default_args.py add_attention_layer_with_query_weighting max_pooling_with_mask add_conv_layer keras_diagonal max_pooling add_attention_layer mean_pooling add_attention_layer_with_doc_weighting add_embed_layer repeat_vector create_attention_model elementwise_prod attention_weighting_prod mean_pooling_with_mask create_masks read_metadata save_data gen_data inject_word_weight read_urls read_sentences inject_ngram_weight sample_val_set load_data compute_overlap_feat read_relevance construct_vocab_emb get_best_args get_default_args load_best_args print_dataset evaluate set_args get_model_weights create_option_parser print_args main batch_generator get_word_vector word_tokenize merge_two_dicts get_ngrams split_sent clean_text unsplit_query normalize_unicode get_vector invert_dict print int_shape sum print Embedding Sequential add SpatialDropout1D len Convolution1D dropout_layer append conv_layer range GlobalMaxPooling1D Dropout setattr setattr doc_char_embedding_layer add_attention_layer_with_query_weighting setattr add_conv_layer print Sequential add_attention_layer extend url_char_embedding_layer add_attention_layer_with_doc_weighting add_embed_layer query_char_embedding_layer zip append Input range query_word_embedding_layer exists exists ones shape range shape range zeros shape range max zeros ones shape range read_metadata compute_overlap_feat max exists open create_masks pad_sequences read_sentences read_relevance invert_dict read_urls inject_ngram_weight load time merge_two_dicts print min extend inject_word_weight update int sorted format print set sample len load get_word_vector replace endswith print makedirs astype float32 write len close load_word2vec_format zeros open load open glob items list dump save open makedirs format communicate float Popen split print get_weights layers ceil int range print pprint print print dir test getattr type add_option OptionParser layers get_best_args delete SGD ReduceLROnPlateau set_weights get_default_args list ModelCheckpoint load_best_args Adam gen_data add RMSprop shape append create_attention_model get_weights range invert_dict predict format print_dataset save_data get_model_weights shuffle lower sample_val_set load_weights zip construct_vocab_emb zeros compile enumerate int remove merge_two_dicts set_args evaluate print EarlyStopping fit print_args load_data summary randint array len append join enumerate update copy items list max values lower sub endswith endswith list | # Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search This repo contains code and data for our neural tweet search paper published in [AAAI'19](https://arxiv.org/abs/1805.08159). Given a query, we aim to return the most relevant documents(tweets) by ranking their relevency. In social media search, the scenario is different as standard ad-hoc retrieval: shorter document length, less formal languages and multiple relevance source signals (e.g., URL, hashtag). We propose a hierarchical convolutional model to approach the hetergeneous relevance signals (tweet, URL, hashtag) at multiple perspectives, including character-, word-, phrase- and sentence-level modeling. Our model demonstrated significant gains on multiple twitter datasets against state-of-the-art neural ranking models. More details can be found in our paper. ## Requirements - Python 2.7 - Tensorflow or Theano (tested on TF 1.4.1) - Keras (tested on 2.0.5) ## Install - Download our repo: ``` | 2,515 |
jinghanY/geoPrivacyAlbum | ['combinatorial optimization'] | ['Protecting Geolocation Privacy of Photo Collections'] | VGG16/trainer.py VGG16/val.py VGG16/config.py Flickr/Heuristic/delFixed.py syntheticAlbums/IP/user.py syntheticAlbums/IP/topkProtect.py Flickr/IP/topKProtect.py syntheticAlbums/IP/diffBudget.py albums_fea/feaGet.py syntheticAlbums/Heuristic/topkProtect.py VGG16/model.py albums_fea/readList.py syntheticAlbums/IP/delFixed.py VGG16/loader.py VGG16/utils.py VGG16/train.py syntheticAlbums/Heuristic/delFixed.py Flickr/IP/user.py syntheticAlbums/IP/blackbox.py Flickr/Heuristic/topKProtect.py Flickr/IP/delFixed.py load main softmax h_p main softmax h main get_winner_rank IP main IP IP main softmax get_conf_photo main get_winner_rank softmax h_p main softmax h main IP main get_winner_rank IP main get_winner_rank IP IP main softmax get_conf_photo IP main softmax get_conf_photo trainload testload model train load ckpter batcher find valRes append int readlines split sum exp max shape sum softmax load int glob add_argument delete_prop write shape round ArgumentParser parse_args float listdir h_p flush shape sum softmax range h top_k len sum delete clear context time list sum get_values int solve add_constraint shape Model maximize array abs max range delete get_winner_rank transpose npz_num isnan IP minimize range softmax append get_conf_photo mean str delete_num album_size sum eps array delete_num_2 delete_num_1 to_float minimum resize_images to_int32 read_file decode_jpeg load get_batch batcher write mean swapOp run range getfeed append fetchOp | # Protecting Geolocation Privacy of Photo Collections (AAAI-2020) [[arXiv]](http://arxiv.org/abs/1912.02085) **extended version** A person's physical whereabouts is sensitve information that many desire to keep private. We consider the specific issue of location privacy as potentially revealed by posting photo collections by carefully pruning select photos from photo collections before these are posted publicly. <img src="figures/figure2.png" height="210" width="860"> ## Usage Clone our repo and create a sub-directory named ```dataset``` under the home directory. Download our datasets from ```release``` and put them under ```geoPrivacyAlbum/dataset```. Read the document of the dataset in the ```release```. Please read the ```Album Geolocation``` section and run the code to get the score matrices for synthetic albums before running ```Protection``` strategies. ## Image Geolocation The image geolocation is casted as an image classification problem. We use VGG16 for this task and get the image features. The code was adapted from [https://github.com/ayanc/tf-boilerplate](https://github.com/ayanc/tf-boilerplate). | 2,516 |
jingjin25/LFASR-FS-GAF | ['depth estimation'] | ['Deep Coarse-to-fine Dense Light Field Reconstruction with Flexible Sampling and Geometry-aware Fusion'] | train.py test_pretrained.py model/net_utils.py utils/dataset.py model/model_lfasr.py utils/util.py save_results main predict_y smooth_loss main reconstruction_loss Net_view Net_refine Net_LFASR construct_psv_grid construct_syn_grid AltFilter make_Altlayer_1D AltFilter_1D make_Altlayer TestDataFromHdf5 TrainDataFromHdf5 CropPatches_w MergePatches_w ycbcr2rgb compt_psnr warping crop_boundary Store_as_array psv_range test_dataset model_dir DataLoader test_path device arb_sample save_dir input_ind angular_in load_state_dict to format eval mkdir TestDataFromHdf5 save_img_dir load join print psv_step makedirs train_dataset angular_out len input_ind net_refine CropPatches_w MergePatches_w net_view from_numpy Tensor angular_out range uint8 format save_csv_name compare_ssim save_img ycbcr2rgb astype compt_psnr to_csv save append angular_out DataFrame range save_img_dir model reduce smooth zero_grad dataset_path save dataset num_source seed defaultdict StepLR Adam max_epoch title savefig append range resume_epoch plot TrainDataFromHdf5 close lr manual_seed train enumerate smooth_loss backward figure isfile reconstruction_loss step sqrt sum numel add gradient type_as stack floor append range cat type_as stack floor append range cat append AltFilter range append range AltFilter_1D grid_sample squeeze type_as shape stack unsqueeze floor shape list view deepcopy reshape transpose inv dot shape array to floor range device to range device mean | # LFASR-FS-GAF PyTorch implementation of **IEEE TPAMI 2020** paper: "**Deep Coarse-to-fine Dense Light Field Reconstruction with Flexible Sampling and Geometry-aware Fusion**". [[Paper]](https://ieeexplore.ieee.org/document/9204825) ## Requirements - Python 3.6 - PyTorch 1.3 - Matlab (for training/test data generation) ## Dataset We provide MATLAB code for preparing the training and test data. Please first download light field datasets, and put them into corresponding folders in `LFData`. ## Demo | 2,517 |
jingraham/neurips19-graph-protein-design | ['protein folding'] | ['Generative Models for Graph-Based Protein Design'] | data/build_chain_dataset.py experiments/train_s2s.py data/SPIN2/define_splits_SPIN2.py experiments/seq_only_test.py struct2seq/seq_model.py experiments/rocklin/mutations/collect_mutation_results.py struct2seq/noam_opt.py experiments/test_rocklin_mutations.py struct2seq/protein_features.py struct2seq/data.py struct2seq/__init__.py struct2seq/self_attention.py data/SPIN2/analyze_perplexity_SPIN.py data/ollikainen/split_dataset_by_ollikainen.py experiments/utils.py experiments/test_redesign.py experiments/seq_only_train.py experiments/test_s2s.py struct2seq/struct2seq.py data/mmtf_util.py mmtf_parse mmtf_fetch download_cached load_cath_topologies load_chain_set _perplexity _plot_log_probs _featurize _loss _plot_log_probs _featurize _loss _scores _loss _S_to_seq _plot_log_probs similarity _loss plot_log_probs loss_smoothed_reweight get_args setup_device_rng load_checkpoint loss_smoothed featurize loss_nll setup_model setup_cli_model StructureLoader StructureDataset SequenceDataset NoamOpt get_std_opt PositionalEncodings ProteinFeatures NeighborAttention PositionWiseFeedForward gather_nodes gather_nodes_t MPNNLayer gather_edges Normalize cat_neighbors_nodes TransformerLayer LanguageRNN SequenceModel Struct2Seq int format print urlopen dirname float makedirs parse_gzip download_cached mmtf_fetch index next range len print defaultdict append sum log enumerate T format arange clim add_subplot close colorbar tight_layout imshow savefig figure tick_params array yticks asarray astype float32 isnan stack pad array zeros to max enumerate len size sum view size sum view join sum parse_args add_argument ArgumentParser seed FloatTensor print device manual_seed is_available set_default_tensor_type print sum to format restore get_args setup_device_rng load_checkpoint setup_model vars print format load load_state_dict asarray astype float32 isnan shuffle_subset stack pad array zeros to max enumerate len T format arange clim add_subplot close colorbar tight_layout imshow savefig figure tick_params array yticks NLLLoss size sum view size float sum size float sum softmax gather size expand gather size view expand gather size expand cat gather_nodes | # Graph-Based Protein Design This repo contains code for [Generative Models for Graph-Based Protein Design](https://papers.nips.cc/paper/9711-generative-models-for-graph-based-protein-design) by John Ingraham, Vikas Garg, Regina Barzilay and Tommi Jaakkola, NeurIPS 2019. Our approach 'designs' protein sequences for target 3D structures via a graph-conditioned, autoregressive language model: <p align="center"><img src="data/scheme.png" width="500"></p> ## Overview * `struct2seq/` contains model code * `experiments/` contains scripts for training and evaluating the model * `data/` contains scripts for building and processing datasets in the paper ## Requirements * Python >= 3.0 | 2,518 |
jinhan/tacotron2-vae | ['style transfer', 'speech synthesis'] | ['Learning latent representations for style control and transfer in end-to-end speech synthesis'] | logger.py text/cleaners.py text/korean.py plotting_utils.py train.py app.py text/numbers_.py text/symbols.py CoordConv.py layers.py utils.py text/cmudict.py audio_processing.py data_utils.py loss_scaler.py distributed.py multiproc.py synthesizer.py hparams.py text/ko_dictionary.py modules.py model.py text/__init__.py fp16_optimizer.py loss_function.py stft.py view_method generate_api_response index API send_uploads generate_audio_response send_audio send_css send_js griffin_lim window_sumsquare dynamic_range_decompression dynamic_range_compression CoordConv2d CoordConv1d AddCoords CoordConv3d TextMelCollate TextMelLoader DistributedDataParallel _unflatten_dense_tensors _flatten_dense_tensors apply_gradient_allreduce FP16_Module conversion_helper fp32_to_fp16 fp16_to_fp32 FP16_Optimizer create_hparams ConvNorm2D LinearNorm ConvNorm TacotronSTFT Tacotron2Logger Tacotron2Loss_VAE LossScaler DynamicLossScaler Tacotron2 Decoder Postnet Prenet Encoder LocationLayer Attention ReferenceEncoder VAE_GST plot_gate_outputs_to_numpy plot_spectrogram_to_numpy plot_scatter save_figure_to_numpy plot_alignment_to_numpy STFT Synthesizer validate load_model load_checkpoint save_checkpoint batchnorm_to_float prepare_dataloaders init_distributed prepare_directories_and_logger warm_start_model train reduce_tensor get_mask_from_lengths load_wav_to_torch to_gpu load_filepaths_and_text str2bool add_postfix makedirs lowercase english_cleaners expand_abbreviations collapse_whitespace basic_cleaners korean_cleaners convert_to_ascii transliteration_cleaners expand_numbers _parse_cmudict _get_pronunciation CMUDict load_symbols_2 normalize_upper test_normalize load_symbols_1 jamo_to_korean normalize_with_dictionary number_to_korean load_symbols_4 is_lead load_symbols_3 normalize normalize_english is_vowel tokenize _get_text_from_candidates compare_sentence_with_jamo normalize_number normalize_quote get_mode tokenizer_fn is_tail normalize_numbers _expand_dollars _expand_ordinal _expand_decimal_point _expand_number _remove_commas text_to_sequence change_symbol _clean_text _symbols_to_sequence _should_keep_symbol sequence_to_text _arpabet_to_sequence join format replace synthesize print dirname hexdigest makedirs join read format replace synthesize print b64encode dirname float hexdigest makedirs data loads get float 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 tuple isinstance list parse info HParams values 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 list subplots xlabel draw close ylabel tight_layout scatter save_figure_to_numpy range len subplots draw close save_figure_to_numpy scatter numpy legend zip argmax enumerate children isinstance _BatchNorm float all_reduce clone print device_count set_device init_process_group validation_files n_frames_per_step DataLoader training_files TextMelCollate TextMelLoader join chmod Tacotron2Logger makedirs apply_gradient_allreduce min distributed_run half batchnorm_to_float fp16_run float cuda print format load load_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 FP16_Optimizer seed load_model clip_fp32_grads Adam epochs grad_clip_thresh parse_batch warm_start_model Tacotron2Loss_VAE 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 parameters init_distributed step len arange byte item read is_available contiguous cuda print format rsplit sub lowercase collapse_whitespace lowercase convert_to_ascii collapse_whitespace lowercase expand_abbreviations collapse_whitespace convert_to_ascii expand_numbers ko_tokenize append _get_pronunciation sub split split is_vowel is_tail is_lead append h2j get_mode print normalize list hangul_to_jamo normalize_number normalize_english replace normalize_quote strip sub normalize_with_dictionary join any compile sub group all sub normalize_with_dictionary int str join list replace len literal_eval any sub startswith keys enumerate split print normalize group split int group sub change_symbol group match append len cleaner getattr | # tacotron2-vae ## Overview  - Generate emotional voices by receiving text and emotional label as input - Korean Version of ["Learning Latent Representations for Style Control and Transfer in End-to-end Speech Synthesis"](https://arxiv.org/pdf/1812.04342.pdf) ## Data 1. Dataset * Korean Speech Emotion Dataset ([more info](http://aicompanion.or.kr/kor/main/)) * Single Female Voice Actor recorded six diffrent emotions(neutral, happy, sad, angry, disgust, fearful), each with 3,000 sentences. * For training, I used four emotions(neutral,happy,sad,angry), total 21.25 hours | 2,519 |
jixunbo/LightMBN | ['person re identification'] | ['Lightweight Multi-Branch Network for Person Re-Identification'] | data_v1/gta.py data_v2/utils.py model/resnet50_ibn.py data_v2/datasets/utils.py utils/random_erasing.py model/c.py data_v2/datasets/image/market1501.py data_v2/datasets/video/ilidsvid.py loss/ranked_loss.py data_v2/datamanager.py data_v2/datasets/image/cuhk03_labeled.py data_v2/datasets/image/cuhk03_splited.py data_v2/datasets/image/grid.py data_v2/datasets/video/dukemtmcvidreid.py model/attention.py utils/visualize_actmap.py data_v2/datasets/video/mars.py model/pyramid.py data_v2/datasets/image/msmt17.py data_v1/dukemtmc.py optim/n_adam.py model/__init__.py data_v2/datasets/video/prid2011.py optim/warmup_cosine_scheduler.py loss/__init__.py model/mcn.py data_v2/datasets/image/mot17.py model/bnneck.py loss/center_loss.py loss/osm_caa_loss.py main.py data_v2/datasets/image/viper.py model/lmbn_n_no_drop.py data_v2/datasets/image/ilids.py data_v2/datasets/image/cuhk03_detected.py loss/focal_loss.py model/resnet50.py data_v2/datasets/__init__.py model/g_c.py optim/__init__.py utils/model_complexity.py data_v2/datasets/image/cuhk01.py data_v2/datasets/image/dukemtmcreid.py model/p.py model/lmbn_r.py data_v2/__init__.py model/mgn.py data_v2/datasets/video/__init__.py engine_v3.py loss/triplet.py data_v2/datasets/image/prid.py engine_v1.py data_v2/transforms.py utils/functions.py optim/nadam.py utils/visualize_rank.py data_v1/__init__.py utils/rank_cylib/test_cython.py data_v1/sampler.py utils/re_ranking.py data_v2/datasets/dataset.py engine_v2.py data_v2/datasets/image/sensereid.py loss/grouploss.py utils/utility.py loss/multi_similarity_loss.py model/se_resnet.py data_v2/sampler.py model/osnet.py utils/rank_cylib/setup.py model/lmbn_r_no_drop.py data_v2/datasets/image/cuhk03.py model/pcb.py data_v2/datasets/image/cuhk02.py option.py data_v1/market1501.py model/g_p.py optim/warmup_scheduler.py model/lmbn_n_drop_no_bnneck.py data_v2/datasets/image/__init__.py model/lmbn_n.py Engine Engine Engine DukeMTMC GTA Market1501 RandomIdentitySampler a_RandomIdentitySampler RandomSampler Data ImageDataManager DataloaderX VideoDataManager DataManager RandomIdentitySampler build_train_sampler RandomErasing Cutout ColorAugmentation Random2DTranslation RandomPatch build_transforms check_isfile collect_env_info read_json download_url set_random_seed write_json read_image mkdir_if_missing check_isfile collect_env_info read_json download_url set_random_seed write_json read_image mkdir_if_missing register_video_dataset init_image_dataset register_image_dataset init_video_dataset CUHK01 CUHK02 CUHK03_Detected CUHK03_Labeled CUHK03_splited DukeMTMCreID GRID Market1501 MOT17 MSMT17 PRID SenseReID VIPeR DukeMTMCVidReID iLIDSVID Mars PRID2011 CenterLoss FocalLoss focal_loss GroupLoss _replicator dynamics MultiSimilarityLoss OSM_CAA_Loss rank_loss RankedLoss normalize_rank euclidean_dist_rank CrossEntropyLabelSmooth TripletLoss TripletSemihardLoss make_loss LossFunction Dual_Module BatchRandomErasing CAM_Module PAM_Module BatchFeatureErase_Top BatchDrop BatchDropTop SE_Module BNNeck ClassBlock BNNeck3 C G_C G_P LMBN_n LMBN_n_drop_no_bnneck LMBN_n_no_drop LMBN_r LMBN_r_no_drop BNNeck ClassBlock MCN new_BNNeck make_model MGN Conv1x1 osnet_x0_5 osnet_x1_0 OSNet init_pretrained_weights Conv3x3 Conv1x1Linear ConvLayer ChannelGate osnet_x0_25 LightConv3x3 osnet_x1_25 OSBlock osnet_ibn_x1_0 osnet_x0_75 P PCB Pyramid load_ckpt ResNet50 BatchDrop resnet152_ibn_a resnet50_ibn_a Bottleneck_IBN ResNet_IBN IBN resnet101_ibn_a se_resnet_18 se_resnet_34 se_resnet_50 SENet se_resnet_152 se_resnet_101 Bottleneck conv3x3 BasicBlock make_model Nadam NAdam WarmupCosineAnnealingLR WarmupMultiStepLR make_optimizer make_scheduler mean_ap cmc evaluate_py _unique_sample compute_mAP_baseline cmc_baseline evaluation hook_maxpool3d hook_adapavgpool1d hook_leakyrelu hook_relu hook_avgpool2d hook_adapmaxpool2d hook_batchnormNd hook_layernorm hook_maxpool1d hook_adapmaxpool3d compute_model_complexity hook_adapavgpool3d hook_instancenormNd hook_convNd hook_avgpool1d hook_avgpool3d hook_adapmaxpool1d _get_flops_counter _ntuple hook_groupnorm hook_maxpool2d hook_linear hook_adapavgpool2d RandomErasing Cutout batch_torch_topk re_ranking batch_euclidean_distance batch_v re_ranking_gpu euclidean_distance k_reciprocal_neigh checkpoint main visactmap _parse_data_for_train extract_feature _parse_data_for_eval visualize_ranked_results main RandomIdentitySampler RandomSampler int format isinstance print Compose Normalize round makedirs format warn isfile dirname mkdir_if_missing seed manual_seed_all manual_seed print format write urlretrieve convert get_pretty_env_info list keys list keys list keys list keys size mean pow sum log _replicator matmul expand_as t sqrt addmm_ expand ne exp mul lt clamp size add div eq float sum range load join list items update state_dict format print warn _get_torch_home OrderedDict load_state_dict startswith append download makedirs init_pretrained_weights OSNet init_pretrained_weights OSNet init_pretrained_weights OSNet init_pretrained_weights OSNet init_pretrained_weights OSNet init_pretrained_weights load format print load_state_dict ResNet_IBN load_url load_state_dict ResNet_IBN load_url load_state_dict ResNet_IBN load_url load_state_dict SENet SENet SENet SENet SENet list format model lower import_module write_log DataParallel nGPU device to range str list parts model print tuple map Adam SGD RMSprop parameters weight_decay getattr lr append range pop list cosine_annealing WarmupCosineAnnealingLR StepLR float MultiStepLR w_cosine_annealing WarmupMultiStepLR CosineAnnealingLR epochs gamma warmup split invert format asarray print cumsum astype argsort shape mean int32 append sum range zeros list items choice asarray arange defaultdict astype argsort shape _unique_sample int32 zip append zeros range enumerate len asarray arange astype average_precision_score argsort shape int32 append range flatten argwhere in1d zero_ range len asarray arange setdiff1d astype argsort shape intersect1d int32 zero_ argwhere append compute_mAP_baseline float range groups numel in_channels item kernel_size numel kernel_size numel _pair item _triple kernel_size numel item kernel_size numel kernel_size numel _pair item _triple kernel_size numel item ceil size numel output_size list output_size numel item Tensor _pair list output_size numel _triple item Tensor ceil size numel output_size list output_size numel item Tensor _pair list output_size numel _triple item Tensor numel numel affine numel elementwise_affine numel in_features numel int remove defaultdict flops format namedtuple model print rand training apply params append train sum cuda is_cuda zeros_like around max list exp transpose append sum range concatenate astype mean unique minimum int float32 argpartition k_reciprocal_neigh zeros len t matmul cpu euclidean_distance append empty_cache range cat t euclidean_distance append empty_cache numpy range cat list exp squeeze astype float32 tqdm unsqueeze euclidean_distance zeros numpy max range zeros_like around cuda list append range format size batch_v mean unique zeros batch_torch_topk int time minimum collect print tqdm k_reciprocal_neigh empty_cache numpy len imwrite model clamp_ floor resize cuda max list basename view ones transpose normalize COLORMAP_JET sum cat format size astype eval zip keys enumerate join uint8 print applyColorMap makedirs min cpu numpy len resume_from_checkpoint cuda make_optimizer dir Model width visactmap format height load_pretrained_weights write_log is_available make_scheduler setattr checkpoint join ImageDataManager pre_train testloader join BORDER_CONSTANT format basename imwrite copyMakeBorder _cp_img_to print ones FONT_HERSHEY_SIMPLEX argsort shape resize mkdir_if_missing imread range len norm arange view model FloatTensor size extend index_select div _parse_data_for_eval expand_as cpu to cat query data_type visualize_ranked_results device re_rank re_ranking transpose query_loader test_loader eval gallery dot numpy | # Lightweight Multi-Branch Network for Person Re-Identification Pytorch implementation for the paper "Lightweight Multi-Branch Network for Person Re-Identification" [](https://arxiv.org/abs/2101.10774)  This repo supports easy dataset preparation, including Market-1501, DukeMTMC-ReID, CUHK03, MOT17, sota deep neural networks and various options(tricks) for reid, easy combination of different kinds of loss function, end-to-end training and evaluation and less package requirements. List of functions - Warm up cosine annealing learning rate - Random erasing augmentation - Cutout augmentation - Drop Block and Batch Erasing - Label smoothing(Cross Entropy loss) | 2,520 |
jiyanggao/TALL | ['temporal localization'] | ['TALL: Temporal Activity Localization via Language Query'] | vs_multilayer.py ctrl_model.py util/cnn.py dataset.py main.py CTRL_Model TrainingDataSet TestingDataSet calculate_nIoL calculate_IoU compute_ap do_eval_slidingclips run_training nms_temporal dense_to_one_hot calculate_IoU main compute_IoU_recall_top_n_forreg vs_multilayer fc_layer softmax_loss_layer pooling_layer deconv_layer deconv_relu_layer conv_relu_layer conv_layer fc_relu_layer zeros arange average_precision_score dense_to_one_hot append array range list map sub append range len float nms_temporal range calculate_IoU str print reshape write load_movie_slidingclip run zeros float compute_IoU_recall_top_n_forreg movie_names range len CTRL_Model open run_training conv2d bias_add conv_layer relu as_list conv2d_transpose bias_add deconv_layer relu max_pool as_list reshape xw_plus_b matmul fc_layer relu as_list batch_size reshape concat sparse_to_dense range | ## TALL: Temporal Activity Localization via Language Query This is the repository for our ICCV 2017 paper [_TALL: Temporal Activity Localization via Language Query_](https://arxiv.org/abs/1705.02101). ### Visual Features on TACoS Download the C3D features for [training set](https://drive.google.com/file/d/1zQp0aYGFCm8PqqHOh4UtXfy2U3pJMBeu/view?usp=sharing) and [test set](https://drive.google.com/file/d/1zC-UrspRf42Qiu5prQw4fQrbgLQfJN-P/view?usp=sharing) of TACoS dataset. Modify the path to feature folders in main.py ### Sentence Embeddings on TACoS Download the Skip-thought sentence embeddings and sample files from [here](https://drive.google.com/file/d/1HF-hNFPvLrHwI5O7YvYKZWTeTxC5Mg1K/view?usp=sharing) of TACoS Dataset, and put them under exp_data folder. ### Reproduce the results on TACoS `python main.py` ### Charades-STA anno download The sentence temporal annotations on [Charades](http://allenai.org/plato/charades/) dataset are available here: [train](https://drive.google.com/file/d/1ZjG7wJpPSMIBYnW7BAG2u9VVEoNvFm5c/view?usp=sharing), [test](https://drive.google.com/file/d/1QG4MXFkoj6JFU0YK5olTY75xTARKSW5e/view?usp=sharing). The format is "[video name] [start time] [end time]##[sentence]". You may want to generate the skip-thought embeddings and C3D features on Charades-STA, and modify the codes slightly to reproduce the experiments. | 2,521 |
jiyfeng/disco4textcat | ['text categorization'] | ['Neural Discourse Structure for Text Categorization'] | pythoncode/bracket_reader.py pythoncode/yelp_processing.py pythoncode/rst_reader.py BracketReader test Elem RSTReader test get_vocab check_token get_docdict get_allbracketsfiles write_docs parse_fname Doc main load_labels refine_with_vocab print read convert BracketReader textrelas textdepths segtexts RSTReader childnodes split float replace print format len textrelas read int format replace print pnodes parse_fname segtexts Doc RSTReader items sorted defaultdict list check_token print len split append check_token split format replace print close open get_vocab format get_docdict get_allbracketsfiles write_docs load_labels | # Disco4textcat The code used for my ACL paper **Neural Discourse Structure for Text Categorization** ## Compile the code TODO | 2,522 |
jizongFox/deep-clustering-toolbox | ['semantic segmentation'] | ['Deep Co-Training for Semi-Supervised Image Segmentation'] | deepclustering/meters/_metric.py playground/structured-consistency-loss/main.py test/deepclustering/dataset/classification/test_semi_loaders.py playground/PaNN/toy_example/augment.py deepclustering/utils/yaml_parser.py deepclustering/postprocessing/clip_images.py deepclustering/meters/_meterinterface.py playground/swa_cifar_benchmark/arch/googlenet.py deepclustering/dataset/classification/mnist.py test/deepclustering/meters/test_Averagewith_std.py playground/swa_cifar_benchmark/arch/senet.py deepclustering/meters2/individual_meters/torchnet/meter/averagevaluemeter.py deepclustering/dataset/classification/stl10.py playground/swa_cifar_benchmark/arch/wideresnet.py deepclustering/viewer/batchviewer.py deepclustering/dataloader/_utils/pin_memory.py deepclustering/arch/classification/IIC/net5g.py deepclustering/arch/segmentation/epsnetv2/cnn_utils.py deepclustering/meters/kappa.py playground/IIC_VAT/VATIICTrainer.py playground/subspaceClustering/run.py deepclustering/meters2/individual_meters/iou.py test/deepclustering/model/test_ema.py test/deepclustering/model/test_interface.py deepclustering/dataset/classification/svhn.py deepclustering/meters2/meter_interface.py deepclustering/arch/classification/preresnet.py deepclustering/meters2/individual_meters/torchnet/meter/__init__.py deepclustering/utils/io.py deepclustering/arch/classification/dummy.py deepclustering/writer/dataframedrawer.py deepclustering/writer/draw_csv.py playground/PaNN/toy_example/utils.py test/deepclustering/dataset/test_toyexample.py playground/PaNN/toy_example/trainer.py deepclustering/meters2/historicalContainer/__init__.py deepclustering/schedulers/customized_scheduler.py deepclustering/meters2/individual_meters/kappa.py deepclustering/postprocessing/plot.py deepclustering/model/ema.py deepclustering/dataset/segmentation/_metainfoGenerator.py deepclustering/utils/download_unzip_helper.py test/deepclustering/augment/test_interface.py playground/subspaceClustering/dataset.py deepclustering/arch/segmentation/deeplab/resnet.py deepclustering/loss/loss.py deepclustering/meters2/individual_meters/torchnet/meter/apmeter.py deepclustering/meters2/individual_meters/averagemeter.py deepclustering/schedulers/lr_scheduler.py deepclustering/dataloader/_utils/worker.py playground/swa_cifar_benchmark/arch/mobilenetv2.py test/deepclustering/utils/test_fsmGenerator.py deepclustering/arch/classification/IIC/vgg.py deepclustering/trainer/_hooks.py setup.py test/deepclustering/augment/test_tensor_aug.py deepclustering/decorator/decorator.py deepclustering/schedulers/__init__.py deepclustering/dataset/classification/utils.py playground/swa_cifar_benchmark/arch/__init__.py deepclustering/loss/IMSAT_loss.py deepclustering/dataset/classification/cifar_helper.py deepclustering/meters2/utils/__init__.py deepclustering/postprocessing/utils.py deepclustering/arch/segmentation/attention_unet.py deepclustering/dataset/semi_helper.py deepclustering/decorator/lazy_load_checkpoint.py deepclustering/meters2/individual_meters/torchnet/meter/meter.py deepclustering/trainer/__init__.py playground/IMSAT/IMSATTrainer.py deepclustering/dataset/segmentation/toydataset.py deepclustering/dataset/segmentation/prostate_dataset.py deepclustering/decorator/deprecation_helper.py deepclustering/meters2/individual_meters/torchnet/meter/aucmeter.py deepclustering/arch/classification/vat_network.py deepclustering/dataset/segmentation/toydataset_helper.py deepclustering/viewer/Viewer.py playground/IMSAT/mnist.py playground/PaNN/toy_example/dataset.py deepclustering/dataset/segmentation/_medicalSegmentationDataset.py deepclustering/meters2/individual_meters/torchnet/meter/movingaveragevaluemeter.py deepclustering/dataset/segmentation/__init__.py deepclustering/loss/__init__.py playground/subspaceClustering/subspace_trainer.py deepclustering/dataset/segmentation/spleen_dataset.py deepclustering/meters/dicemeter.py deepclustering/model/models.py test/deepclustering/augment/test_sychronized_augmentation.py playground/PaNN/toy_example/toy_example.py test/deepclustering/augment/test_tensorCutout.py test/deepclustering/meters/test_haussdorf.py deepclustering/utils/general.py deepclustering/utils/multiprocessing.py deepclustering/arch/segmentation/__init__.py test/deepclustering/meters/test_new_interface.py deepclustering/augment/sychronized_augment.py deepclustering/arch/classification/IIC/residual.py deepclustering/optim/adabound.py deepclustering/arch/segmentation/deeplab/deeplabv2.py deepclustering/arch/classification/IIC/net5g_multi_head.py deepclustering/viewer/realtime_viewer.py deepclustering/dataset/segmentation/_patient_sampler.py deepclustering/utils/warnings.py deepclustering/dataloader/_utils/fetch.py playground/swa_cifar_benchmark/arch/preact_resnet.py playground/IMSAT/train_IMSAT.py deepclustering/arch/segmentation/network.py deepclustering/arch/segmentation/epsnetv2/SegmentationModel.py playground/swa_cifar_benchmark/arch/resnext.py deepclustering/meters/_utils.py deepclustering/model/convert2apex.py deepclustering/writer/__init__.py deepclustering/meters2/individual_meters/surface_meter.py deepclustering/meters/cache.py deepclustering/meters2/individual_meters/surface_distance.py deepclustering/dataloader/sampler.py deepclustering/arch/segmentation/deeplab/deeplabv3plus.py deepclustering/manager.py deepclustering/meters2/individual_meters/torchnet/meter/msemeter.py playground/swa_cifar_benchmark/trainer.py test/deepclustering/GradualWarmpScheduler/test_scheduler.py test/deepclustering/writer/test_dataframedrawer.py deepclustering/schedulers/polynomiallr.py deepclustering/optim/radam.py playground/subspaceClustering/admm.py playground/MINE/MutualInformationEstmator.py deepclustering/utils/VAT.py deepclustering/arch/segmentation/deeplab/msc.py deepclustering/meters/instance.py playground/swa_cifar_benchmark/arch/large_conv.py deepclustering/postprocessing/report.py deepclustering/viewer/__init__.py script/train_IIC_Twohead.py deepclustering/augment/ndim_transforms.py deepclustering/utils/segmentation/utils.py playground/swa_cifar_benchmark/arch/dpn.py deepclustering/arch/segmentation/deeplab/deeplabv3.py playground/swa_cifar_benchmark/arch/pnasnet.py deepclustering/dataloader/dataloader_helper.py playground/swa_cifar_benchmark/my_scheduler.py playground/swa_cifar_benchmark/arch/lenet.py deepclustering/arch/segmentation/joseent/layers.py deepclustering/meters2/individual_meters/dicemeter.py deepclustering/dataloader/_utils/signal_handling.py deepclustering/augment/tensor_augment.py deepclustering/dataloader/distributed.py deepclustering/dataloader/__init__.py deepclustering/arch/segmentation/deeplab/enet.py deepclustering/dataset/classification/mnist_helper.py playground/MI/main.py deepclustering/dataloader/_utils/collate.py deepclustering/loss/kl_losses.py playground/swa_cifar_benchmark/arch/shufflenetv2.py deepclustering/__init__.py playground/semisup/semisup.py deepclustering/meters2/storage_interface.py deepclustering/meters2/historicalContainer/historical_container.py deepclustering/meters2/individual_meters/torchnet/meter/classerrormeter.py deepclustering/meters2/individual_meters/torchnet/meter/timemeter.py deepclustering/arch/__init__.py deepclustering/dataset/clustering_helper.py deepclustering/loss/IID_losses.py playground/MI/__init__.py test/deepclustering/dataset/classification/test_cifar10ClusteringDataloaders.py test/deepclustering/loss/test_dice_loss.py test/deepclustering/loss/test_loss.py deepclustering/dataloader/dataloader.py playground/MI/toy_example.py deepclustering/dataset/segmentation/wMH_dataset.py deepclustering/utils/adversarial_generator.py deepclustering/meters/__init__.py test/deepclustering/dataset/segmentation/test_iSeg2017_loader.py deepclustering/schedulers/warmup_scheduler.py playground/swa_cifar_benchmark/arch/efficientnet.py deepclustering/utils/classification/assignment_mapping.py deepclustering/decorator/cache_decorator.py deepclustering/model/__init__.py test/deepclustering/loss/test_imsat.py deepclustering/trainer/_trainer.py playground/auto_encoder/mnist.py deepclustering/dataset/classification/__init__.py deepclustering/meters2/individual_meters/_metric.py deepclustering/writer/SummaryWriter.py deepclustering/arch/segmentation/enet.py test/deepclustering/augment/test_dataagument.py deepclustering/meters2/individual_meters/cache.py deepclustering/augment/__init__.py playground/IIC_VAT/main.py deepclustering/method/__init__.py playground/swa_cifar_benchmark/arch/vgg.py deepclustering/meters/averagemeter.py deepclustering/arch/classification/IIC/net6c.py deepclustering/arch/segmentation/joseent/networks.py test/deepclustering/dataset/segmentation/test_patient_sampler.py deepclustering/dataset/segmentation/mmwhs_dataset.py test/deepclustering/dataset/segmentation/test_wmh_loader.py deepclustering/decorator/__init__.py playground/swa_cifar_benchmark/swa_main.py playground/swa_cifar_benchmark/arch/resnet.py deepclustering/dataloader/dataset.py deepclustering/meters/confusionmatrix.py deepclustering/meters2/individual_meters/instance.py playground/IMSAT/mnist_helper.py deepclustering/postprocessing/report2.py playground/PaNN/main.py deepclustering/arch/segmentation/deeplab/__init__.py deepclustering/dataset/classification/svhn_helper.py deepclustering/writer/_dataframedrawercallback.py deepclustering/arch/classification/__init__.py deepclustering/meters/iou.py deepclustering/augment/pil_augment.py playground/Mixup/mixup_main.py deepclustering/arch/classification/IIC/baselines/__init__.py playground/subspaceClustering/arch.py playground/subspaceClustering/subclassClustering.py deepclustering/arch/segmentation/joseent/__init__.py playground/swa_cifar_benchmark/arch/mobilenet.py test/deepclustering/dataset/segmentation/test_prostate_dataset.py deepclustering/utils/typecheckconvert.py deepclustering/dataloader/_utils/__init__.py deepclustering/arch/segmentation/epsnetv2/Model.py deepclustering/utils/segmentation/__init__.py deepclustering/arch/segmentation/epsnetv2/__init__.py deepclustering/dataset/classification/stl10_helper.py test/deepclustering/utils/test_yaml_parser.py deepclustering/optim/__init__.py deepclustering/dataset/segmentation/iSeg2017_dataset.py playground/PaNN/trainer.py deepclustering/dataset/classification/cifar.py deepclustering/meters2/__init__.py test/deepclustering/dataset/segmentation/test_acdc_loader.py deepclustering/dataset/segmentation/acdc_dataset.py deepclustering/meters2/individual_meters/hausdorff.py playground/swa_cifar_benchmark/arch/shufflenet.py test/deepclustering/trainer/test_trainer.py deepclustering/arch/classification/IIC/baselines/triplets.py deepclustering/arch/classification/IIC/net6c_two_head.py test/deepclustering/viewer/test_viewer.py deepclustering/dataset/classification/vision.py deepclustering/arch/classification/IIC/net5g_two_head.py deepclustering/meters2/individual_meters/torchnet/meter/mapmeter.py playground/MINE/__init__.py deepclustering/utils/__init__.py deepclustering/meters/hausdorff.py deepclustering/meters2/individual_meters/__init__.py deepclustering/arch/segmentation/vnet.py test/deepclustering/meters/test_meters.py deepclustering/meters2/individual_meters/confusionmatrix.py deepclustering/meters2/individual_meters/general_dice_meter.py deepclustering/arch/classification/vgg16_bn.py deepclustering/meters2/individual_meters/torchnet/meter/confusionmeter.py test/deepclustering/arch/test_arch_interface.py deepclustering/arch/classification/IMSAT/imsat.py playground/swa_cifar_benchmark/arch/densenet.py deepclustering/loss/dice_loss.py deepclustering/dataset/__init__.py ConfigManger ModelMode get_arch weights_init PlaceholderNet Dummy PreResNet Bottleneck PreResNet110 PreResNet164 conv3x3 BasicBlock VATNetwork init_weights vgg16_bn ClusterNet5gTrunk ClusterNet5gHead ClusterNet5g ClusterNet5gMultiHead ClusterNet5gMultiHeadHead ClusterNet5gTwoHeadHead ClusterNet5gTwoHead ClusterNet6cHead ClusterNet6c ClusterNet6cTrunk ClusterNet6cTwoHead ClusterNet6cTwoHeadHead ResNet conv3x3 ResNetTrunk BasicBlock VGGNet VGGTrunk TripletsNet5gHead TripletsNet6cHead TripletsNet6c TripletsNet5g IMSATHeader IMSATNet conv_block UNet_Attention Attention_block up_conv Decoder Encoder Enet InitialBlock BottleNeck UNetEnc SegNet PSPNet UNetDec_bn UNet_bn UNetDec UNet UNetEnc_bn FCN16 FCN8 SegNetEnc FCN32 PSPDec DownTransition UpTransition _make_nConv InputTransition passthrough ContBatchNorm3d LUConv VNet ELUCons OutputTransition _ASPPModule DeepLabV2 DeepLabV3 _ASPPModule DeepLabV3Plus UpsamplingBottleneck DownsamplingBottleneck ENet InitialBlock RegularBottleneck MSC _Bottleneck _ResBlock _ConvBatchNormReLU DeepLabV3Plus_ResNet101_MSC DeepLabV2S_ResNet101_MSC init_weights DeepLabV2_ResNet101_MSC DeepLabV3_ResNet101_MSC CDilated CDilatedB PSPModule BR C CBR CB EESP DownSampler EESPNet EESPNet_Seg conv_block convBatch conv_block_3 maxpool conv_block_Asym conv_block_1 conv_block_3_3 upSampleConv conv downSampleConv conv_decod_block Conv_residual_conv BottleNeckDownSampling Dummy ENet BottleNeckNormal_Asym CorstemNet BottleNeckDownSamplingDilatedConvLast weights_init BottleNeckUpSampling BottleNeckDownSamplingDilatedConv BottleNeckNormal Transformer _recover_ignore_index ToTensor blur_boundary GaussianNoise RandomRotate ElasticDeformation RangeNormalize RandomFlip RandomRotate90 get_transformer AbstractLabelToBoundary Normalize StandardLabelToBoundary RandomContrast LabelToAffinities LabelToBoundaryAndAffinities RandomLabelToAffinities Identity ToLabel PILCutout CenterCrop RandomRotation ToTensor SobelProcess RandomVerticalFlip RandomApply Resize RandomCrop RandomChoice RandomHorizontalFlip Identity Img2Tensor RandomTransforms FixRandomSeed SequentialWrapper CenterCrop Compose RandomVerticalFlip Resize RandomCrop RandomHorizontalFlip TensorCutout GaussianNoise TransformInterface _TransformInterface _DatasetKind _InfiniteConstantSampler _MultiProcessingDataLoaderIter _BaseDataLoaderIter DataLoader _SingleProcessDataLoaderIter background DataIter BackgroundGenerator random_split ConcatDataset IterableDataset Subset TensorDataset ChainDataset CombineDataset Dataset DistributedSampler _InfiniteRandomIterator SubsetRandomSampler WeightedRandomSampler RandomSampler BatchSampler InfiniteRandomSampler SequentialSampler default_collate default_convert _MapDatasetFetcher _BaseDatasetFetcher _IterableDatasetFetcher pin_memory _pin_memory_loop _set_SIGCHLD_handler ManagerWatchdog _worker_loop get_worker_info WorkerInfo _set_python_exit_flag ClusterDatasetInterface SemiDataSetInterface MedicalDatasetSemiInterface _draw_indices CIFAR100 CIFAR10 Cifar10ClusteringDatasetInterface Cifar10SemiSupervisedDatasetInterface MNIST read_label_file get_int read_image_file MNISTClusteringDatasetInterface MNISTSemiSupervisedDatasetInterface STL10 STL10DatasetInterface SVHN SVHNSemiSupervisedDatasetInterface SVHNClusteringDatasetInterface list_files download_url check_integrity gen_bar_updater list_dir makedir_exist_ok StandardTransform VisionDataset ACDCSemiInterface ACDCDataset ISeg2017SemiInterface ISeg2017Dataset MMWHSDataset MMWHSSemiInterface ProstateSemiInterface ProstateDataset SpleenSemiInterface SpleenDataset Cls_ShapesDataset ShapesDataset Seg_ShapesDataset Ins_ShapesDataset ToyExampleInterFace WMHSemiInterface WMHDataset default_transform MedicalImageSegmentationDataset MedicalImageSegmentationDatasetWithMetaInfo allow_extension classSizeCalulator getImage_GT SubMedicalDatasetBasedOnIndex PatientSampler MultiProcessCache SingleProcessCache FixRandomSeed processed _extract_bn_modules TimeBlock _disable_tracking_bn_stats_pytoch_el_1_1_0 threaded_ threaded timethis _disable_tracking_bn_stats export WaitThreadsEnd SuppressStdout convert_params DeprecationWarning warn_deprecated warn deprecated lazy_load_checkpoint _extract_variable_from_kwargs _extract_variable_from_args GeneralizedDiceLoss MetaDice IIDLoss compute_joint MultualInformaton_IMSAT Perturbation_Loss SimplexCrossEntropyLoss Entropy KL_div _check_reduction_params JSD_div SimplexCrossEntropyLoss Entropy KL_div _check_reduction_params JSD_div AverageValueMeter AveragewithStd Cache ConfusionMatrix BatchDiceMeter _DiceMeter toOneHot SliceDiceMeter numpy_haussdorf HaussdorffDistance InstanceValue IoU KappaMetrics Kappa2Annotator MeterInterface _AggregatedMeter _Metric rename_df_columns MeterInterface MeterInteractMixin _IOMixin Storage HistoricalContainer AverageValueMeter AveragewithStd Cache ConfusionMatrix BatchDiceMeter _DiceMeter toOneHot SliceDiceMeter UniversalDice numpy_haussdorf HaussdorffDistance InstanceValue IoU KappaMetrics Kappa2Annotator average_surface_distance hausdorff_distance mod_hausdorff_distance SurfaceMeter _Metric APMeter AUCMeter AverageValueMeter ClassErrorMeter ConfusionMeter mAPMeter Meter MovingAverageValueMeter MSEMeter TimeMeter rename_df_columns _Method AMPGradientBackwardStep to_Apex EMA_Model DeployModel Model NormalGradientBackwardStep ZeroGradientBackwardStep AdaBoundW AdaBound AdamW RAdam PlainRAdam call_from_cmd get_image_paths get_args main split_images main get_args main get_args call_from_cmd arg_parser extract_path_info extract_value main _butter_lowpass butter_lowpass_filter identical RampScheduler WeightScheduler ConstantScheduler ExponentialLR StepLR CosineAnnealingWarmRestarts LambdaLR MultiStepLR _LRScheduler ReduceLROnPlateau CosineAnnealingLR CyclicLR PolynomialLR GradualWarmupScheduler HookBase HookMixin _Trainer _TrainerHook FSGMGenerator VATGenerator distance kl vat _is_tarxz _is_zip extract_archive _is_tgz _is_tar download_url download_and_extract_archive check_md5 check_integrity makedir_exist_ok gen_bar_updater calculate_md5 _is_targz download _is_gzip map_ mmap_ iter_average dict_filter simplex logit2one_hot identical uniq probs2one_hot uncurry eq Vectorize set_nicer intersection dict_merge class2one_hot union Identical set_benchmark one_hot uc_ _register sset set_environment assert_list flatten_dict fix_all_seed probs2class tqdm_ _tqdm id_ extract_from_big_dict nice_dict write_yaml yaml_load path2Path path2str read_img multiprocessing_mapping read_img2 to_float is_single_integer is_float_array is_callable is_tuple_or_list to_torch is_string is_integer_array is_np_array is_single_float is_single_number is_single_bool is_np_scalar to_numpy is_generator is_iterable VATLoss_Multihead _disable_tracking_bn_stats VATLoss _l2_normalize _warnings YAMLArgParser str2bool original_match hungarian_match flat_acc ToLabel compute_iou non_max_suppression ImageViewer2DWidget view_batch ImageSlicingWidget BatchViewer tensor2plotable _is_iterable_tensor _empty_iterator _is_tensor multi_slice_viewer_debug cmap Multi_Slice_Viewer main get_parser Volume DataFrameDrawer arg_parser DrawCSV2 SummaryWriter _create_new_axe _repositioning_axes singleline_plot multipleline_plot _num_axes plot_callback autoencoder MNISTTrainer gradient_difference_loss IMSATIICTrainer IMSATTrainer MNIST read_label_file get_int read_image_file MNISTClusteringDatasetInterface MNISTSemiSupervisedDatasetInterface IMSAT_Enhanced_Trainer IMSAT_Trainer IIC_Trainer IIC_enhanced_Trainer IIC_adv_Trainer Net IMSAT IIC_Trainer Trainer ma MI_Estimator mutual_information sample_batch Mine get_dataloader MixUpTrainer dice_loss SemiSegTrainer inverse_transform_matrix AffineTensorTransform _draw_equal_dataset _override_transformation get_mnist_dataloaders _draw_inequal_dataset show_dataset SemiEntropyTrainer SemiUDATrainer SemiPrimalDualTrainer SemiTrainer convbnrelu_bloc get_prior_from_dataset SimpleNet val Wloss DropPath Dataug Affine test imshow Prediction Transf CIFAR10ID test_val train signout structured_consistency_loss create_gaussian_norm_dataset fix_seed ClusterNet5gTrunk ClusterNet5gHead ClusterNet5g create_gaussian_norm_dataset TensorDataset merge SubSpaceClusteringMethod2 SubSpaceClusteringMethod subspaceTrainer CosineAnnealingLR _LRScheduler CosineAnnealingLR_ SWATrainer SGDTrainer DenseNet201 DenseNet161 DenseNet121 Transition DenseNet Bottleneck densenet_cifar test DenseNet169 DPN Bottleneck test DPN92 DPN26 EfficientNetB0 Block EfficientNet test GoogLeNet Inception test conv_block LargeConvNet GradReverse SimpleNet identical LeNet Block MobileNet test Block test MobileNetV2 PNASNetB PNASNetA SepConv test PNASNet CellB CellA PreActBlock PreActResNet50 PreActResNet PreActResNet18 test PreActResNet152 PreActBottleneck PreActResNet101 PreActResNet34 ResNet ResNet18 ResNet34 Bottleneck ResNet101 test ResNet50 BasicBlock ResNet152 Block ResNeXt29_4x64d ResNeXt ResNeXt29_2x64d test_resnext ResNeXt29_32x4d ResNeXt29_8x64d PreActBlock SENet18 SENet test BasicBlock ShuffleNetG2 ShuffleNet Bottleneck test ShuffleBlock ShuffleNetG3 SplitBlock test DownBlock ShuffleBlock ShuffleNetV2 BasicBlock VGG test BasicBlock NetworkBlock WideResNet get_dataloader Test_arch_interface TestPILCutout TestGrey2tensor TestRandomApply Test_RandomCrop Test_Sobel TestCenterCrop TestInterface Test_Sequential_Wrapper TestTensorCutout TestCasewithSetUp TestResize TensorRandomCrop TestCenterCrop TestTensorAugmentation TestToyExample TestCifar Test_semisupervised_MNIST Test_semisupervised_CIFAR TestDownloadDataset TestDownloadDataset Test_SemiDataloader TestPatientSampler TestDownloadDataset Test_ACDCDataset TestDownloadDataset Test_wMHDatasetDataset TestDiceLoss TestIMSATLoss TestKLDiv Test_IIC TestJSDDiv TestCrossEntropyLoss TestDrawAverageWithSTD TestHaussdorffDistance TestBasicInterface TestMeterInterface TestEMA TestModel_ TestTrainer TestAdversarialFSGMGenerator fakenetwork np2torch TestViewer torch2numpy TestDataFrameDrawer xavier_normal_ normal_ data fill_ get lower pop arch_callable list Compose list Compose data isinstance fill_ Conv2d xavier_uniform_ normal_ zero_ BatchNorm2d Linear append LUConv range bias kaiming_normal_ modules weight constant_ BatchNorm2d Sequential Conv2d PReLU BatchNorm2d Sequential Conv2d PReLU BatchNorm2d Sequential Conv2d PReLU BatchNorm2d Sequential Conv2d conv_block BatchNorm2d Sequential Conv2d activ insert append BatchNorm2d layer BatchNorm2d Sequential ConvTranspose2d MaxPool2d gaussian append items list Compose tolist Tensor Mapping type isinstance sum list isinstance Sequence new type zip _new_shared Tensor Mapping get pin_memory set_device put hasattr isinstance Sequence Tensor Mapping SIGCHLD signal getsignal seed init_fn get fetch set_num_threads close _set_worker_signal_handlers put create_fetcher is_set manual_seed cancel_join_thread ManagerWatchdog is_alive WorkerInfo print int array shuffle tqdm md5 hexdigest makedirs join basename urlretrieve print expanduser makedir_exist_ok expanduser list expanduser list listdir filter dcp append hasattr __name__ apply warn list index get t shape unsqueeze sum squeeze class2one_hot softmax probs2one_hot hd list __surface_distances max percentile __surface_distances max initialize torchnet device to optimizer step zero_grad is_apex hasattr glob sorted print mean append crop range len get_image_paths len mkdir save titles folder_path split_images range open parse_args pprint add_argument ArgumentParser str get_args insert check_output main grid Path xrange to_list yrange list stem title ylim savefig legend partial plot close set classes smooth_factor xlim enumerate extend filter out_dir T print to_csv folder pprint to_dict isinstance print add_argument add_mutually_exclusive_group high_better ArgumentParser vars parse_args specific_folders extract_path_info file save_filename rglob save_dir append str split_path enumerate arg_parser butter lfilter _butter_lowpass str Path view backward grad requires_grad_ distance device normalize to network range download_and_extract_archive join basename format print download_url expanduser check_integrity _is_tarxz _is_zip join remove _is_tar dirname _is_gzip md5 print nice print str list items seed str manual_seed_all manual_seed seed manual_seed_all manual_seed ones_like type float32 argmax shape class2one_hot probs2class softmax MutableMapping items list isinstance extend append items list items list isinstance isinstance print pprint exists path2Path get T set_num_threads put task_done open open is_tensor is_tensor ones view join warn isinstance float numel int Tensor list items zeros_like zeros sum range int list items zeros_like Tensor sum range astype delete float32 compute_iou append minimum maximum QApplication show list exec_ argv isinstance concatenate setBatch exit instance BatchViewer Tensor numpy range len isinstance is_tensor ndarray isinstance _is_tensor isinstance Tensor ndarray isinstance show subplots use set_title tensor2plotable reshape mpl_connect axis imshow _is_tensor contour array enumerate arange ListedColormap N get_cmap add_argument_group parse_args add_argument show decode insert img_source Multi_Slice_Viewer gt_folders vars get_parser Volume pprint verbose axes set_position Bbox linspace _num_axes enumerate _num_axes add_subplot mean exp log mine_net list range concatenate choice ParallelDataLoader sum append shape _inverse_transform_matrix dataset isinstance list sort tolist extend unique range len items list tolist extend __len__ set range value_counts isinstance print indices __repr__ MNIST _draw_equal_dataset _override_transformation _draw_inequal_dataset show_dataset Subset targets DataLoader dcp value_counts show transpose numpy gradit zero_grad whengrad wcriterion max progress_bar transf iter append next range detach_ grad net enumerate criterion backward print repeat stoch step len backward print aug progress_bar zero_grad len mean filter numpy modules item append train step max net enumerate stoch append eval print print name eval mkdir save append seed shuffle zeros range randn net densenet_cifar randn DPN92 EfficientNetB0 shape size GoogLeNet MobileNet MobileNetV2 PNASNetB PreActResNet18 ResNet18 randn print size ResNeXt29_2x64d net SENet18 ShuffleNetG2 ShuffleNetV2 VGG | # deep-clustering-toolbox #### PyTorch Vision toolbox not only for deep-clustering ### Introduction I still use this repo for research propose. I update some modules frequently to make the framework flexible enough. This repo contains the base code for a deep learning framework using `PyTorch`, to benchmark algorithms for various dataset. The current version supports `MNIST`, `CIFAR10`, `SVHN` and `STL-10` for semisupervised and unsupervised learning. `ACDC`, `Promise12`, `WMH` and so on are supported as segmentation counterpart. #### Features: >- Powerful cmd parser using `yaml` module, providing flexible input formats without predefined argparser. >- Automatic checkpoint management adapting to various settings | 2,523 |
jjalfaro9/CXR-ReportGen | ['text generation'] | ['Clinically Accurate Chest X-Ray Report Generation'] | project/generate_indexer.py project/main.py project/att.py project/data.py create_train_test_split.py project/train.py project/models.py create_data_files.py project/scratch.py main AdaptiveAttention Attention VisualSentinel main collate_fn CXRDataset addWordsToIndexer Indexer ImageEncoder WordDecoder SentenceDecoder get_models train test save_models write system listdir pixel_array open list Tensor stack long zip append zeros max range enumerate len print DataLoader CXRDataset enumerate len get_index enumerate index split model_name save img_feature_size WordDecoder embedd_size device img_size DataParallel vocab_size SentenceDecoder ImageEncoder to parallel hidden_size batch_size save_models continue_training zero_grad MultiStepLR device dataset argmax max list img_enc squeeze Adam teacher_forcing_const load_state_dict to lambda_sent parallel CrossEntropyLoss range SummaryWriter format word_dec close get_models item float long enumerate load int criterion backward print add_scalar lambda_word tqdm parameters model_name sentence_dec step len load format tqdm get_models eval load_state_dict device to enumerate | # CXR-ReportGen Our implementation of https://arxiv.org/pdf/1904.02633.pdf | 2,524 |
jjgo/shrinkbench | ['network pruning'] | ['What is the State of Neural Network Pruning?'] | models/mnistnet.py experiment/train.py pruning/mixin.py metrics/size.py util/csvlogger.py pruning/vision.py models/cifar_vgg.py datasets/places365.py pruning/modules.py util/color.py analysis/python/main.py strategies/utils.py strategies/magnitude.py models/__init__.py analysis/python/pareto.py pruning/utils.py util/online.py datasets/datasets.py models/cifar_resnet.py metrics/flops.py util/__init__.py datasets/__init__.py metrics/memory.py metrics/__init__.py pruning/abstract.py scripts/prune.py plot/data.py plot/plot.py strategies/__init__.py experiment/prune.py strategies/random.py experiment/base.py metrics/accuracy.py pruning/__init__.py strategies/channel.py metrics/abstract_flops.py models/head.py models/pretrained.py plot/__init__.py util/automap.py pruning/mask.py experiment/__init__.py _load_normalized_prune_arch_dfs make_papers_comparisons_fig load_lottery_ticket_resnet_results _methods_and_years_for_models color_for_idx _load_comparesto_dict create_improvements_df _cached_search_pubs_query_top_hit hist_integers clean_results info_for_bibtex estimated_model_stats plot_tradeoff_curve marker_for_idx load_pytorch_pretrained_models_csv load_models_csv make_rn50_magnitude_fig lotsa_colors_cmap _load_valid_pruning_results load_state_of_sparsity_resnet_results load_efficientnet_results _load_paper2combos_dict make_pruned_vs_arch_fig load_existing_models_results make_numeric_comparisons_fig generate_bibtex_with_citekeys plot_efficientnet_results load_clean_papers_csv load_results make_pruning_grid_fig load_pruning_results print_misc_papers_stats _load_is_published_at_top_conf_dict make_line_traits_dicts make_nresults_fig save_fig print_misc_results_stats linear_interp_at_x_position slope_between_pts compare_curves cmp_to_linear_interp_curve pareto_cmp_to_points extract_curve_vertices MNIST ImageNet dataset_builder Places365 dataset_path CIFAR10 CIFAR100 Places365 Experiment PruningExperiment TrainingExperiment conv2d_flops dense_flops correct accuracy flops _linear_flops _conv2d_flops memory_size model_size nonzero ResNet LambdaLayer resnet_factory _weights_init BasicBlock vgg_bn_drop_100 vgg_bn_drop VGGBnDrop ConvBNReLU reduce_linear_layer replace_head get_classifier_module mark_classifier MnistNet LeNet pretrained_weights df_from_results broadcast_unitary_compression df_filter plot_df reset_plt plot_acc_compression plot_error_compression_strategy plot_acc_compression_strategy plot_error_compression Pruning LayerPruning apply_masks masks_details mask_module ActivationMixin GradientMixin Conv2dMasked _same_shape _ensure_tensor LinearMasked _same_device MaskedModule get_gradients fraction_to_keep get_activations get_param_gradients hook_applyfn get_params VisionPruning jsonfile ActivationNormChannelPruning WeightNormChannelPruning LayerMagGrad LayerMagAct GlobalMagWeight GlobalMagAct GlobalMagGrad LayerMagWeight RandomPruning random_mask importance_masks flatten_importances fraction_threshold map_importances activation_importance fraction_mask threshold_mask norms_tensor AutoMap _color2code printc highlight colors CSVLogger OnlineStats OnlineStatsMap get_cmap isinstance list get_zorder arange set_major_locator MaxNLocator set_xlim color_for_idx set_zorder hist twinx set_visible max list dict unique zip get_cmap len join savefig randn abs sleep search_pubs_query format entries print _cached_search_pubs_query_top_hit append next list format print apply rename info_for_bibtex append citedby read_csv drop subplots strip show list columns sorted set_title DiGraph set_xlabel len hist_integers nodes shape pprint edges add_edge format tight_layout figlegend set zip add_node isinstance load_clean_papers_csv print draw maximum subplots_adjust set_ylabel _load_is_published_at_top_conf_dict get_legend_handles_labels array save_fig split pop int iterrows format from_records concat strip copy dict Path append sort_values read_csv _methods_and_years_for_models Path sort_values read_csv drop list endswith lower startswith append _methods_and_years_for_models rename Path sort_values read_csv values drop load_efficientnet_results concat sort_values load_pytorch_pretrained_models_csv read_csv drop columns print concat apply mean rename Path read_csv join set_index load_clean_papers_csv astype drop NaN nan isna read_csv split concat rename NaN isna fillna split load_lottery_ticket_resnet_results load_pruning_results load_state_of_sparsity_resnet_results load_efficientnet_results clean_results list load_results print unique nanmax show format subplots errorbar print close mean log2 array vstack unique append sort_values std save_fig values load_results vstack append sort_values values subplots show list sorted set_title set_xlabel delaxes plot_tradeoff_curve update format close figlegend _load_valid_pruning_results tight_layout zip enumerate items subplots_adjust dict set_ylabel make_line_traits_dicts get_legend_handles_labels ravel save_fig len subplots from_records values show list set_title set_xlabel hist_integers append close tight_layout figlegend set unique zip _load_paper2combos_dict print load_results subplots_adjust set_ylabel _load_is_published_at_top_conf_dict get_legend_handles_labels array save_fig len setdefault load_results copy apply array unique median load_existing_models_results values subplots _load_normalized_prune_arch_dfs roll list set_xlabel scatter sort_values update plot figlegend tight_layout zip get_cmap enumerate set_xscale suptitle subplots_adjust dict argsort set_ylabel get_legend_handles_labels ravel array save_fig subplots _load_normalized_prune_arch_dfs color_for_idx values show sorted set_title set_xlabel array append isin sort_values marker_for_idx errorbar plot close figlegend tight_layout set mean unique get_cmap enumerate set_xscale print subplots_adjust set_ylabel get_legend_handles_labels ravel std save_fig show format subplots xlabel semilogx ylabel close title lineplot legend load_existing_models_results save_fig list compare_curves from_records to_csv _load_valid_pruning_results set _load_comparesto_dict unique zip append sum values len list load_results unique isinstance zip load_clean_papers_csv print strip split append len join sorted list load_clean_papers_csv print shape subplots drop values show create_improvements_df list set_title set_xlabel hist_integers pprint sum format close tight_layout figlegend unique print subplots_adjust set_ylabel _load_is_published_at_top_conf_dict get_legend_handles_labels array save_fig len list format print load_results set array unique zip sum drop_duplicates len join sorted list load_clean_papers_csv print unique zip load_clean_papers_csv zip slope_between_pts atleast_2d linear_interp_at_x_position astype int8 any sum array atleast_2d all arange slope_between_pts len copy pareto_cmp_to_points vstack array unique append max enumerate minimum asarray atleast_2d linear_interp_at_x_position min cmp_to_linear_interp_curve maximum vstack unique extract_curve_vertices empty max enumerate len resolve print exists Compose dataset_path dataset_builder Normalize dataset_builder Normalize dataset_builder Normalize Normalize dataset_builder filterwarnings dataset_builder Normalize int tuple lower ceil float prod zeros len device items list copy get_activations size get_activations shape nonzero prod shape parameters nonzero numpy prod weight kaiming_normal_ __name__ pretrained_weights VGGBnDrop load_state_dict pretrained_weights VGGBnDrop load_state_dict in_features Linear SqueezeNet isinstance fc classifier reduce_linear_layer Inception3 SqueezeNet isinstance getattr classifier Inception3 len get_classifier_module print glob sort_values broadcast_unitary_compression Path append DataFrame read_csv items list append iterrows copy set defaultdict groupby xticks values sorted list ylabel map add legend append sort_values update errorbar plot xscale set mean xlabel figure std join errorbar plot mean sort_values std drop sorted list plot_acc_compression_strategy xlabel xscale ylabel map set title axhline figure legend xticks join plot mean sort_values drop list xlabel xscale map ylabel title plot_error_compression_strategy figure legend xticks enumerate items list named_children isinstance set_masks setattr items list named_children _ensure_tensor _same_device getattr mul_ append items list named_modules Tensor numpy isinstance OrderedDict hook_applyfn remove apply defaultdict model backward zero_grad loss_func training copy OrderedDict named_parameters modules train CrossEntropyLoss sum model_size ones_like quantile ones_like logical_and fraction_threshold append reshape norm mean repeat norms_tensor upper startswith hasattr isinstance _color2code print _color2code END | # ShrinkBench Open source PyTorch library to facilitate development and standardized evaluation of neural network pruning methods.  ## Paper This repo contains the analysis and benchmarks results from the paper [What is the State of Neural Network Pruning?](https://arxiv.org/abs/2003.03033). # Installation First, install the dependencies, this repo depends on - `PyTorch` - `Torchvision` - `NumPy` | 2,525 |
jjprincess/PMTD | ['scene text detection', 'instance segmentation', 'semantic segmentation', 'scene text recognition'] | ['Pyramid Mask Text Detector'] | maskrcnn_benchmark/modeling/make_layers.py maskrcnn_benchmark/modeling/rpn/loss.py maskrcnn_benchmark/layers/roi_align.py demo/utils/generate_util.py maskrcnn_benchmark/utils/model_zoo.py maskrcnn_benchmark/solver/__init__.py maskrcnn_benchmark/layers/nms.py maskrcnn_benchmark/data/datasets/evaluation/voc/voc_eval.py tests/test_feature_extractors.py tests/test_detectors.py tests/test_box_coder.py maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py maskrcnn_benchmark/utils/imports.py tests/test_rpn_heads.py maskrcnn_benchmark/data/samplers/distributed.py maskrcnn_benchmark/modeling/backbone/fbnet.py maskrcnn_benchmark/utils/env.py tests/checkpoint.py maskrcnn_benchmark/data/samplers/iteration_based_batch_sampler.py demo/inference.py maskrcnn_benchmark/layers/_utils.py maskrcnn_benchmark/layers/dcn/__init__.py maskrcnn_benchmark/layers/dcn/deform_conv_func.py maskrcnn_benchmark/modeling/detector/__init__.py maskrcnn_benchmark/utils/metric_logger.py maskrcnn_benchmark/modeling/rpn/utils.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 maskrcnn_benchmark/structures/bounding_box.py setup.py demo/utils/generate_icdar2017.py maskrcnn_benchmark/data/samplers/grouped_batch_sampler.py demo/utils/convert_results_to_icdar.py maskrcnn_benchmark/modeling/rpn/retinanet/inference.py maskrcnn_benchmark/data/datasets/evaluation/voc/__init__.py maskrcnn_benchmark/modeling/backbone/fbnet_builder.py maskrcnn_benchmark/layers/__init__.py maskrcnn_benchmark/utils/comm.py maskrcnn_benchmark/layers/dcn/deform_pool_func.py maskrcnn_benchmark/structures/segmentation_mask.py docker/docker-jupyter/jupyter_notebook_config.py maskrcnn_benchmark/data/datasets/coco.py maskrcnn_benchmark/modeling/roi_heads/keypoint_head/loss.py maskrcnn_benchmark/data/datasets/evaluation/__init__.py maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_feature_extractors.py maskrcnn_benchmark/structures/keypoint.py maskrcnn_benchmark/modeling/poolers.py maskrcnn_benchmark/layers/misc.py maskrcnn_benchmark/data/datasets/evaluation/coco/__init__.py maskrcnn_benchmark/data/transforms/__init__.py maskrcnn_benchmark/modeling/rpn/rpn.py demo/PMTD_predictor.py maskrcnn_benchmark/utils/cv2_util.py maskrcnn_benchmark/utils/timer.py maskrcnn_benchmark/layers/smooth_l1_loss.py maskrcnn_benchmark/utils/miscellaneous.py maskrcnn_benchmark/modeling/rpn/retinanet/loss.py maskrcnn_benchmark/modeling/roi_heads/keypoint_head/inference.py tests/test_backbones.py demo/PMTD_demo.py tools/train_net.py maskrcnn_benchmark/layers/dcn/deform_conv_module.py maskrcnn_benchmark/data/build.py maskrcnn_benchmark/modeling/roi_heads/keypoint_head/keypoint_head.py tests/test_metric_logger.py maskrcnn_benchmark/modeling/backbone/resnet.py maskrcnn_benchmark/modeling/roi_heads/mask_head/inference.py tests/test_predictors.py maskrcnn_benchmark/modeling/roi_heads/mask_head/loss.py maskrcnn_benchmark/data/datasets/evaluation/coco/coco_eval.py maskrcnn_benchmark/utils/collect_env.py maskrcnn_benchmark/utils/logger.py maskrcnn_benchmark/layers/batch_norm.py maskrcnn_benchmark/data/datasets/concat_dataset.py maskrcnn_benchmark/utils/checkpoint.py tests/test_segmentation_mask.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/roi_heads/keypoint_head/roi_keypoint_predictors.py maskrcnn_benchmark/modeling/utils.py maskrcnn_benchmark/modeling/roi_heads/roi_heads.py maskrcnn_benchmark/engine/__init__.py maskrcnn_benchmark/modeling/registry.py maskrcnn_benchmark/modeling/detector/generalized_rcnn.py maskrcnn_benchmark/modeling/roi_heads/keypoint_head/roi_keypoint_feature_extractors.py maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_predictors.py maskrcnn_benchmark/data/datasets/voc.py maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_predictors.py maskrcnn_benchmark/modeling/backbone/fbnet_modeldef.py tools/cityscapes/instances2dict_with_polygons.py maskrcnn_benchmark/data/samplers/__init__.py demo/transforms.py maskrcnn_benchmark/config/paths_catalog.py maskrcnn_benchmark/modeling/box_coder.py tests/env_tests/env.py demo/predictor.py tests/test_fbnet.py maskrcnn_benchmark/data/transforms/transforms.py tools/test_net.py maskrcnn_benchmark/modeling/backbone/backbone.py maskrcnn_benchmark/modeling/roi_heads/box_head/box_head.py maskrcnn_benchmark/structures/boxlist_ops.py tests/test_nms.py maskrcnn_benchmark/__init__.py tests/utils.py tools/cityscapes/convert_cityscapes_to_coco.py maskrcnn_benchmark/config/defaults.py maskrcnn_benchmark/layers/sigmoid_focal_loss.py maskrcnn_benchmark/utils/registry.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 tests/test_configs.py maskrcnn_benchmark/modeling/backbone/fpn.py maskrcnn_benchmark/utils/c2_model_loading.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/modeling/rpn/retinanet/retinanet.py maskrcnn_benchmark/layers/roi_pool.py maskrcnn_benchmark/engine/inference.py maskrcnn_benchmark/data/datasets/list_dataset.py maskrcnn_benchmark/engine/trainer.py maskrcnn_benchmark/layers/dcn/deform_pool_module.py maskrcnn_benchmark/data/transforms/build.py get_extensions is_clockwise PlaneClustering plane_init save_results prepare_data main plane_clustering get_intersection_of_plane main build_parser create_pmtd_demo PMTDDemo vis_keypoints COCODemo Resize nms get_results output_results generate_cocojson generate_cocojson_join CocoLabel match_file_path exist_file_path GenerateUtil DatasetCatalog ModelCatalog make_data_sampler _quantize make_data_loader make_batch_data_sampler build_dataset _compute_aspect_ratios BatchCollator COCODataset _has_only_empty_bbox has_valid_annotation _count_visible_keypoints ConcatDataset ListDataset PascalVOCDataset evaluate COCOResults check_expected_results prepare_for_coco_segmentation evaluate_predictions_on_coco evaluate_box_proposals do_coco_evaluation prepare_for_coco_keypoint prepare_for_coco_detection coco_evaluation calc_detection_voc_ap do_voc_evaluation calc_detection_voc_prec_rec eval_detection_voc voc_evaluation DistributedSampler GroupedBatchSampler IterationBasedBatchSampler build_transforms Compose ToTensor Resize Normalize RandomHorizontalFlip ColorJitter compute_on_dataset inference _accumulate_predictions_from_multiple_gpus do_train reduce_loss_dict FrozenBatchNorm2d _NewEmptyTensorOp Conv2d DFConv2d interpolate BatchNorm2d ConvTranspose2d ROIAlign _ROIAlign _ROIPool ROIPool SigmoidFocalLoss _SigmoidFocalLoss sigmoid_focal_loss_cpu smooth_l1_loss _load_C_extensions DeformConvFunction ModulatedDeformConvFunction ModulatedDeformConv DeformConv ModulatedDeformConvPack DeformRoIPoolingFunction DeformRoIPoolingPack ModulatedDeformRoIPoolingPack DeformRoIPooling BalancedPositiveNegativeSampler BoxCoder conv_with_kaiming_uniform make_conv3x3 get_group_gn make_fc group_norm Matcher make_pooler LevelMapper Pooler cat build_resnet_fpn_p3p7_backbone build_backbone build_resnet_fpn_backbone build_resnet_backbone add_rpn_head add_roi_head_mask FBNetROIHead _get_rpn_stage FBNetRPNHead FBNetTrunk add_roi_head _get_head_stage _get_trunk_cfg create_builder add_conv_body add_roi_head_keypoints _get_divisible_by ConvBNRelu _expand_block_cfg FBNetBuilder CascadeConv3x3 get_blocks SEModule _add_to_arch IRFBlock Shift expand_stages_cfg expand_stage_cfg ShiftBlock5x5 _py2_round get_num_stages unify_arch_def _get_upsample_op Upsample Identity _block_cfgs_to_list ChannelShuffle add_archs LastLevelMaxPool FPN LastLevelP6P7 StemWithGN ResNetHead _make_stage BottleneckWithBatchNorm ResNet BottleneckWithGN Bottleneck StemWithFixedBatchNorm StemWithBatchNorm BottleneckWithFixedBatchNorm BaseStem 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 FPNXconv1fcFeatureExtractor FPN2MLPFeatureExtractor ResNet50Conv5ROIFeatureExtractor FPNPredictor make_roi_box_predictor FastRCNNPredictor heatmaps_to_keypoints Keypointer make_roi_keypoint_post_processor KeypointPostProcessor ROIKeypointHead build_roi_keypoint_head make_roi_keypoint_loss_evaluator project_keypoints_to_heatmap KeypointRCNNLossComputation _within_box cat_boxlist_with_keypoints KeypointRCNNFeatureExtractor make_roi_keypoint_feature_extractor KeypointRCNNPredictor make_roi_keypoint_predictor paste_mask_in_image expand_boxes Masker make_roi_mask_post_processor MaskPostProcessorCOCOFormat expand_masks MaskPostProcessor make_roi_mask_loss_evaluator MaskRCNNLossComputation project_masks_on_boxes keep_only_positive_boxes ROIMaskHead build_roi_mask_head MaskRCNNFPNFeatureExtractor make_roi_mask_feature_extractor MaskRCNNC4Predictor MaskRCNNConv1x1Predictor MaskRCNNC4Predictor_Upsample make_roi_mask_predictor AnchorGenerator generate_anchors _scale_enum _whctrs make_anchor_generator _ratio_enum make_anchor_generator_retinanet _generate_anchors BufferList _mkanchors make_rpn_postprocessor RPNPostProcessor RPNLossComputation generate_rpn_labels make_rpn_loss_evaluator build_rpn RPNHeadFeatureSingleConv RPNModule RPNHead RPNHead_Softmax RPNHeadConvRegressor concat_box_prediction_layers permute_and_flatten make_retinanet_postprocessor RetinaNetPostProcessor make_retinanet_loss_evaluator generate_retinanet_labels RetinaNetLossComputation build_retinanet RetinaNetHead RetinaNetModule make_optimizer make_lr_scheduler WarmupMultiStepLR BoxList cat_boxlist boxlist_iou boxlist_nms remove_small_boxes _cat ImageList to_image_list PersonKeypoints kp_connections _create_flip_indices Keypoints keypoints_to_heat_map SegmentationMask PolygonList PolygonInstance BinaryMaskList _rename_basic_resnet_weights _rename_conv_weights_for_deformable_conv_layers load_resnet_c2_format load_c2_format _rename_weights_for_resnet _load_c2_pickled_weights _rename_fpn_weights DetectronCheckpointer Checkpointer collect_env_info get_pil_version synchronize get_world_size reduce_dict all_gather get_rank is_main_process findContours setup_environment setup_custom_environment import_file setup_logger SmoothedValue MetricLogger mkdir strip_prefix_if_present load_state_dict align_and_update_state_dicts cache_url _register_generic Registry get_time_str Timer TestCheckpointer TestBackbones TestBoxCoder TestConfigs SubsetSampler TestGroupedBatchSampler TestIterationBasedBatchSampler create_random_input create_model get_config_files _test_build_detectors _test_run_selected_detectors TestDetectors _test_primitive TestFBNetBuilder TestFeatureExtractors _test_feature_extractors TestMetricLogger TestNMS TestPredictors _test_predictors TestRPNHeads TestSegmentationMask load_config_from_file load_config get_config_root_path get_config_root_path main build_masker main train run_test convert_coco_stuff_mat xyxy_to_xywh convert_cityscapes_instance_only poly_to_box parse_args getLabelID instances2dict_with_polygons main glob join dirname abspath mean as_tensor empty t ones argmin matmul t flatten median abs range cat enumerate t squeeze empty range range len get_img_info append zip convert resize get_field range bbox len reg_pyramid_in_image defaultdict PlaneClustering prepare_data tqdm save append numpy load join save_results add_argument ArgumentParser merge_from_file Masker PlaneClustering PMTDDemo merge_from_list run_on_opencv_image namedWindow zip COLOR_BGR2RGB print waitKey resizeWindow select_top_predictions image_path bbox WINDOW_NORMAL imshow create_pmtd_demo compute_prediction parse_args imread build_parser cvtColor minimum line NAMES tuple CONNECTIONS copy index get_cmap range circle len load range append save ones range len chdir insert_annotation dump insert_factory GenerateUtil get_coco_label range insert_annotation dump insert_factory GenerateUtil get_coco_label range abspath exists abspath get ConcatDataset getattr append factory SequentialSampler RandomSampler list sorted 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 _has_only_empty_bbox PascalVOCDataset isinstance COCODataset dict __name__ items list format join COCOResults check_expected_results getLogger prepare_for_coco_segmentation prepare_for_coco_keypoint item info save evaluate_box_proposals prepare_for_coco_detection get_img_info convert tolist extend resize enumerate get_img_info decode isinstance resize tolist extend tqdm forward_single_image get_field frPyObjects enumerate convert tolist extend resize get_field enumerate arange zeros_like resize max boxlist_iou append loadAnns sum range cat getAnnIds mean float enumerate get_img_info reshape sort convert min zeros as_tensor len accumulate summarize evaluate COCOeval error format info getLogger get_img_info format info get_groundtruth eval_detection_voc resize append enumerate calc_detection_voc_ap calc_detection_voc_prec_rec list defaultdict cumsum astype extend copy keys numpy array unique zip append zeros argmax max arange concatenate empty nan sum max range len warning info getLogger BRIGHTNESS TO_BGR255 CONTRAST MIN_SIZE_TEST HUE Compose Resize MIN_SIZE_TRAIN Normalize MAX_SIZE_TRAIN MAX_SIZE_TEST SATURATION ColorJitter update tqdm eval device to enumerate update list sorted getLogger warning all_gather keys getLogger save_results save Timer device dataset get_time_str _accumulate_predictions_from_multiple_gpus tic format synchronize total_time get_world_size info toc join dict compute_on_dataset len get_world_size getLogger model zero_grad save str MetricLogger to sum update format timedelta info reduce_loss_dict enumerate time global_avg train step len _output_size tuple dtype sigmoid unsqueeze device log abs where join glob extend dirname abspath EPSILON DIM_PER_GP NUM_GROUPS group_norm Conv2d bias normal_ kaiming_normal_ ReLU append weight constant_ kaiming_uniform_ bias weight constant_ Linear POOLER_RESOLUTION POOLER_SCALES POOLER_SAMPLING_RATIO Pooler OrderedDict ResNet Sequential BACKBONE_OUT_CHANNELS FPN ResNet Sequential OrderedDict RES2_OUT_CHANNELS BACKBONE_OUT_CHANNELS FPN ResNet Sequential OrderedDict RES2_OUT_CHANNELS BACKBONE_OUT_CHANNELS get format FBNetBuilder SCALE_FACTOR WIDTH_DIVISOR DW_CONV_SKIP_BN unify_arch_def DW_CONV_SKIP_RELU loads ARCH_DEF BN_TYPE info ARCH get_num_stages get list get_blocks range FBNetTrunk Sequential OrderedDict create_builder last_depth get list format warn get_blocks range len create_builder FBNetRPNHead RPNHeadConvRegressor out_channels get get_blocks create_builder create_builder create_builder int Upsample append deepcopy range append expand_stage_cfg append expand_stage_cfg enumerate enumerate update deepcopy _block_cfgs_to_list _add_to_arch max append deepcopy append transformation_module range MASK_ON CombinedROIHeads RETINANET_ON KEYPOINT_ON append CLS_AGNOSTIC_BBOX_REG BoxCoder DETECTIONS_PER_IMG PostProcessor BBOX_REG_WEIGHTS USE_FPN NMS SCORE_THRESH POSITIVE_FRACTION FG_IOU_THRESHOLD CLS_AGNOSTIC_BBOX_REG BATCH_SIZE_PER_IMAGE BoxCoder BalancedPositiveNegativeSampler BBOX_REG_WEIGHTS BG_IOU_THRESHOLD Matcher FastRCNNLossComputation int transpose maximum resize ceil zeros argmax range len Keypointer KeypointPostProcessor convert cat_boxlist add_field get_fields cat POSITIVE_FRACTION FG_IOU_THRESHOLD BATCH_SIZE_PER_IMAGE BalancedPositiveNegativeSampler KeypointRCNNLossComputation BG_IOU_THRESHOLD Matcher RESOLUTION zeros_like float new_zeros int uint8 float expand_masks min float32 expand interpolate zeros to max POSTPROCESS_MASKS zip convert device resize append to crop get_mask_tensor FG_IOU_THRESHOLD MaskRCNNLossComputation BG_IOU_THRESHOLD Matcher RESOLUTION get_field squeeze append AnchorGenerator STRADDLE_THRESH ANCHOR_SIZES ANCHOR_STRIDE USE_FPN ASPECT_RATIOS AnchorGenerator STRADDLE_THRESH OCTAVE ANCHOR_SIZES tuple SCALES_PER_OCTAVE append float ASPECT_RATIOS ANCHOR_STRIDES range 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 FPN_POST_NMS_PER_BATCH POST_NMS_TOP_N_TEST MIN_SIZE PRE_NMS_TOP_N_TRAIN FPN_POST_NMS_TOP_N_TEST PRE_NMS_TOP_N_TEST get_field POSITIVE_FRACTION FG_IOU_THRESHOLD RPNLossComputation BATCH_SIZE_PER_IMAGE BalancedPositiveNegativeSampler BG_IOU_THRESHOLD Matcher RETINANET_ON reshape permute view permute_and_flatten reshape shape zip append NMS_TH DETECTIONS_PER_IMG INFERENCE_TH PRE_NMS_TOP_N RetinaNetPostProcessor get_field FG_IOU_THRESHOLD RetinaNetLossComputation SigmoidFocalLoss LOSS_GAMMA BG_IOU_THRESHOLD Matcher LOSS_ALPHA WEIGHT_DECAY_BIAS SGD named_parameters BASE_LR 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 update copy long enumerate max _rename_basic_resnet_weights sorted format getLogger OrderedDict from_numpy info keys _rename_fpn_weights sorted format replace getLogger match STAGE_WITH_DCN info keys enumerate CONV_BODY _rename_conv_weights_for_deformable_conv_layers replace _rename_weights_for_resnet _load_c2_pickled_weights get_pretty_env_info barrier get_world_size from_buffer dumps get_world_size loads zip append to max cat get_world_size startswith 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 items sorted list OrderedDict 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 str timedelta glob join get_config_root_path deepcopy to freeze build_detection_model int SIZE_DIVISIBILITY rand MIN_SIZE_TRAIN to_image_list append to assertGreater get_config_files len assertGreater len format Size assertEqual op shape to get deepcopy list assertGreater format items print fe assertEqual rand Size BoxList getattr builder assertIsNotNone load_config len get deepcopy list assertGreater format items print fe rand builder load_config len join get_config_root_path merge_from_file deepcopy realpath join dirname abspath coco_eval ArgumentParser make_data_loader opts OUTPUT_DIR collect_env_info set_device MASK_ON get_rank freeze to inference KEYPOINT_ON TEST build_masker merge_from_file DEVICE format build_detection_model init_process_group synchronize config_file setup_logger WEIGHT merge_from_list mkdir init info enumerate add_argument DetectronCheckpointer local_rank len DEVICE make_optimizer initialize load build_detection_model update CHECKPOINT_PERIOD make_data_loader WEIGHT DistributedDataParallel DetectronCheckpointer do_train device to OUTPUT_DIR make_lr_scheduler join zip TEST synchronize MASK_ON inference mkdir make_data_loader empty_cache OUTPUT_DIR module KEYPOINT_ON enumerate len distributed run_test train add_argument exit ArgumentParser print_help min max print join len load join zip print endswith len xyxy_to_xywh poly_to_box append walk open hasInstances uint8 format toDict print Instance findContours len astype RETR_EXTERNAL copy unique abspath append CHAIN_APPROX_NONE array flush open append instances2dict_with_polygons | # PMTD: Pyramid Mask Text Detector This project hosts the inference code for implementing the PMTD algorithm for text detection, as presented in our paper: Pyramid Mask Text Detector; Liu Jingchao, Liu Xuebo, Sheng Jie, Liang Ding, Li Xin and Liu Qingjie; arXiv preprint arXiv:1903.11800 (2019). The full paper is available at: [https://arxiv.org/abs/1903.11800](https://arxiv.org/abs/1903.11800).  ## Installation Check [INSTALL.md](INSTALL.md) for installation instructions. ## Trained model | 2,526 |
jkcrosby3/FashionMNST | ['data augmentation'] | ['DENSER: Deep Evolutionary Network Structured Representation'] | utils/helper.py configs.py benchmark/convnet.py app.py benchmark/runner.py utils/argparser.py utils/mnist_reader.py visualization/project_zalando.py start_s3_sync get_json_logger touch touch_dir _get_logger main cnn_model_fn PredictJob JobWorker JobManager get_args_request parse_arg get_args_cli now_int upload_result_s3 get_sprite_image invert_grayscale create_sprite_image vector_to_matrix_mnist UploadS3Thread load_mnist UploadS3Thread start Event dirname makedirs makedirs setFormatter touch_dir DEBUG getLogger addHandler StreamHandler Formatter touch setLevel INFO FileHandler setFormatter getLogger addHandler Formatter touch setLevel INFO FileHandler dense max_pooling2d dropout one_hot minimize reshape GradientDescentOptimizer conv2d softmax_cross_entropy asarray evaluate print Estimator shuffle labels images numpy_input_fn train range read_data_sets int append items list defaultdict utcfromtimestamp info int isinstance ones sqrt ceil array range vector_to_matrix_mnist invert_grayscale join | # Fashion-MNIST [](https://github.com/zalandoresearch/fashion-mnist/) [](https://gitter.im/fashion-mnist/Lobby?utm_source=share-link&utm_medium=link&utm_campaign=share-link) [](README.zh-CN.md) [](README.ja.md) [](https://opensource.org/licenses/MIT) [](https://hanxiao.github.io/2018/09/28/Fashion-MNIST-Year-In-Review/) <details><summary>Table of Contents</summary><p> * [Why we made Fashion-MNIST](#why-we-made-fashion-mnist) * [Get the Data](#get-the-data) | 2,527 |
jlee335/AI-Alggagi-Game | ['unity'] | ['Unity: A General Platform for Intelligent Agents'] | UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/chardet/universaldetector.py python-envs/sample-env/Lib/site-packages/pip/_vendor/urllib3/contrib/pyopenssl.py ml-agents-envs/mlagents_envs/communicator_objects/command_pb2.py python-envs/sample-env/Lib/site-packages/pip/__init__.py python-envs/sample-env/Lib/site-packages/setuptools/command/bdist_wininst.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/progress/bar.py ml-agents-envs/mlagents_envs/communicator.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_internal/distributions/base.py python-envs/sample-env/Lib/site-packages/pip/_vendor/pep517/compat.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/chardet/version.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/distlib/metadata.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/progress/spinner.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/chardet/langcyrillicmodel.py python-envs/sample-env/Lib/site-packages/pip/_internal/req/req_file.py UnitySDK/python-envs/sample-env/Lib/site-packages/setuptools/command/build_py.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_internal/models/link.py python-envs/sample-env/Lib/site-packages/pip/_vendor/chardet/eucjpprober.py ml-agents/mlagents/trainers/meta_curriculum.py python-envs/sample-env/Lib/site-packages/pip/_vendor/urllib3/packages/__init__.py python-envs/sample-env/Lib/site-packages/pip/_internal/utils/temp_dir.py python-envs/sample-env/Lib/site-packages/pip/_vendor/distlib/_backport/misc.py UnitySDK/python-envs/sample-env/Lib/site-packages/pkg_resources/_vendor/packaging/utils.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/packaging/__init__.py python-envs/sample-env/Lib/site-packages/pip/_internal/pep425tags.py utils/validate_meta_files.py python-envs/sample-env/Lib/site-packages/pip/_vendor/html5lib/treebuilders/dom.py python-envs/sample-env/Lib/site-packages/pip/_vendor/chardet/version.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/chardet/latin1prober.py python-envs/sample-env/Lib/site-packages/pkg_resources/_vendor/packaging/version.py python-envs/sample-env/Lib/site-packages/pip/_vendor/chardet/mbcsgroupprober.py python-envs/sample-env/Lib/site-packages/pip/_internal/cli/cmdoptions.py ml-agents/mlagents/trainers/components/reward_signals/__init__.py ml-agents-envs/mlagents_envs/side_channel/engine_configuration_channel.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_internal/distributions/__init__.py python-envs/sample-env/Lib/site-packages/pip/_vendor/cachecontrol/caches/redis_cache.py UnitySDK/python-envs/sample-env/Lib/site-packages/setuptools/__init__.py python-envs/sample-env/Lib/site-packages/pip/_vendor/distlib/database.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/html5lib/filters/alphabeticalattributes.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_internal/utils/ui.py ml-agents/mlagents/trainers/components/reward_signals/reward_signal_factory.py UnitySDK/python-envs/sample-env/Lib/site-packages/pkg_resources/_vendor/packaging/_compat.py python-envs/sample-env/Lib/site-packages/pip/_internal/commands/completion.py python-envs/sample-env/Lib/site-packages/setuptools/command/sdist.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/distlib/index.py python-envs/sample-env/Lib/site-packages/pip/_vendor/urllib3/request.py python-envs/sample-env/Lib/site-packages/pip/_vendor/__init__.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_internal/utils/logging.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/pkg_resources/__init__.py python-envs/sample-env/Lib/site-packages/pip/_vendor/chardet/euckrfreq.py ml-agents/mlagents/trainers/sac/policy.py python-envs/sample-env/Lib/site-packages/pip/_vendor/urllib3/contrib/socks.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_internal/vcs/git.py python-envs/sample-env/Lib/site-packages/setuptools/py33compat.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/packaging/markers.py UnitySDK/python-envs/sample-env/Lib/site-packages/setuptools/msvc.py python-envs/sample-env/Lib/site-packages/setuptools/command/upload.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/urllib3/contrib/appengine.py python-envs/sample-env/Lib/site-packages/pip/_internal/models/__init__.py ml-agents/mlagents/trainers/brain.py UnitySDK/python-envs/sample-env/Lib/site-packages/setuptools/py33compat.py ml-agents-envs/mlagents_envs/side_channel/float_properties_channel.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/chardet/big5prober.py ml-agents/mlagents/trainers/tests/test_meta_curriculum.py 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python-envs/sample-env/Lib/site-packages/setuptools/site-patch.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_internal/utils/filetypes.py python-envs/sample-env/Lib/site-packages/pip/_vendor/html5lib/treeadapters/__init__.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/urllib3/contrib/pyopenssl.py python-envs/sample-env/Lib/site-packages/pip/_vendor/cachecontrol/__init__.py python-envs/sample-env/Lib/site-packages/setuptools/monkey.py python-envs/sample-env/Lib/site-packages/setuptools/ssl_support.py python-envs/sample-env/Lib/site-packages/pip/_vendor/distlib/_backport/tarfile.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/cachecontrol/__init__.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/urllib3/util/retry.py ml-agents-envs/mlagents_envs/tests/test_side_channel.py python-envs/sample-env/Lib/site-packages/pip/_vendor/requests/help.py python-envs/sample-env/Lib/site-packages/setuptools/glob.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_internal/build_env.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/retrying.py python-envs/sample-env/Lib/site-packages/pip/_vendor/lockfile/symlinklockfile.py ml-agents-envs/mlagents_envs/environment.py python-envs/sample-env/Lib/site-packages/pip/_vendor/distlib/version.py python-envs/sample-env/Lib/site-packages/pip/_vendor/html5lib/_tokenizer.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_internal/commands/__init__.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_internal/utils/urls.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/urllib3/contrib/securetransport.py UnitySDK/python-envs/sample-env/Lib/site-packages/pkg_resources/_vendor/packaging/_structures.py python-envs/sample-env/Lib/site-packages/pip/_vendor/urllib3/fields.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_internal/models/format_control.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/distlib/markers.py python-envs/sample-env/Lib/site-packages/setuptools/sandbox.py UnitySDK/python-envs/sample-env/Lib/site-packages/setuptools/command/register.py python-envs/sample-env/Lib/site-packages/pip/_internal/utils/ui.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/msgpack/_version.py UnitySDK/python-envs/sample-env/Lib/site-packages/pkg_resources/_vendor/pyparsing.py python-envs/sample-env/Lib/site-packages/setuptools/command/__init__.py UnitySDK/python-envs/sample-env/Lib/site-packages/pkg_resources/_vendor/packaging/requirements.py ml-agents/mlagents/trainers/brain_conversion_utils.py python-envs/sample-env/Lib/site-packages/pip/_internal/commands/hash.py python-envs/sample-env/Lib/site-packages/pip/_vendor/html5lib/_ihatexml.py python-envs/sample-env/Lib/site-packages/pip/_vendor/urllib3/connection.py python-envs/sample-env/Lib/site-packages/pip/_vendor/distlib/_backport/sysconfig.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/html5lib/treebuilders/base.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_internal/operations/check.py python-envs/sample-env/Lib/site-packages/pkg_resources/_vendor/six.py UnitySDK/python-envs/sample-env/Lib/site-packages/pip/_vendor/urllib3/poolmanager.py VerifyVersionCommand UnityGymException ActionFlattener UnityEnv test_gym_wrapper test_multi_agent create_mock_group_spec test_branched_flatten setup_mock_unityenvironment test_gym_wrapper_visual create_mock_vector_step_result VerifyVersionCommand set_warnings_enabled 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 BrainParameters CameraResolution group_spec_to_brain_parameters BufferException AgentBuffer Curriculum make_demo_buffer load_demonstration demo_to_buffer get_global_agent_id EnvManager EnvironmentStep SamplerException TrainerConfigError CurriculumError TrainerError MetaCurriculumError CurriculumLoadingError CurriculumConfigError RunOptions create_environment_factory create_sampler_manager parse_command_line run_training prepare_for_docker_run try_create_meta_curriculum run_cli main _create_parser get_version_string MetaCurriculum EncoderType LearningModel LearningRateSchedule Policy RLTrainer main parse_command_line MultiRangeUniformSampler UniformSampler SamplerFactory SamplerManager GaussianSampler Sampler SimpleEnvManager StatsWriter StatsSummary StatsReporter TensorboardWriter CSVWriter worker EnvironmentResponse UnityEnvWorker StepResponse SubprocessEnvManager EnvironmentCommand get_layer_shape pool_to_HW flatten sqr_diff process_layer process_model get_layer_rank slow_but_stable_topological_sort get_attr basic_lstm ModelBuilderContext order_by get_epsilon get_tensor_dtype replace_strings_in_list debug embody by_op get_tensor_dims strided_slice remove_duplicates_from_list axis_to_barracuda by_name locate_actual_output_node convert strides_to_HW get_tensor_data very_slow_but_stable_topological_sort gru TFPolicy UnityPolicyException UnityTrainerException Trainer TrainerController TrainerFactory initialize_trainer load_config _load_config AgentExperience Trajectory SplitObservations 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_mock_batchedstep simulate_rollout make_brain_parameters setup_mock_brain make_fake_trajectory create_mock_banana_brain create_batchedstep_from_brainparams create_mock_brainparams create_mock_3dball_brain test_agent_manager_queue test_agentprocessor test_agent_manager create_mock_brain create_mock_policy test_barracuda_converter sac_dummy_config test_bcmodule_rnn_update test_bcmodule_update ppo_dummy_config test_bcmodule_constant_lr_update create_policy_with_bc_mock test_bcmodule_dc_visual_update 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_load_demo test_load_demo_dir basic_options test_docker_target_path test_run_training test_env_args test_commandline_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_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_min_visual_size test_process_trajectory test_rl_functions 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_get_value_estimates test_normalization 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 FakeTrainer create_rl_trainer dummy_config test_rl_trainer create_mock_brain test_clear_update_buffer test_sac_update_reward_signals create_sac_policy_mock test_process_trajectory 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_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_sac clamp test_simple_ppo Simple1DEnvironment _check_environment_trains test_tensorboard_writer test_stat_reporter_text test_stat_reporter_add_summary_write test_csv_writer mock_env_factory SubprocessEnvManagerTest MockEnvWorker 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 trainer_controller_with_take_step_mocks trainer_controller_with_start_learning_mocks test_start_learning_trains_forever_if_no_train_model test_initialize_ppo_trainer test_handles_no_default_section test_load_config_invalid_yaml test_initialize_invalid_trainer_raises_exception dummy_bad_config dummy_config test_load_config_missing_file test_load_config_valid_yaml test_initialize_trainer_parameters_override_defaults test_raise_if_no_config_for_brain dummy_config_with_override test_trajectory_to_agentbuffer test_split_obs np_zeros_no_float64 np_array_no_float64 _check_no_float64 np_ones_no_float64 VerifyVersionCommand StepResult ActionType AgentGroupSpec BatchedStepResult BaseEnv Communicator UnityEnvironment UnityObservationException UnityWorkerInUseException UnityException UnityCommunicationException UnityTimeOutException UnityEnvironmentException UnityActionException MockCommunicator RpcCommunicator UnityToExternalServicerImplementation agent_group_spec_from_proto _generate_split_indices process_pixels observation_to_np_array batched_step_result_from_proto _process_vector_observation _process_visual_observation TimerNode hierarchical_timer get_timer_root get_timer_tree reset_timers set_gauge timed GaugeNode TimerStack UnityToExternalProtoServicer add_UnityToExternalProtoServicer_to_server UnityToExternalProtoStub EngineConfigurationChannel EngineConfig FloatPropertiesChannel RawBytesChannel SideChannelType SideChannel test_initialization test_reset test_returncode_to_signal_name test_close test_step test_handles_bad_filename test_rpc_communicator_checks_port_on_create test_rpc_communicator_create_multiple_workers test_rpc_communicator_close test_batched_step_result_from_proto generate_compressed_proto_obs test_agent_group_spec_from_proto test_vector_observation test_action_masking_continuous test_process_visual_observation test_action_masking_discrete_1 test_process_pixels test_process_visual_observation_bad_shape test_action_masking_discrete test_action_masking_discrete_2 generate_compressed_data test_process_pixels_gray generate_list_agent_proto generate_uncompressed_proto_obs test_raw_bytes test_int_channel test_float_properties IntChannel test_timers decorated_func NoOpBuildEnvironment BuildEnvironment _Prefix Cache EphemWheelCache WheelCache SimpleWheelCache _normalize_name get_configuration_files _disassemble_key Configuration MultiDomainBasicAuth _get_keyring_auth InsecureHTTPAdapter parse_content_disposition SafeFileCache _copy_file unpack_http_url unpack_vcs_link is_archive_file unpack_file_url get_file_content unpack_url sanitize_content_filename is_vcs_url _progress_indicator _download_url _download_http_url is_file_url url_to_path _check_download_dir looks_like_ci is_dir_url PipSession LocalFSAdapter PipXmlrpcTransport is_url user_agent _get_used_vcs_backend PreviousBuildDirError PipError HashErrors BadCommand RequirementsFileParseError InvalidWheelFilename UninstallationError BestVersionAlreadyInstalled ConfigurationFileCouldNotBeLoaded InstallationError VcsHashUnsupported NoneMetadataError CommandError HashMissing ConfigurationError HashUnpinned DirectoryUrlHashUnsupported UnsupportedPythonVersion UnsupportedWheel HashError DistributionNotFound HashMismatch FoundCandidates _ensure_html_header PackageFinder _ensure_html_response _get_encoding_from_headers _get_html_page _handle_get_page_fail LinkEvaluator _create_link_from_element _get_html_response CandidatePreferences _is_url_like_archive filter_unallowed_hashes HTMLPage _clean_link _check_link_requires_python _determine_base_url _extract_version_from_fragment CandidateEvaluator _find_name_version_sep _match_vcs_scheme _NotHTML _NotHTTP _check_dist_requires_python Resolver distutils_scheme get_src_prefix get_supported get_impl_tag get_abbr_impl get_darwin_arches _is_running_32bit is_manylinux1_compatible get_config_var version_info_to_nodot get_flag is_manylinux2010_compatible get_abi_tag get_all_minor_versions_as_strings get_impl_ver get_impl_version_info get_platform load_pyproject_toml make_pyproject_path _is_list_of_str get_csv_rows_for_installed _contains_egg_info sorted_outrows check_compatibility should_use_ephemeral_cache replace_python_tag rehash open_for_csv move_wheel_files format_command_result normpath wheel_version root_is_purelib get_legacy_build_wheel_path format_tag get_entrypoints fix_script WheelBuilder hash_file message_about_scripts_not_on_PATH Wheel main get_path_completion_type autocomplete auto_complete_paths RequirementCommand Command make_option_group prefer_binary make_search_scope exists_action find_links trusted_host constraints _handle_python_version only_binary add_target_python_options _handle_no_binary _handle_no_cache_dir check_list_path_option editable extra_index_url _handle_merge_hash check_install_build_global _get_format_control requirements make_target_python raise_option_error _handle_only_binary _handle_no_use_pep517 no_binary check_dist_restriction _convert_python_version create_main_parser parse_command invalid_config_error_message UpdatingDefaultsHelpFormatter ConfigOptionParser PrettyHelpFormatter CustomOptionParser CheckCommand CompletionCommand ConfigurationCommand DebugCommand show_value show_tags show_sys_implementation DownloadCommand FreezeCommand HashCommand _hash_of_file HelpCommand InstallCommand get_lib_location_guesses create_env_error_message build_wheels is_wheel_installed tabulate format_for_json ListCommand format_for_columns transform_hits highest_version SearchCommand print_results ShowCommand print_results search_packages_info UninstallCommand WheelCommand _sort_commands get_similar_commands get_summaries AbstractDistribution InstalledDistribution SourceDistribution WheelDistribution make_distribution_for_install_requirement InstallationCandidate FormatControl PackageIndex Link SearchScope SelectionPreferences TargetPython _simulate_installation_of create_package_set_from_installed _create_whitelist check_package_set check_install_conflicts get_requirement_info freeze FrozenRequirement RequirementPreparer deduce_helpful_msg _strip_extras parse_editable install_req_from_req_string install_req_from_editable install_req_from_line ignore_comments join_lines break_args_options preprocess parse_requirements skip_regex expand_env_variables build_parser process_line InstallRequirement RequirementSet RequirementTracker uninstallation_paths compress_for_rename StashedUninstallPathSet compact _unique compress_for_output_listing UninstallPathSet UninstallPthEntries _script_names install_given_reqs user_data_dir _win_path_to_bytes _get_win_folder_from_registry site_config_dirs user_config_dir _get_win_folder_with_ctypes user_cache_dir get_extension_suffixes get_terminal_size str_to_display samefile console_to_str native_str expanduser get_path_uid _showwarning PipDeprecationWarning install_warning_logger deprecated auto_decode check_path_owner check_glibc_version libc_ver glibc_version_string have_compatible_glibc glibc_version_string_ctypes glibc_version_string_confstr Hashes MissingHashes BetterRotatingFileHandler IndentingFormatter get_indentation indent_log MaxLevelFilter _color_wrap _is_broken_pipe_error BrokenStdoutLoggingError ExcludeLoggerFilter ColorizedStreamHandler setup_logging write_delete_marker_file path_to_display split_auth_from_netloc captured_stderr rmtree_errorhandler ask normalize_path get_pip_version get_installed_version ask_password _transform_url ask_path_exists captured_stdout get_installed_distributions redact_password_from_url captured_output _make_build_dir _redact_netloc dist_is_local is_svn_page split_leading_dir renames unpack_file unzip_file consume current_umask file_contents path_to_url normalize_version_info read_chunks _check_no_input redact_netloc format_size splitext get_prog ensure_dir FakeFile backup_dir protect_pip_from_modification_on_windows call_subprocess has_leading_dir is_local untar_file format_command_args dist_in_usersite split_auth_netloc_from_url dist_location remove_auth_from_url dist_is_editable rmtree ask_input is_installable_dir dist_in_site_packages egg_link_path make_subprocess_output_error _get_netloc StreamWrapper display_path cached_property enum KeyBasedCompareMixin pip_version_check was_installed_by_pip SelfCheckState get_requires_python check_requires_python get_metadata get_installer make_setuptools_shim_args AdjacentTempDirectory TempDirectory hidden_cursor InteractiveSpinner InterruptibleMixin DownloadProgressSpinner DownloadFillingCirclesBar BlueEmojiBar DownloadProgressMixin BaseDownloadProgressBar DownloadBlueEmojiProgressBar RateLimiter open_spinner DownloadSilentBar DownloadBar SilentBar DefaultDownloadProgressBar _select_progress_class DownloadProgressProvider NonInteractiveSpinner SpinnerInterface WindowsMixin virtualenv_no_global running_under_virtualenv Bazaar looks_like_hash Git Mercurial Subversion VersionControl RevOptions make_vcs_requirement_url RemoteNotFoundError VcsSupport user_data_dir user_log_dir _get_win_folder_from_registry AppDirs user_config_dir _get_win_folder_with_pywin32 user_state_dir site_config_dir _get_win_folder_with_ctypes site_data_dir user_cache_dir _get_win_folder_with_jna uname_attr uname_info LinuxDistribution lsb_release_attr id codename minor_version major_version name distro_release_attr linux_distribution os_release_info version_parts version build_number info os_release_attr main lsb_release_info like distro_release_info cached_property _BaseAddress summarize_address_range _IPv4Constants _IPAddressBase ip_interface _count_righthand_zero_bits _BaseV6 collapse_addresses AddressValueError IPv6Interface _BaseV4 IPv4Address _compat_to_bytes IPv6Address get_mixed_type_key _compat_bytes_to_byte_vals _split_optional_netmask ip_address ip_network NetmaskValueError v6_int_to_packed _IPv6Constants _find_address_range _compat_bit_length IPv4Network _BaseNetwork v4_int_to_packed _compat_range IPv6Network _collapse_addresses_internal IPv4Interface _TotalOrderingMixin RecursiveGrammarException ParseFatalException _defaultSuccessDebugAction matchPreviousExpr MatchFirst unicode_set oneOf Keyword CloseMatch LineStart _lazyclassproperty withClass ParseSyntaxException locatedExpr srange QuotedString removeQuotes delimitedList countedArray OneOrMore Literal line TokenConverter _makeTags indentedBlock pyparsing_unicode ParseException _escapeRegexRangeChars NotAny CaselessKeyword LineEnd SkipTo _ParseResultsWithOffset _xml_escape StringStart _flatten Suppress makeHTMLTags Word replaceHTMLEntity ParseBaseException White Forward _defaultStartDebugAction originalTextFor infixNotation Token ZeroOrMore Combine OnlyOnce replaceWith dictOf lineno pyparsing_common makeXMLTags Regex StringEnd _defaultExceptionDebugAction Empty Dict ParserElement ParseExpression And _trim_arity GoToColumn CharsNotIn Each CaselessLiteral _MultipleMatch ParseResults nestedExpr traceParseAction WordEnd Optional Or withAttribute Char Group WordStart _NullToken tokenMap ParseElementEnhance PrecededBy matchPreviousLiteral _PositionToken matchOnlyAtCol FollowedBy NoMatch col ungroup nullDebugAction Retrying retry RetryError Attempt _SixMetaPathImporter remove_move u Module_six_moves_urllib_parse python_2_unicode_compatible create_unbound_method add_move assertRaisesRegex Module_six_moves_urllib_request print_ iteritems ensure_binary assertCountEqual MovedAttribute assertRegex _LazyDescr _MovedItems get_unbound_function MovedModule ensure_str Module_six_moves_urllib iterlists wraps with_metaclass Module_six_moves_urllib_error itervalues _add_doc b _LazyModule Module_six_moves_urllib_response ensure_text add_metaclass iterkeys Module_six_moves_urllib_robotparser reraise _import_module vendored CacheControlAdapter DictCache BaseCache CacheController parse_uri CallbackFileWrapper OneDayCache BaseHeuristic ExpiresAfter LastModified expire_after datetime_to_header _b64_decode_bytes _b64_decode_str Serializer CacheControl main get_args get_session setup_logging _secure_open_write FileCache url_to_file_path RedisCache where EUCTWProber Big5Prober Big5DistributionAnalysis EUCTWDistributionAnalysis CharDistributionAnalysis SJISDistributionAnalysis EUCJPDistributionAnalysis GB2312DistributionAnalysis EUCKRDistributionAnalysis CharSetGroupProber CharSetProber CodingStateMachine CP949Prober CharacterCategory MachineState ProbingState LanguageFilter InputState SequenceLikelihood EscCharSetProber EUCJPProber EUCKRProber GB2312Prober HebrewProber SJISContextAnalysis EUCJPContextAnalysis JapaneseContextAnalysis Latin1Prober MultiByteCharSetProber MBCSGroupProber SingleByteCharSetProber SBCSGroupProber SJISProber UniversalDetector UTF8Prober detect main description_of AnsiCodes set_title code_to_chars AnsiCursor AnsiBack clear_line AnsiFore AnsiStyle clear_screen AnsiToWin32 StreamWrapper wrap_stream reinit reset_all init deinit colorama_text WinStyle WinColor WinTerm quote splituser 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FileOperator HTTP is_string_sequence HTTPSHandler SafeTransport ExportEntry parse_name_and_version get_project_data zip_dir CSVWriter _csv_open SubprocessMixin iglob chdir parse_marker parse_requirement Sequencer unarchive get_export_entry proceed CSVBase normalize_name resolve Cache Progress ServerProxy write_exports _get_external_data in_venv HTTPSOnlyHandler get_cache_base Configurator HTTPS ensure_slash get_package_data EventMixin cached_property tempdir get_scheme NormalizedVersion UnsupportedVersionError VersionScheme _suggest_normalized_version LegacyVersion NormalizedMatcher _suggest_semantic_version Version is_semver Matcher _legacy_key SemanticVersion LegacyMatcher _match_prefix SemanticMatcher _pep_440_key _semantic_key Mounter is_compatible compatible_tags Wheel DistlibException RegistryError move unpack_archive _ensure_directory register_unpack_format copy2 _basename _check_unpack_options _make_tarball SpecialFileError ReadError _samefile _call_external_zip unregister_unpack_format _find_unpack_format _unpack_zipfile copyfile unregister_archive_format copytree copyfileobj copystat register_archive_format _unpack_tarfile copy _destinsrc make_archive _get_gid get_unpack_formats Error ExecError ignore_patterns _make_zipfile get_archive_formats rmtree _get_uid copymode _subst_vars _extend_dict _getuserbase get_config_var get_path_names get_scheme_names _expand_vars _init_posix _ensure_cfg_read _get_default_scheme format_value get_python_version get_path parse_config_h _init_non_posix get_paths get_config_vars get_config_h_filename _parse_makefile _print_dict _main _safe_realpath is_python_build _expand_globals get_makefile_filename get_platform _LowLevelFile TarError StreamError filemode ReadError TarFile _StreamProxy itn is_tarfile EmptyHeaderError EOFHeaderError SubsequentHeaderError stn calc_chksums copyfileobj nti HeaderError TarIter InvalidHeaderError nts _BZ2Proxy TarInfo _Stream _FileInFile ExtractError CompressionError ExFileObject TruncatedHeaderError _ReparseException DataLossWarning parseFragment adjust_attributes parse impliedTagToken getPhases ParseError method_decorator_metaclass HTMLParser serialize SerializeError htmlentityreplace_errors HTMLSerializer hexToInt normaliseCharList escapeRegexp charStringToList listToRegexpStr missingRanges InfosetFilter BufferedStream HTMLUnicodeInputStream HTMLBinaryInputStream HTMLInputStream EncodingBytes lookupEncoding EncodingParser ContentAttrParser HTMLTokenizer isSurrogatePair MethodDispatcher surrogatePairToCodepoint moduleFactoryFactory memoize _attr_key Filter Filter Filter Filter Filter Filter Filter collapse_spaces to_genshi to_sax TreeBuilder Node ActiveFormattingElements getDomBuilder getETreeBuilder TreeBuilder DocumentType testSerializer Document tostring getTreeBuilder NonRecursiveTreeWalker TreeWalker TreeWalker getETreeBuilder ensure_str FragmentWrapper TreeWalker Root Doctype FragmentRoot TreeWalker concatenateCharacterTokens pprint getTreeWalker Trie Trie Trie StreamWriter Codec getregentry IncrementalEncoder IncrementalDecoder StreamReader ToASCII nameprep ToUnicode decode IDNAError _is_script check_initial_combiner check_hyphen_ok ulabel check_label check_bidi _combining_class encode valid_string_length uts46_remap alabel InvalidCodepoint InvalidCodepointContext valid_contextj valid_contexto valid_label_length check_nfc _unot _punycode IDNABidiError _decode_range intranges_contain _encode_range intranges_from_list _seg_37 _seg_70 _seg_72 _seg_4 _seg_63 _seg_36 _seg_19 _seg_53 _seg_23 _seg_55 _seg_12 _seg_41 _seg_47 _seg_74 _seg_17 _seg_58 _seg_60 _seg_66 _seg_10 _seg_8 _seg_56 _seg_68 _seg_33 _seg_65 _seg_45 _seg_3 _seg_51 _seg_30 _seg_73 _seg_52 _seg_13 _seg_67 _seg_62 _seg_15 _seg_50 _seg_38 _seg_22 _seg_76 _seg_42 _seg_44 _seg_71 _seg_61 _seg_5 _seg_46 _seg_57 _seg_49 _seg_14 _seg_26 _seg_25 _seg_1 _seg_24 _seg_77 _seg_11 _seg_48 _seg_78 _seg_0 _seg_6 _seg_35 _seg_21 _seg_54 _seg_59 _seg_31 _seg_29 _seg_7 _seg_43 _seg_18 _seg_75 _seg_40 _seg_2 _seg_9 _seg_32 _seg_27 _seg_16 _seg_34 _seg_69 _seg_39 _seg_28 _seg_20 _seg_64 LinkLockFile MkdirLockFile read_pid_from_pidfile write_pid_to_pidfile PIDLockFile remove_existing_pidfile SQLiteLockFile SymlinkLockFile Error LockBase NotLocked _SharedBase LockError LockFailed LockTimeout _fl_helper MkdirFileLock LinkFileLock UnlockError SQLiteFileLock NotMyLock AlreadyLocked locked OutOfData BufferFull ExtraData StackError FormatError UnpackException Packer dict_iteritems _get_data_from_buffer _unpack_from unpack _check_type_strict _is_recursionerror StringIO unpackb Unpacker pack ExtType packb unpack UndefinedEnvironmentName Op Marker _coerce_parse_result _get_env Variable Value UndefinedComparison InvalidMarker default_environment Node _format_marker _eval_op _evaluate_markers format_full_version Requirement InvalidRequirement _version_split BaseSpecifier _IndividualSpecifier Specifier _pad_version _require_version_compare SpecifierSet LegacySpecifier InvalidSpecifier canonicalize_version canonicalize_name parse _BaseVersion InvalidVersion LegacyVersion _parse_local_version Version _legacy_cmpkey _parse_letter_version _cmpkey _parse_version_parts with_metaclass Infinity NegativeInfinity build mkdir_p _do_build main tempdir check_build_wheel check_build_sdist check main ansi _stderr_supports_color enable_colourful_output LogFormatter read_json write_json _load_pyproject build_sdist BuildEnvironment build_wheel BackendUnavailable UnsupportedOperation Pep517HookCaller default_subprocess_runner tempdir build_wheel _find_already_built_wheel BackendUnavailable prepare_metadata_for_build_wheel _DummyException build_sdist _get_wheel_metadata_from_wheel _dist_info_files get_requires_for_build_wheel main get_requires_for_build_sdist GotUnsupportedOperation _build_backend _makedirs_31 _is_egg_path NullProvider _rebuild_mod_path get_build_platform Environment invalid_marker fixup_namespace_packages __getstate__ get_supported_platform _cygwin_patch EggProvider _sset_dict VersionConflict find_eggs_in_zip register_finder ResolutionError _macosx_arch DistributionNotFound register_loader_type safe_listdir load_entry_point dist_factory parse_version WorkingSet ZipManifests UnknownExtra _initialize find_distributions PathMetadata FileMetadata PEP440Warning find_on_path _version_from_file safe_extra file_ns_handler _normalize_cached get_entry_info _bypass_ensure_directory EggInfoDistribution safe_name ExtractionError ResourceManager EggMetadata declare_namespace MemoizedZipManifests DistInfoDistribution get_entry_map _initialize_master_working_set _by_version_descending ContextualVersionConflict normalize_path register_namespace_handler Distribution find_nothing issue_warning parse_requirements IResourceProvider DefaultProvider _always_object resolve_egg_link compatible_platforms evaluate_marker get_provider IMetadataProvider _find_adapter safe_version PkgResourcesDeprecationWarning _sget_dict Requirement RequirementParseError _handle_ns _call_aside _remove_md5_fragment yield_lines ensure_directory null_ns_handler _macosx_vers get_distribution EmptyProvider EntryPoint split_sections get_default_cache ZipProvider _set_parent_ns __setstate__ _ReqExtras _mkstemp distributions_from_metadata run_script _sget_object _is_unpacked_egg NoDists _sset_object non_empty_lines to_filename _declare_state FillingSquaresBar FillingCirclesBar IncrementalBar ChargingBar ShadyBar PixelBar Bar Countdown Stack Counter Pie PieSpinner MoonSpinner Spinner PixelSpinner LineSpinner Progress Infinite TomlError load _p_stmt _Source _p_toml _p_value _p_basicstr_content loads _p_ws _p_key _p_ews translate_to_test format_rfc3339 parse_rfc3339_re _TimeZone parse_rfc3339 dump _escape_id _escape_string dumps _format_value HTTPAdapter BaseAdapter get options request patch delete put post head HTTPProxyAuth _basic_auth_str HTTPDigestAuth AuthBase HTTPBasicAuth MockRequest _copy_cookie_jar cookiejar_from_dict extract_cookies_to_jar CookieConflictError merge_cookies morsel_to_cookie create_cookie MockResponse RequestsCookieJar get_cookie_header remove_cookie_by_name HTTPError ChunkedEncodingError ProxyError Timeout StreamConsumedError URLRequired SSLError InvalidURL InvalidHeader RequestException ConnectionError InvalidSchema FileModeWarning RequestsWarning RequestsDependencyWarning InvalidProxyURL RetryError TooManyRedirects MissingSchema ContentDecodingError ConnectTimeout ReadTimeout UnrewindableBodyError main _implementation info dispatch_hook default_hooks Response RequestEncodingMixin PreparedRequest Request RequestHooksMixin session SessionRedirectMixin merge_setting merge_hooks Session _init CaseInsensitiveDict LookupDict guess_filename prepend_scheme_if_needed address_in_network is_valid_cidr _parse_content_type_header to_key_val_list get_auth_from_url parse_list_header select_proxy stream_decode_response_unicode extract_zipped_paths should_bypass_proxies urldefragauth get_encoding_from_headers set_environ default_user_agent unquote_unreserved super_len proxy_bypass_registry dict_to_sequence dotted_netmask get_unicode_from_response default_headers requote_uri is_ipv4_address rewind_body parse_dict_header proxy_bypass dict_from_cookiejar parse_header_links guess_json_utf unquote_header_value get_netrc_auth check_header_validity get_environ_proxies iter_slices get_encodings_from_content from_key_val_list add_dict_to_cookiejar unicode_is_ascii to_native_string check_compatibility _check_cryptography _match_hostname HTTPSConnection DummyConnection VerifiedHTTPSConnection HTTPConnection ConnectionPool _normalize_host connection_from_url HTTPSConnectionPool HTTPConnectionPool ProtocolError HTTPError ConnectTimeoutError ProxySchemeUnknown DecodeError NewConnectionError HeaderParsingError SSLError InvalidHeader HostChangedError TimeoutError IncompleteRead PoolError InsecureRequestWarning ResponseError ClosedPoolError DependencyWarning MaxRetryError LocationValueError HTTPWarning ResponseNotChunked TimeoutStateError SecurityWarning InsecurePlatformWarning BodyNotHttplibCompatible EmptyPoolError SystemTimeWarning LocationParseError RequestError SubjectAltNameWarning SNIMissingWarning ProxyError ReadTimeoutError UnrewindableBodyError _replace_multiple format_header_param_html5 guess_content_type RequestField format_header_param_rfc2231 encode_multipart_formdata iter_fields iter_field_objects choose_boundary proxy_from_url _default_key_normalizer ProxyManager PoolManager RequestMethods BrotliDecoder DeflateDecoder GzipDecoderState _get_decoder GzipDecoder HTTPResponse MultiDecoder HTTPHeaderDict RecentlyUsedContainer add_stderr_logger disable_warnings AppEngineManager AppEnginePlatformWarning AppEnginePlatformError NTLMConnectionPool WrappedSocket _dnsname_to_stdlib inject_into_urllib3 get_subj_alt_name _validate_dependencies_met PyOpenSSLContext _verify_callback extract_from_urllib3 makefile SecureTransportContext WrappedSocket inject_into_urllib3 extract_from_urllib3 _read_callback _write_callback makefile SOCKSHTTPSConnection SOCKSHTTPSConnectionPool SOCKSHTTPConnectionPool SOCKSProxyManager SOCKSConnection is_appengine is_prod_appengine_mvms is_appengine_sandbox is_local_appengine is_prod_appengine SecurityConst CFConst _assert_no_error _load_client_cert_chain _is_cert _cf_data_from_bytes _cf_string_to_unicode _temporary_keychain _is_identity _load_items_from_file _cf_dictionary_from_tuples _cert_array_from_pem _SixMetaPathImporter remove_move u Module_six_moves_urllib_parse python_2_unicode_compatible create_unbound_method add_move assertRaisesRegex Module_six_moves_urllib_request print_ iteritems assertCountEqual MovedAttribute assertRegex _LazyDescr _MovedItems get_unbound_function MovedModule Module_six_moves_urllib iterlists wraps with_metaclass Module_six_moves_urllib_error itervalues _add_doc b _LazyModule Module_six_moves_urllib_response add_metaclass iterkeys Module_six_moves_urllib_robotparser reraise _import_module backport_makefile is_valid_uri uri_reference normalize_uri urlparse iri_reference URIBuilder to_bytes to_str MissingComponentError InvalidPort InvalidAuthority ResolutionError UnpermittedComponentError PasswordForbidden InvalidComponentsError MissingDependencyError RFC3986Exception ValidationError IRIReference merge_paths encode_component normalize_percent_characters remove_dot_segments normalize_username normalize_path normalize_authority normalize_query normalize_fragment normalize_password normalize_scheme normalize_host authority_from ParseResultBytes ParseResult split_authority ParseResultMixin URIReference check_password authority_is_valid host_is_valid fragment_is_valid is_valid ensure_one_of valid_ipv4_host_address Validator scheme_is_valid query_is_valid subauthority_component_is_valid ensure_components_are_valid ensure_required_components_exist path_is_valid URIMixin match_hostname CertificateError _to_unicode _dnsname_match _ipaddress_match _has_ipv6 allowed_gai_family _set_socket_options is_connection_dropped create_connection LifoQueue set_file_position make_headers rewind_body is_response_to_head is_fp_closed assert_header_parsing Retry resolve_ssl_version resolve_cert_reqs ssl_wrap_socket _is_key_file_encrypted _const_compare_digest_backport is_ipaddress assert_fingerprint create_urllib3_context Timeout split_first Url _encode_invalid_chars get_host parse_url _have_working_poll select_wait_for_socket NoWayToWaitForSocketError wait_for_socket poll_wait_for_socket wait_for_read wait_for_write null_wait_for_socket _retry_on_intr assert_lower generate test_invalid_label test_decode test_iter_decode assert_raises test_all_labels test_encode test_x_user_defined test_iter_encode test_labels StreamWriter Codec IncrementalEncoder IncrementalDecoder StreamReader decode encode _iter_decode_generator _get_encoding iter_decode iter_encode lookup Encoding IncrementalEncoder _iter_encode_generator IncrementalDecoder _detect_bom ascii_lower _makedirs_31 _is_egg_path NullProvider _rebuild_mod_path get_build_platform Environment invalid_marker fixup_namespace_packages __getstate__ get_supported_platform _cygwin_patch EggProvider _sset_dict VersionConflict find_eggs_in_zip register_finder ResolutionError _macosx_arch DistributionNotFound register_loader_type safe_listdir load_entry_point dist_factory parse_version WorkingSet ZipManifests UnknownExtra _initialize find_distributions PathMetadata FileMetadata PEP440Warning find_on_path _version_from_file safe_extra file_ns_handler _normalize_cached get_entry_info _bypass_ensure_directory EggInfoDistribution safe_name ExtractionError ResourceManager EggMetadata declare_namespace MemoizedZipManifests DistInfoDistribution get_entry_map _initialize_master_working_set _by_version_descending ContextualVersionConflict normalize_path register_namespace_handler Distribution find_nothing issue_warning parse_requirements IResourceProvider DefaultProvider _always_object resolve_egg_link compatible_platforms evaluate_marker get_provider IMetadataProvider _find_adapter safe_version PkgResourcesDeprecationWarning _sget_dict Requirement RequirementParseError _handle_ns _call_aside _remove_md5_fragment yield_lines ensure_directory null_ns_handler _macosx_vers get_distribution EmptyProvider EntryPoint split_sections get_default_cache ZipProvider _set_parent_ns __setstate__ _ReqExtras _mkstemp distributions_from_metadata run_script _sget_object _is_unpacked_egg NoDists _sset_object non_empty_lines to_filename _declare_state VendorImporter user_data_dir user_log_dir _get_win_folder_from_registry AppDirs user_config_dir _get_win_folder_with_pywin32 user_state_dir site_config_dir _get_win_folder_with_ctypes site_data_dir user_cache_dir _get_win_folder_with_jna RecursiveGrammarException ParseFatalException _defaultSuccessDebugAction matchPreviousExpr MatchFirst oneOf Keyword CloseMatch LineStart withClass ParseSyntaxException locatedExpr srange QuotedString removeQuotes delimitedList countedArray OneOrMore Literal line TokenConverter _makeTags indentedBlock ParseException _escapeRegexRangeChars NotAny CaselessKeyword LineEnd SkipTo _ParseResultsWithOffset _xml_escape StringStart _flatten Suppress makeHTMLTags Word replaceHTMLEntity ParseBaseException White Forward _defaultStartDebugAction originalTextFor infixNotation Token ZeroOrMore Combine OnlyOnce _ForwardNoRecurse replaceWith dictOf lineno pyparsing_common makeXMLTags Regex StringEnd _defaultExceptionDebugAction Empty Dict ParserElement ParseExpression And _trim_arity GoToColumn CharsNotIn Each CaselessLiteral _MultipleMatch ParseResults nestedExpr traceParseAction WordEnd Optional Or withAttribute Group _Constants WordStart _NullToken tokenMap ParseElementEnhance matchPreviousLiteral _PositionToken matchOnlyAtCol FollowedBy NoMatch col ungroup nullDebugAction _SixMetaPathImporter remove_move u Module_six_moves_urllib_parse python_2_unicode_compatible create_unbound_method add_move assertRaisesRegex Module_six_moves_urllib_request print_ iteritems assertCountEqual MovedAttribute assertRegex _LazyDescr _MovedItems get_unbound_function MovedModule Module_six_moves_urllib iterlists wraps with_metaclass Module_six_moves_urllib_error itervalues _add_doc b _LazyModule Module_six_moves_urllib_response add_metaclass iterkeys Module_six_moves_urllib_robotparser reraise _import_module UndefinedEnvironmentName Op Marker _coerce_parse_result _get_env Variable Value UndefinedComparison InvalidMarker default_environment Node _format_marker _eval_op _evaluate_markers format_full_version Requirement InvalidRequirement _version_split BaseSpecifier _IndividualSpecifier Specifier _pad_version _require_version_compare SpecifierSet LegacySpecifier InvalidSpecifier canonicalize_name parse _BaseVersion InvalidVersion LegacyVersion _parse_local_version Version _legacy_cmpkey _parse_letter_version _cmpkey _parse_version_parts with_metaclass Infinity NegativeInfinity unpack_archive unpack_zipfile unpack_directory unpack_tarfile UnrecognizedFormat default_filter _open_setup_script SetupRequirementsError _BuildMetaBackend _get_immediate_subdirectories Distribution _to_str _BuildMetaLegacyBackend _file_with_extension read_configuration ConfigHandler ConfigMetadataHandler ConfigOptionsHandler configuration_to_dict _get_option parse_configuration get_module_constant _update_globals extract_constant find_module Require newer_pairwise_group check_importable _check_extra _get_unpatched Distribution check_nsp check_test_suite check_requirements check_package_data assert_bool check_entry_points read_pkg_file write_pkg_file check_packages get_metadata_version DistDeprecationWarning check_extras Feature check_specifier assert_string_list Library Extension _have_cython check_glibc_version libc_ver glibc_version_string have_compatible_glibc iglob glob _isrecursive glob0 _iglob escape _rlistdir has_magic glob1 glob2 run DistutilsRefactoringTool Mixin2to3 _get_mro patch_func get_unpatched_function patch_all get_unpatched _patch_distribution_metadata patch_for_msvc_specialized_compiler get_unpatched_class _augment_exception RegistryInfo msvc9_find_vcvarsall SystemInfo msvc14_gen_lib_options EnvironmentInfo PlatformInfo msvc9_query_vcvarsall msvc14_get_vc_env Installer DevelopInstaller decode_entity HashChecker parse_bdist_wininst socket_timeout open_with_auth parse_requirement_arg interpret_distro_name find_external_links htmldecode ContentChecker _encode_auth Credential PyPIConfig unique_everseen unique_values egg_info_for_url PackageIndex _splituser fix_sf_url local_open distros_for_location distros_for_filename distros_for_url get_supported get_impl_tag get_abbr_impl get_darwin_arches _is_running_32bit is_manylinux1_compatible get_config_var get_flag get_abi_tag get_impl_ver get_impl_version_info get_platform get_all_headers Bytecode_compat hide_setuptools _clear_modules ExceptionSaver save_path DirectorySandbox save_argv UnpickleableException override_temp _needs_hiding run_setup SandboxViolation _execfile save_modules pushd save_pkg_resources_state setup_context AbstractSandbox __boot opener_for match_hostname _certifi_where CertificateError find_ca_bundle get_win_certfile _dnsname_match VerifyingHTTPSConn once VerifyingHTTPSHandler filesys_decode try_encode decompose unpack Wheel windows_only hide_file SetuptoolsDeprecationWarning _install_setup_requires PackageFinder setup Command PEP420PackageFinder _find_all_simple findall format_alias shquote alias strip_module make_zipfile _get_purelib write_safety_flag can_scan sorted_walk analyze_egg bdist_egg iter_symbols write_stub scan_module walk_egg bdist_rpm bdist_wininst build_clib get_abi3_suffix link_shared_object build_ext _customize_compiler_for_shlib build_py assert_relative _unique_everseen develop VersionlessRequirement dist_info is_sh chmod _patch_usage EasyInstallDeprecationWarning get_site_dirs get_win_launcher load_launcher_manifest isascii extract_wininst_cfg WindowsExecutableLauncherWriter RewritePthDistributions easy_install is_python ScriptWriter is_64bit _pythonpath current_umask bootstrap update_dist_caches WindowsCommandSpec auto_chmod _remove_and_clear_zip_directory_cache_data get_exe_prefixes _update_zipimporter_cache nt_quote_arg _to_bytes CommandSpec main _first_line_re is_python_script samefile WindowsScriptWriter expand_paths PthDistributions rmtree _uncache _collect_zipimporter_cache_entries write_pkg_info FileList overwrite_arg _write_requirements egg_info write_setup_requirements get_pkg_info_revision EggInfoDeprecationWarning write_requirements write_entries translate_pattern write_toplevel_names write_arg InfoCommon warn_depends_obsolete write_file manifest_maker install install_egg_info install_lib install_scripts sdist_add_defaults register rotate saveopts sdist walk_revctrl option_base edit_config config_file setopt NonDataProperty test ScanningLoader upload _encode upload_docs VendorImporter RecursiveGrammarException ParseFatalException _defaultSuccessDebugAction matchPreviousExpr MatchFirst oneOf Keyword CloseMatch LineStart withClass ParseSyntaxException locatedExpr srange QuotedString removeQuotes delimitedList countedArray OneOrMore Literal line TokenConverter _makeTags indentedBlock ParseException _escapeRegexRangeChars NotAny CaselessKeyword LineEnd SkipTo _ParseResultsWithOffset _xml_escape StringStart _flatten Suppress makeHTMLTags Word replaceHTMLEntity ParseBaseException White Forward _defaultStartDebugAction originalTextFor infixNotation Token ZeroOrMore Combine OnlyOnce _ForwardNoRecurse replaceWith dictOf lineno pyparsing_common makeXMLTags Regex StringEnd _defaultExceptionDebugAction Empty Dict ParserElement ParseExpression And _trim_arity GoToColumn CharsNotIn Each CaselessLiteral _MultipleMatch ParseResults nestedExpr traceParseAction WordEnd Optional Or withAttribute Group _Constants WordStart _NullToken tokenMap ParseElementEnhance matchPreviousLiteral _PositionToken matchOnlyAtCol FollowedBy NoMatch col ungroup nullDebugAction _SixMetaPathImporter remove_move u Module_six_moves_urllib_parse python_2_unicode_compatible create_unbound_method add_move assertRaisesRegex Module_six_moves_urllib_request print_ iteritems assertCountEqual MovedAttribute assertRegex _LazyDescr _MovedItems get_unbound_function MovedModule Module_six_moves_urllib iterlists wraps with_metaclass Module_six_moves_urllib_error itervalues _add_doc b _LazyModule Module_six_moves_urllib_response add_metaclass iterkeys Module_six_moves_urllib_robotparser reraise _import_module UndefinedEnvironmentName Op Marker _coerce_parse_result _get_env Variable Value UndefinedComparison InvalidMarker default_environment Node _format_marker _eval_op _evaluate_markers format_full_version Requirement InvalidRequirement _version_split BaseSpecifier _IndividualSpecifier Specifier _pad_version _require_version_compare SpecifierSet LegacySpecifier InvalidSpecifier canonicalize_name parse _BaseVersion InvalidVersion LegacyVersion _parse_local_version Version _legacy_cmpkey _parse_letter_version _cmpkey _parse_version_parts with_metaclass Infinity NegativeInfinity NoOpBuildEnvironment BuildEnvironment _Prefix Cache EphemWheelCache WheelCache SimpleWheelCache _ensure_html_header group_locations _ensure_html_response _get_encoding_from_headers _get_html_page _handle_get_page_fail _create_link_from_element _get_html_response LinkCollector _is_url_like_archive _remove_duplicate_links HTMLPage CollectedLinks _clean_link _make_html_page _determine_base_url _match_vcs_scheme _NotHTML parse_links _NotHTTP _normalize_name get_configuration_files _disassemble_key Configuration _copy_source_tree _progress_indicator unpack_file_url parse_content_disposition _download_url _copy_file _copy2_ignoring_special_files get_file_content unpack_url sanitize_content_filename _download_http_url unpack_http_url unpack_vcs_link _get_used_vcs_backend _check_download_dir PreviousBuildDirError PipError HashErrors BadCommand RequirementsFileParseError InvalidWheelFilename UninstallationError BestVersionAlreadyInstalled ConfigurationFileCouldNotBeLoaded InstallationError VcsHashUnsupported NoneMetadataError CommandError HashMissing ConfigurationError HashUnpinned DirectoryUrlHashUnsupported UnsupportedPythonVersion UnsupportedWheel HashError DistributionNotFound HashMismatch _extract_version_from_fragment PackageFinder _check_link_requires_python CandidateEvaluator CandidatePreferences filter_unallowed_hashes _find_name_version_sep BestCandidateResult LinkEvaluator _check_dist_requires_python Resolver get_major_minor_version distutils_scheme get_src_prefix main get_supported get_impl_tag get_abbr_impl is_linux_armhf is_manylinux2014_compatible _is_running_32bit is_manylinux1_compatible get_darwin_arches get_config_var version_info_to_nodot get_flag is_manylinux2010_compatible get_abi_tag get_all_minor_versions_as_strings get_impl_ver get_impl_version_info get_platform load_pyproject_toml make_pyproject_path _is_list_of_str make_link_collector was_installed_by_pip SelfCheckState pip_self_version_check _get_statefile_name get_csv_rows_for_installed _contains_egg_info sorted_outrows check_compatibility should_use_ephemeral_cache replace_python_tag rehash open_for_csv PipScriptMaker MissingCallableSuffix move_wheel_files format_command_result normpath wheel_version root_is_purelib get_legacy_build_wheel_path format_tag get_entrypoints fix_script WheelBuilder hash_file _always_true message_about_scripts_not_on_PATH _raise_for_invalid_entrypoint Wheel get_path_completion_type autocomplete auto_complete_paths Command make_option_group prefer_binary exists_action find_links trusted_host constraints _handle_python_version only_binary add_target_python_options _handle_no_binary _handle_no_cache_dir check_list_path_option editable extra_index_url _handle_merge_hash check_install_build_global _get_format_control requirements make_target_python raise_option_error _handle_only_binary _handle_no_use_pep517 no_binary check_dist_restriction _convert_python_version CommandContextMixIn create_main_parser parse_command invalid_config_error_message UpdatingDefaultsHelpFormatter ConfigOptionParser PrettyHelpFormatter CustomOptionParser RequirementCommand SessionCommandMixin IndexGroupCommand CheckCommand CompletionCommand ConfigurationCommand DebugCommand show_value show_tags show_sys_implementation DownloadCommand FreezeCommand HashCommand _hash_of_file HelpCommand InstallCommand get_lib_location_guesses get_check_binary_allowed build_wheels create_env_error_message is_wheel_installed tabulate format_for_json ListCommand format_for_columns transform_hits highest_version SearchCommand print_results ShowCommand print_results search_packages_info UninstallCommand WheelCommand create_command get_similar_commands AbstractDistribution InstalledDistribution WheelDistribution make_distribution_for_install_requirement SourceDistribution InstallationCandidate FormatControl PackageIndex Link SearchScope SelectionPreferences TargetPython get_keyring_auth MultiDomainBasicAuth suppressed_cache_errors SafeFileCache looks_like_ci InsecureHTTPAdapter PipSession LocalFSAdapter user_agent PipXmlrpcTransport _simulate_installation_of create_package_set_from_installed _create_whitelist check_package_set check_install_conflicts get_requirement_info freeze FrozenRequirement _generate_metadata_legacy _generate_metadata get_metadata_generator _find_egg_info RequirementPreparer _get_prepared_distribution _looks_like_path deduce_helpful_msg is_archive_file _strip_extras parse_req_from_editable parse_editable RequirementParts _get_url_from_path convert_extras install_req_from_editable parse_req_from_line install_req_from_req_string install_req_from_line ignore_comments join_lines break_args_options preprocess parse_requirements skip_regex expand_env_variables build_parser process_line InstallRequirement RequirementSet RequirementTracker uninstallation_paths compress_for_rename StashedUninstallPathSet compact _unique compress_for_output_listing UninstallPathSet UninstallPthEntries _script_names install_given_reqs user_data_dir _win_path_to_bytes _get_win_folder_from_registry site_config_dirs user_config_dir _get_win_folder_with_ctypes user_cache_dir get_extension_suffixes get_terminal_size str_to_display samefile console_to_str backslashreplace_decode_fn native_str expanduser get_path_uid _showwarning PipDeprecationWarning install_warning_logger deprecated auto_decode NamedTemporaryFileResult replace is_socket check_path_owner copy2_fixed adjacent_tmp_file check_glibc_version libc_ver glibc_version_string have_compatible_glibc glibc_version_string_ctypes glibc_version_string_confstr Hashes MissingHashes inject_securetransport BetterRotatingFileHandler IndentingFormatter get_indentation indent_log MaxLevelFilter _color_wrap _is_broken_pipe_error BrokenStdoutLoggingError ExcludeLoggerFilter ColorizedStreamHandler setup_logging has_delete_marker_file write_delete_marker_file hide_url path_to_display parse_netloc split_auth_from_netloc captured_stderr rmtree_errorhandler ask normalize_path get_pip_version get_installed_version ask_password _transform_url ask_path_exists captured_stdout get_installed_distributions captured_output _make_build_dir _redact_netloc dist_is_local renames redact_auth_from_url consume normalize_version_info build_url_from_netloc read_chunks _check_no_input build_netloc HiddenText redact_netloc format_size splitext get_prog ensure_dir write_output backup_dir FakeFile protect_pip_from_modification_on_windows is_local dist_in_usersite split_auth_netloc_from_url dist_location remove_auth_from_url dist_is_editable hide_value rmtree ask_input is_installable_dir dist_in_site_packages egg_link_path _get_netloc StreamWrapper display_path cached_property enum is_console_interactive KeyBasedCompareMixin get_requires_python check_requires_python get_metadata get_installer make_setuptools_shim_args call_subprocess make_command runner_with_spinner_message format_command_args reveal_command_args make_subprocess_output_error AdjacentTempDirectory TempDirectory hidden_cursor InteractiveSpinner InterruptibleMixin DownloadProgressSpinner DownloadFillingCirclesBar BlueEmojiBar DownloadProgressMixin BaseDownloadProgressBar DownloadBlueEmojiProgressBar RateLimiter open_spinner DownloadSilentBar DownloadBar SilentBar DefaultDownloadProgressBar _select_progress_class DownloadProgressProvider NonInteractiveSpinner SpinnerInterface WindowsMixin has_leading_dir untar_file is_within_directory split_leading_dir unpack_file unzip_file current_umask get_url_scheme path_to_url url_to_path virtualenv_no_global running_under_virtualenv Bazaar looks_like_hash Git Mercurial Subversion VersionControl RevOptions make_vcs_requirement_url is_url RemoteNotFoundError find_path_to_setup_from_repo_root VcsSupport user_data_dir user_log_dir _get_win_folder_from_registry AppDirs user_config_dir _get_win_folder_with_pywin32 user_state_dir site_config_dir _get_win_folder_with_ctypes site_data_dir user_cache_dir _get_win_folder_with_jna suppress _classic_mro redirect_stdout _GeneratorContextManager contextmanager closing _RedirectStream _reraise_with_existing_context nullcontext ContextStack _check_methods redirect_stderr _make_context_fixer AbstractContextManager ContextDecorator ExitStack uname_attr uname_info LinuxDistribution lsb_release_attr id codename minor_version major_version name distro_release_attr linux_distribution os_release_info version_parts version build_number info os_release_attr main lsb_release_info like distro_release_info cached_property _BaseAddress summarize_address_range _IPv4Constants _IPAddressBase ip_interface _count_righthand_zero_bits _BaseV6 collapse_addresses AddressValueError IPv6Interface _BaseV4 IPv4Address _compat_to_bytes IPv6Address get_mixed_type_key _compat_bytes_to_byte_vals _split_optional_netmask ip_address ip_network NetmaskValueError v6_int_to_packed _IPv6Constants _find_address_range _compat_bit_length IPv4Network _BaseNetwork v4_int_to_packed _compat_range IPv6Network _collapse_addresses_internal IPv4Interface _TotalOrderingMixin RecursiveGrammarException ParseFatalException _defaultSuccessDebugAction matchPreviousExpr MatchFirst unicode_set oneOf Keyword CloseMatch LineStart conditionAsParseAction _lazyclassproperty withClass ParseSyntaxException locatedExpr srange QuotedString removeQuotes delimitedList countedArray OneOrMore Literal line TokenConverter _makeTags indentedBlock pyparsing_unicode ParseException _escapeRegexRangeChars NotAny CaselessKeyword LineEnd SkipTo _ParseResultsWithOffset _xml_escape StringStart _flatten Suppress makeHTMLTags Word replaceHTMLEntity _WordRegex ParseBaseException White Forward _defaultStartDebugAction originalTextFor infixNotation Token ZeroOrMore Combine OnlyOnce replaceWith dictOf lineno pyparsing_common makeXMLTags Regex StringEnd _defaultExceptionDebugAction _PendingSkip Empty Dict ParserElement ParseExpression And _trim_arity GoToColumn CharsNotIn Each CaselessLiteral _MultipleMatch ParseResults nestedExpr traceParseAction WordEnd Optional Or _SingleCharLiteral withAttribute Char Group WordStart _NullToken tokenMap ParseElementEnhance PrecededBy matchPreviousLiteral _PositionToken matchOnlyAtCol FollowedBy NoMatch col ungroup nullDebugAction Retrying retry RetryError Attempt _SixMetaPathImporter remove_move u Module_six_moves_urllib_parse python_2_unicode_compatible create_unbound_method add_move assertRaisesRegex Module_six_moves_urllib_request print_ iteritems ensure_binary assertCountEqual MovedAttribute assertRegex _LazyDescr _MovedItems get_unbound_function MovedModule ensure_str Module_six_moves_urllib iterlists wraps with_metaclass Module_six_moves_urllib_error itervalues _add_doc b _LazyModule Module_six_moves_urllib_response ensure_text add_metaclass iterkeys Module_six_moves_urllib_robotparser reraise _import_module vendored CacheControlAdapter DictCache BaseCache CacheController parse_uri CallbackFileWrapper OneDayCache BaseHeuristic ExpiresAfter LastModified expire_after datetime_to_header _b64_decode_bytes _b64_decode_str Serializer CacheControl main get_args get_session setup_logging _secure_open_write FileCache url_to_file_path RedisCache where EUCTWProber Big5Prober Big5DistributionAnalysis EUCTWDistributionAnalysis CharDistributionAnalysis SJISDistributionAnalysis EUCJPDistributionAnalysis GB2312DistributionAnalysis EUCKRDistributionAnalysis CharSetGroupProber CharSetProber CodingStateMachine CP949Prober CharacterCategory MachineState ProbingState LanguageFilter InputState SequenceLikelihood EscCharSetProber EUCJPProber EUCKRProber GB2312Prober HebrewProber SJISContextAnalysis EUCJPContextAnalysis JapaneseContextAnalysis Latin1Prober MultiByteCharSetProber MBCSGroupProber SingleByteCharSetProber SBCSGroupProber SJISProber UniversalDetector UTF8Prober detect main description_of AnsiCodes set_title code_to_chars AnsiCursor AnsiBack clear_line AnsiFore AnsiStyle clear_screen AnsiToWin32 StreamWrapper wrap_stream reinit reset_all init deinit colorama_text WinStyle WinColor WinTerm quote splituser InstalledDistribution make_dist DistributionPath DependencyGraph get_dependent_dists get_required_dists Distribution EggInfoDistribution BaseInstalledDistribution _Cache make_graph PackageIndex RedirectHandler get_all_distribution_names JSONLocator PyPIRPCLocator Page DependencyFinder DirectoryLocator PyPIJSONLocator DistPathLocator SimpleScrapingLocator Locator AggregatingLocator Manifest interpret Evaluator _is_literal default_context MetadataConflictError MetadataMissingError LegacyMetadata _best_version _get_name_and_version Metadata _version2fieldlist MetadataInvalidError MetadataUnrecognizedVersionError ZipResourceFinder ResourceFinder ResourceCache ResourceBase Resource register_finder finder ResourceContainer finder_for_path _enquote_executable ScriptMaker split_filename HTTPSConnection get_executable read_exports get_process_umask Transport CSVReader path_to_cache_dir convert_path get_extras _iglob socket_timeout parse_credentials extract_by_key get_resources_dests FileOperator HTTP is_string_sequence HTTPSHandler SafeTransport ExportEntry parse_name_and_version get_project_data zip_dir CSVWriter _csv_open SubprocessMixin iglob chdir parse_marker parse_requirement Sequencer unarchive get_export_entry proceed CSVBase normalize_name resolve Cache Progress ServerProxy write_exports _get_external_data in_venv HTTPSOnlyHandler get_cache_base Configurator HTTPS ensure_slash get_package_data EventMixin cached_property tempdir get_scheme NormalizedVersion UnsupportedVersionError VersionScheme _suggest_normalized_version LegacyVersion NormalizedMatcher _suggest_semantic_version Version is_semver Matcher _legacy_key SemanticVersion LegacyMatcher _match_prefix SemanticMatcher _pep_440_key _semantic_key Mounter is_compatible compatible_tags Wheel DistlibException RegistryError move unpack_archive _ensure_directory register_unpack_format copy2 _basename _check_unpack_options _make_tarball SpecialFileError ReadError _samefile _call_external_zip unregister_unpack_format _find_unpack_format _unpack_zipfile copyfile unregister_archive_format copytree copyfileobj copystat register_archive_format _unpack_tarfile copy _destinsrc make_archive _get_gid get_unpack_formats Error ExecError ignore_patterns _make_zipfile get_archive_formats rmtree _get_uid copymode _subst_vars _extend_dict _getuserbase get_config_var get_path_names get_scheme_names _expand_vars _init_posix _ensure_cfg_read _get_default_scheme format_value get_python_version get_path parse_config_h _init_non_posix get_paths get_config_vars get_config_h_filename _parse_makefile _print_dict _main _safe_realpath is_python_build _expand_globals get_makefile_filename get_platform _LowLevelFile TarError StreamError filemode ReadError TarFile _StreamProxy itn is_tarfile EmptyHeaderError EOFHeaderError SubsequentHeaderError stn calc_chksums copyfileobj nti HeaderError TarIter InvalidHeaderError nts _BZ2Proxy TarInfo _Stream _FileInFile ExtractError CompressionError ExFileObject TruncatedHeaderError _ReparseException DataLossWarning parseFragment adjust_attributes parse impliedTagToken getPhases ParseError method_decorator_metaclass HTMLParser serialize SerializeError htmlentityreplace_errors HTMLSerializer hexToInt normaliseCharList escapeRegexp charStringToList listToRegexpStr missingRanges InfosetFilter BufferedStream HTMLUnicodeInputStream HTMLBinaryInputStream HTMLInputStream EncodingBytes lookupEncoding EncodingParser ContentAttrParser HTMLTokenizer isSurrogatePair MethodDispatcher surrogatePairToCodepoint moduleFactoryFactory memoize _attr_key Filter Filter Filter Filter Filter Filter Filter collapse_spaces to_genshi to_sax TreeBuilder Node ActiveFormattingElements getDomBuilder getETreeBuilder TreeBuilder DocumentType testSerializer Document tostring getTreeBuilder NonRecursiveTreeWalker TreeWalker TreeWalker getETreeBuilder ensure_str FragmentWrapper TreeWalker Root Doctype FragmentRoot TreeWalker concatenateCharacterTokens pprint getTreeWalker Trie Trie Trie StreamWriter Codec getregentry IncrementalEncoder IncrementalDecoder StreamReader ToASCII nameprep ToUnicode decode IDNAError _is_script check_initial_combiner check_hyphen_ok ulabel check_label check_bidi _combining_class encode valid_string_length uts46_remap alabel InvalidCodepoint InvalidCodepointContext valid_contextj valid_contexto valid_label_length check_nfc _unot _punycode IDNABidiError _decode_range intranges_contain _encode_range intranges_from_list _seg_37 _seg_70 _seg_72 _seg_4 _seg_63 _seg_36 _seg_19 _seg_53 _seg_23 _seg_55 _seg_12 _seg_41 _seg_47 _seg_74 _seg_17 _seg_58 _seg_60 _seg_66 _seg_10 _seg_8 _seg_56 _seg_68 _seg_33 _seg_65 _seg_45 _seg_3 _seg_51 _seg_30 _seg_73 _seg_52 _seg_13 _seg_67 _seg_62 _seg_15 _seg_50 _seg_38 _seg_22 _seg_76 _seg_42 _seg_44 _seg_71 _seg_61 _seg_5 _seg_46 _seg_57 _seg_49 _seg_14 _seg_26 _seg_25 _seg_1 _seg_24 _seg_77 _seg_11 _seg_48 _seg_78 _seg_0 _seg_6 _seg_35 _seg_21 _seg_54 _seg_59 _seg_31 _seg_29 _seg_7 _seg_43 _seg_18 _seg_75 _seg_40 _seg_2 _seg_9 _seg_32 _seg_27 _seg_16 _seg_34 _seg_69 _seg_39 _seg_28 _seg_20 _seg_64 OutOfData BufferFull ExtraData StackError FormatError UnpackException Packer dict_iteritems _get_data_from_buffer _unpack_from unpack _check_type_strict _is_recursionerror StringIO unpackb Unpacker pack ExtType packb unpack UndefinedEnvironmentName Op Marker _coerce_parse_result _get_env Variable Value UndefinedComparison InvalidMarker default_environment Node _format_marker _eval_op _evaluate_markers format_full_version Requirement InvalidRequirement _version_split BaseSpecifier _IndividualSpecifier Specifier _pad_version _require_version_compare SpecifierSet LegacySpecifier InvalidSpecifier _cpython_interpreter _mac_binary_formats _generic_interpreter _mac_arch _pypy_interpreter _interpreter_name _check_glibc_version _mac_platforms _cpython_abis _normalize_string parse_tag _independent_tags Tag _generic_abi _cpython_tags _glibc_version_string _linux_platforms _py_interpreter_range sys_tags _generic_platforms _have_compatible_glibc _pypy_tags _generic_tags _is_manylinux_compatible canonicalize_version canonicalize_name parse _BaseVersion InvalidVersion LegacyVersion _parse_local_version Version _legacy_cmpkey _parse_letter_version _cmpkey _parse_version_parts with_metaclass Infinity NegativeInfinity validate_system build compat_system _do_build load_system main check_build_wheel check_build_sdist check main ansi _stderr_supports_color enable_colourful_output LogFormatter read_json write_json dir_to_zipfile mkdir_p tempdir _load_pyproject build_sdist BuildEnvironment build_wheel load build_as_zip build _prep_meta main BackendUnavailable HookMissing UnsupportedOperation BackendInvalid Pep517HookCaller default_subprocess_runner LoggerWrapper norm_and_check tempdir quiet_subprocess_runner build_wheel contained_in _find_already_built_wheel BackendUnavailable HookMissing prepare_metadata_for_build_wheel _DummyException BackendInvalid build_sdist _get_wheel_metadata_from_wheel _dist_info_files get_requires_for_build_wheel main get_requires_for_build_sdist GotUnsupportedOperation _build_backend _makedirs_31 _is_egg_path NullProvider _rebuild_mod_path get_build_platform Environment invalid_marker fixup_namespace_packages __getstate__ get_supported_platform _cygwin_patch EggProvider _sset_dict VersionConflict find_eggs_in_zip register_finder ResolutionError _macosx_arch DistributionNotFound register_loader_type safe_listdir load_entry_point dist_factory parse_version WorkingSet ZipManifests UnknownExtra _initialize find_distributions PathMetadata FileMetadata PEP440Warning find_on_path _version_from_file safe_extra file_ns_handler _normalize_cached get_entry_info _bypass_ensure_directory EggInfoDistribution safe_name ExtractionError ResourceManager EggMetadata declare_namespace MemoizedZipManifests DistInfoDistribution get_entry_map _initialize_master_working_set _by_version_descending ContextualVersionConflict normalize_path register_namespace_handler Distribution find_nothing issue_warning parse_requirements IResourceProvider DefaultProvider _always_object resolve_egg_link compatible_platforms evaluate_marker get_provider IMetadataProvider _find_adapter safe_version PkgResourcesDeprecationWarning _sget_dict Requirement RequirementParseError _handle_ns _call_aside _remove_md5_fragment yield_lines ensure_directory null_ns_handler _macosx_vers get_distribution EmptyProvider EntryPoint split_sections get_default_cache ZipProvider _set_parent_ns __setstate__ _ReqExtras _mkstemp distributions_from_metadata run_script _sget_object _is_unpacked_egg NoDists _sset_object non_empty_lines to_filename _declare_state FillingSquaresBar FillingCirclesBar IncrementalBar ChargingBar ShadyBar PixelBar Bar Countdown Stack Counter Pie PieSpinner MoonSpinner Spinner PixelSpinner LineSpinner Progress Infinite TomlError load _p_stmt _Source _p_toml _p_value _p_basicstr_content loads _p_ws _p_key _p_ews translate_to_test format_rfc3339 parse_rfc3339_re _TimeZone parse_rfc3339 dump _escape_id _escape_string dumps _format_value HTTPAdapter BaseAdapter get options request patch delete put post head HTTPProxyAuth _basic_auth_str HTTPDigestAuth AuthBase HTTPBasicAuth MockRequest _copy_cookie_jar cookiejar_from_dict extract_cookies_to_jar CookieConflictError merge_cookies morsel_to_cookie create_cookie MockResponse RequestsCookieJar get_cookie_header remove_cookie_by_name HTTPError ChunkedEncodingError ProxyError Timeout StreamConsumedError URLRequired SSLError InvalidURL InvalidHeader RequestException ConnectionError InvalidSchema FileModeWarning RequestsWarning RequestsDependencyWarning InvalidProxyURL RetryError TooManyRedirects MissingSchema ContentDecodingError ConnectTimeout ReadTimeout UnrewindableBodyError main _implementation info dispatch_hook default_hooks Response RequestEncodingMixin PreparedRequest Request RequestHooksMixin session SessionRedirectMixin merge_setting merge_hooks Session _init CaseInsensitiveDict LookupDict guess_filename prepend_scheme_if_needed address_in_network is_valid_cidr _parse_content_type_header to_key_val_list get_auth_from_url parse_list_header select_proxy stream_decode_response_unicode extract_zipped_paths should_bypass_proxies urldefragauth get_encoding_from_headers set_environ default_user_agent unquote_unreserved super_len proxy_bypass_registry dict_to_sequence dotted_netmask get_unicode_from_response default_headers requote_uri is_ipv4_address rewind_body parse_dict_header proxy_bypass dict_from_cookiejar parse_header_links guess_json_utf unquote_header_value get_netrc_auth check_header_validity get_environ_proxies iter_slices get_encodings_from_content from_key_val_list add_dict_to_cookiejar unicode_is_ascii to_native_string check_compatibility _check_cryptography _match_hostname HTTPSConnection DummyConnection VerifiedHTTPSConnection HTTPConnection ConnectionPool _normalize_host connection_from_url HTTPSConnectionPool HTTPConnectionPool ProtocolError HTTPError ConnectTimeoutError ProxySchemeUnknown DecodeError NewConnectionError HeaderParsingError SSLError InvalidHeader HostChangedError TimeoutError IncompleteRead PoolError InsecureRequestWarning ResponseError ClosedPoolError DependencyWarning MaxRetryError LocationValueError HTTPWarning ResponseNotChunked TimeoutStateError SecurityWarning InsecurePlatformWarning BodyNotHttplibCompatible EmptyPoolError SystemTimeWarning LocationParseError RequestError SubjectAltNameWarning SNIMissingWarning ProxyError ReadTimeoutError UnrewindableBodyError _replace_multiple format_header_param_html5 guess_content_type RequestField format_header_param_rfc2231 encode_multipart_formdata iter_fields iter_field_objects choose_boundary proxy_from_url _default_key_normalizer ProxyManager PoolManager RequestMethods BrotliDecoder DeflateDecoder GzipDecoderState _get_decoder GzipDecoder HTTPResponse MultiDecoder HTTPHeaderDict RecentlyUsedContainer add_stderr_logger disable_warnings AppEngineManager AppEnginePlatformWarning AppEnginePlatformError NTLMConnectionPool WrappedSocket _dnsname_to_stdlib inject_into_urllib3 get_subj_alt_name _validate_dependencies_met PyOpenSSLContext _verify_callback extract_from_urllib3 makefile SecureTransportContext WrappedSocket inject_into_urllib3 extract_from_urllib3 _read_callback _write_callback makefile SOCKSHTTPSConnection SOCKSHTTPSConnectionPool SOCKSHTTPConnectionPool SOCKSProxyManager SOCKSConnection is_appengine is_prod_appengine_mvms is_appengine_sandbox is_local_appengine is_prod_appengine SecurityConst CFConst _assert_no_error _load_client_cert_chain _is_cert _cf_data_from_bytes _cf_string_to_unicode _temporary_keychain _is_identity _load_items_from_file _cf_dictionary_from_tuples _cert_array_from_pem _SixMetaPathImporter remove_move u Module_six_moves_urllib_parse python_2_unicode_compatible create_unbound_method add_move assertRaisesRegex Module_six_moves_urllib_request print_ iteritems ensure_binary assertCountEqual MovedAttribute assertRegex _LazyDescr _MovedItems get_unbound_function MovedModule ensure_str Module_six_moves_urllib iterlists wraps with_metaclass Module_six_moves_urllib_error itervalues _add_doc b _LazyModule Module_six_moves_urllib_response ensure_text add_metaclass iterkeys Module_six_moves_urllib_robotparser reraise _import_module backport_makefile match_hostname CertificateError _to_unicode _dnsname_match _ipaddress_match _has_ipv6 allowed_gai_family _set_socket_options is_connection_dropped create_connection LifoQueue set_file_position make_headers rewind_body is_response_to_head is_fp_closed assert_header_parsing Retry resolve_ssl_version resolve_cert_reqs ssl_wrap_socket _is_key_file_encrypted _const_compare_digest_backport is_ipaddress assert_fingerprint create_urllib3_context Timeout split_first _idna_encode Url _encode_target _normalize_host _encode_invalid_chars get_host _remove_path_dot_segments parse_url _have_working_poll select_wait_for_socket NoWayToWaitForSocketError wait_for_socket poll_wait_for_socket wait_for_read wait_for_write null_wait_for_socket _retry_on_intr assert_lower generate test_invalid_label test_decode test_iter_decode assert_raises test_all_labels test_encode test_x_user_defined test_iter_encode test_labels StreamWriter Codec IncrementalEncoder IncrementalDecoder StreamReader decode encode _iter_decode_generator _get_encoding iter_decode iter_encode lookup Encoding IncrementalEncoder _iter_encode_generator IncrementalDecoder _detect_bom ascii_lower _makedirs_31 _is_egg_path NullProvider _rebuild_mod_path get_build_platform Environment invalid_marker fixup_namespace_packages __getstate__ get_supported_platform _cygwin_patch EggProvider _sset_dict VersionConflict find_eggs_in_zip register_finder ResolutionError _macosx_arch DistributionNotFound register_loader_type safe_listdir load_entry_point dist_factory parse_version WorkingSet ZipManifests UnknownExtra _initialize find_distributions PathMetadata FileMetadata PEP440Warning find_on_path _version_from_file safe_extra file_ns_handler _normalize_cached get_entry_info _bypass_ensure_directory EggInfoDistribution safe_name ExtractionError ResourceManager EggMetadata declare_namespace MemoizedZipManifests DistInfoDistribution get_entry_map _initialize_master_working_set _by_version_descending ContextualVersionConflict normalize_path register_namespace_handler Distribution find_nothing issue_warning parse_requirements IResourceProvider DefaultProvider _always_object resolve_egg_link compatible_platforms evaluate_marker get_provider IMetadataProvider _find_adapter safe_version PkgResourcesDeprecationWarning _sget_dict Requirement RequirementParseError _handle_ns _call_aside _remove_md5_fragment yield_lines ensure_directory null_ns_handler _macosx_vers get_distribution EmptyProvider EntryPoint split_sections get_default_cache ZipProvider _set_parent_ns __setstate__ _ReqExtras _mkstemp distributions_from_metadata run_script _sget_object _is_unpacked_egg NoDists _sset_object non_empty_lines to_filename _declare_state VendorImporter user_data_dir user_log_dir _get_win_folder_from_registry AppDirs user_config_dir _get_win_folder_with_pywin32 user_state_dir site_config_dir _get_win_folder_with_ctypes site_data_dir user_cache_dir _get_win_folder_with_jna RecursiveGrammarException ParseFatalException _defaultSuccessDebugAction matchPreviousExpr MatchFirst oneOf Keyword CloseMatch LineStart withClass ParseSyntaxException locatedExpr srange QuotedString removeQuotes delimitedList countedArray OneOrMore Literal line TokenConverter _makeTags indentedBlock ParseException _escapeRegexRangeChars NotAny CaselessKeyword LineEnd SkipTo _ParseResultsWithOffset _xml_escape StringStart _flatten Suppress makeHTMLTags Word replaceHTMLEntity ParseBaseException White Forward _defaultStartDebugAction originalTextFor infixNotation Token ZeroOrMore Combine OnlyOnce _ForwardNoRecurse replaceWith dictOf lineno pyparsing_common makeXMLTags Regex StringEnd _defaultExceptionDebugAction Empty Dict ParserElement ParseExpression And _trim_arity GoToColumn CharsNotIn Each CaselessLiteral _MultipleMatch ParseResults nestedExpr traceParseAction WordEnd Optional Or withAttribute Group _Constants WordStart _NullToken tokenMap ParseElementEnhance matchPreviousLiteral _PositionToken matchOnlyAtCol FollowedBy NoMatch col ungroup nullDebugAction _SixMetaPathImporter remove_move u Module_six_moves_urllib_parse python_2_unicode_compatible create_unbound_method add_move assertRaisesRegex Module_six_moves_urllib_request print_ iteritems assertCountEqual MovedAttribute assertRegex _LazyDescr _MovedItems get_unbound_function MovedModule Module_six_moves_urllib iterlists wraps with_metaclass Module_six_moves_urllib_error itervalues _add_doc b _LazyModule Module_six_moves_urllib_response add_metaclass iterkeys Module_six_moves_urllib_robotparser reraise _import_module UndefinedEnvironmentName Op Marker _coerce_parse_result _get_env Variable Value UndefinedComparison InvalidMarker default_environment Node _format_marker _eval_op _evaluate_markers format_full_version Requirement InvalidRequirement _version_split BaseSpecifier _IndividualSpecifier Specifier _pad_version _require_version_compare SpecifierSet LegacySpecifier InvalidSpecifier canonicalize_name parse _BaseVersion InvalidVersion LegacyVersion _parse_local_version Version _legacy_cmpkey _parse_letter_version _cmpkey _parse_version_parts with_metaclass Infinity NegativeInfinity unpack_archive unpack_zipfile unpack_directory unpack_tarfile UnrecognizedFormat default_filter _open_setup_script SetupRequirementsError _BuildMetaBackend _get_immediate_subdirectories Distribution _to_str _BuildMetaLegacyBackend _file_with_extension read_configuration ConfigHandler ConfigMetadataHandler ConfigOptionsHandler configuration_to_dict _get_option parse_configuration get_module_constant _update_globals extract_constant find_module Require newer_pairwise_group check_importable _check_extra _get_unpatched Distribution check_nsp check_test_suite check_requirements check_package_data assert_bool check_entry_points read_pkg_file write_pkg_file check_packages get_metadata_version DistDeprecationWarning check_extras Feature check_specifier assert_string_list Library Extension _have_cython check_glibc_version libc_ver glibc_version_string have_compatible_glibc iglob glob _isrecursive glob0 _iglob escape _rlistdir has_magic glob1 glob2 run DistutilsRefactoringTool Mixin2to3 _get_mro patch_func get_unpatched_function patch_all get_unpatched _patch_distribution_metadata patch_for_msvc_specialized_compiler get_unpatched_class _augment_exception RegistryInfo msvc9_find_vcvarsall SystemInfo msvc14_gen_lib_options EnvironmentInfo PlatformInfo msvc9_query_vcvarsall msvc14_get_vc_env Installer DevelopInstaller decode_entity HashChecker parse_bdist_wininst socket_timeout open_with_auth parse_requirement_arg interpret_distro_name find_external_links htmldecode ContentChecker _encode_auth Credential PyPIConfig unique_everseen unique_values egg_info_for_url PackageIndex _splituser fix_sf_url local_open distros_for_location distros_for_filename distros_for_url get_supported get_impl_tag get_abbr_impl get_darwin_arches _is_running_32bit is_manylinux1_compatible get_config_var get_flag get_abi_tag get_impl_ver get_impl_version_info get_platform get_all_headers Bytecode_compat hide_setuptools _clear_modules ExceptionSaver save_path DirectorySandbox save_argv UnpickleableException override_temp _needs_hiding run_setup SandboxViolation _execfile save_modules pushd save_pkg_resources_state setup_context AbstractSandbox __boot opener_for match_hostname _certifi_where CertificateError find_ca_bundle get_win_certfile _dnsname_match VerifyingHTTPSConn once VerifyingHTTPSHandler filesys_decode try_encode decompose unpack Wheel windows_only hide_file SetuptoolsDeprecationWarning _install_setup_requires PackageFinder setup Command PEP420PackageFinder _find_all_simple findall format_alias shquote alias strip_module make_zipfile _get_purelib write_safety_flag can_scan sorted_walk analyze_egg bdist_egg iter_symbols write_stub scan_module walk_egg bdist_rpm bdist_wininst build_clib get_abi3_suffix link_shared_object build_ext _customize_compiler_for_shlib build_py assert_relative _unique_everseen develop VersionlessRequirement dist_info is_sh chmod _patch_usage EasyInstallDeprecationWarning get_site_dirs get_win_launcher load_launcher_manifest isascii extract_wininst_cfg WindowsExecutableLauncherWriter RewritePthDistributions easy_install is_python ScriptWriter is_64bit _pythonpath current_umask bootstrap update_dist_caches WindowsCommandSpec auto_chmod _remove_and_clear_zip_directory_cache_data get_exe_prefixes _update_zipimporter_cache nt_quote_arg _to_bytes CommandSpec main _first_line_re is_python_script samefile WindowsScriptWriter expand_paths PthDistributions rmtree _uncache _collect_zipimporter_cache_entries write_pkg_info FileList overwrite_arg _write_requirements egg_info write_setup_requirements get_pkg_info_revision EggInfoDeprecationWarning write_requirements write_entries translate_pattern write_toplevel_names write_arg InfoCommon warn_depends_obsolete write_file manifest_maker install install_egg_info install_lib install_scripts sdist_add_defaults register rotate saveopts sdist walk_revctrl option_base edit_config config_file setopt NonDataProperty test ScanningLoader upload _encode upload_docs VendorImporter RecursiveGrammarException ParseFatalException _defaultSuccessDebugAction matchPreviousExpr MatchFirst oneOf Keyword CloseMatch LineStart withClass ParseSyntaxException locatedExpr srange QuotedString removeQuotes delimitedList countedArray OneOrMore Literal line TokenConverter _makeTags indentedBlock ParseException _escapeRegexRangeChars NotAny CaselessKeyword LineEnd SkipTo _ParseResultsWithOffset _xml_escape StringStart _flatten Suppress makeHTMLTags Word replaceHTMLEntity ParseBaseException White Forward _defaultStartDebugAction originalTextFor infixNotation Token ZeroOrMore Combine OnlyOnce _ForwardNoRecurse replaceWith dictOf lineno pyparsing_common makeXMLTags Regex StringEnd _defaultExceptionDebugAction Empty Dict ParserElement ParseExpression And _trim_arity GoToColumn CharsNotIn Each CaselessLiteral _MultipleMatch ParseResults nestedExpr traceParseAction WordEnd Optional Or withAttribute Group _Constants WordStart _NullToken tokenMap ParseElementEnhance matchPreviousLiteral _PositionToken matchOnlyAtCol FollowedBy NoMatch col ungroup nullDebugAction _SixMetaPathImporter remove_move u Module_six_moves_urllib_parse python_2_unicode_compatible create_unbound_method add_move assertRaisesRegex Module_six_moves_urllib_request print_ iteritems assertCountEqual MovedAttribute assertRegex _LazyDescr _MovedItems get_unbound_function MovedModule Module_six_moves_urllib iterlists wraps with_metaclass Module_six_moves_urllib_error itervalues _add_doc b _LazyModule Module_six_moves_urllib_response add_metaclass iterkeys Module_six_moves_urllib_robotparser reraise _import_module UndefinedEnvironmentName Op Marker _coerce_parse_result _get_env Variable Value UndefinedComparison InvalidMarker default_environment Node _format_marker _eval_op _evaluate_markers format_full_version Requirement InvalidRequirement _version_split BaseSpecifier _IndividualSpecifier Specifier _pad_version _require_version_compare SpecifierSet LegacySpecifier InvalidSpecifier canonicalize_name parse _BaseVersion InvalidVersion LegacyVersion _parse_local_version Version _legacy_cmpkey _parse_letter_version _cmpkey _parse_version_parts with_metaclass Infinity NegativeInfinity main set_version extract_version_string check_versions sample UnityEnv create_mock_group_spec create_mock_vector_step_result setup_mock_unityenvironment step UnityEnv create_mock_group_spec create_mock_vector_step_result setup_mock_unityenvironment step setup_mock_unityenvironment UnityEnv create_mock_group_spec create_mock_vector_step_result sample UnityEnv create_mock_group_spec create_mock_vector_step_result setup_mock_unityenvironment step tuple CONTINUOUS range DISCRETE list array range set_verbosity join isdir print replaceFilenameExtension add_argument exit verbose source_file ArgumentParser target_file sqrt topologicalSort list hasattr layers addEdge Graph print inputs set len list hasattr layers print filter match trim_model compile data layers print tensors float16 replace layers dumps data dtype layers isinstance print name tensors inputs outputs shape zip array_without_brackets to_json globals Build array_equal pool reduce Build tanh mad tanh mul Build concat add sigmoid sub mad _ tanh mul Build concat add sigmoid mad print sorted keys is_action_discrete sum get_agent_step_result resequence_and_append done obs from_observations reward batched_step_result_from_proto vector_actions AgentBuffer append reset_agent vector_observations array visual_observations enumerate make_demo_buffer load_demonstration group_spec_to_brain_parameters join suffix isdir endswith isfile append listdir add_argument_group add_argument ArgumentParser parse_args start_learning target_frame_rate create_sampler_manager EngineConfig trainer_config lesson keep_checkpoints docker_target_name sampler_config load_model multi_gpu TrainerController save_freq width quality_level run_id CSVWriter num_envs create_environment_factory height no_graphics try_create_meta_curriculum add_writer curriculum_config base_port env_args TrainerFactory SubprocessEnvManager time_scale train_model TensorboardWriter env_path pop SamplerManager MetaCurriculum set_all_curricula_to_lesson_num chmod format basename isdir glob copyfile copytree prepare_for_docker_run replace seed getLogger print debug _asdict run_training dumps set_warnings_enabled cpu randint setLevel get_version_string parse_command_line run_cli add_argument ArgumentParser RunOptions experiment_config_path load_config FloatPropertiesChannel get_property_dict_copy get_timer_root reset_timers put _send_response StepResponse list _generate_all_results set_actions set_property action set_configuration EngineConfigurationChannel external_brains payload items EnvironmentResponse reset step endswith len print HasField hasattr get_attr isinstance get_attr tensor_shape ndarray isinstance shape int_val bool_val float_val ListFields name ndarray isinstance str tensor_content ndarray product isinstance get_tensor_dtype print get_tensor_dims unpack int_val bool_val array float_val enter append add set Build mul sub insert Build tolist append range len locate_actual_output_node name find_tensor_by_name split locate_actual_output_node name lstm find_tensor_by_name find_forget_bias split get_layer_shape id Struct tensor get_layer_rank layer_ranks hasattr name patch_data rank input_shapes out_shapes input get_attr append replace_strings_in_list tensors embody astype op inputs zip enumerate print float32 patch_data_fn model_tensors map_ignored_layer_to_its_input co_argcount len items 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 str min_lesson_length format SACTrainer copy warning PPOTrainer get check_config rcls list_local_devices list zeros_like size reversed range append discount_rewards Mock CameraResolution arange ones append array range ones AgentExperience append zeros range len vector_action_space_size pop number_visual_observations to_agentbuffer make_fake_trajectory vector_observation_space_size create_mock_brainparams create_mock_brainparams create_mock_brainparams create_mock_brainparams create_mock_brainparams zeros Mock Mock create_mock_batchedstep ActionInfo publish_trajectory_queue AgentProcessor range create_mock_policy add_experiences AgentManager create_mock_policy Mock get_nowait AgentManagerQueue put join remove _get_candidate_names convert _get_default_tempdir dirname abspath isfile next dirname abspath create_policy_with_bc_mock ppo_dummy_config create_mock_3dball_brain update items list create_policy_with_bc_mock create_mock_3dball_brain update items list create_policy_with_bc_mock current_lr create_mock_3dball_brain update items list create_policy_with_bc_mock create_mock_3dball_brain update items list create_mock_banana_brain create_policy_with_bc_mock update items list create_mock_banana_brain create_policy_with_bc_mock 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 truncate resequence_and_append construct_fake_buffer AgentBuffer Curriculum Curriculum Curriculum dumps StringIO StringIO load_demonstration demo_to_buffer dirname abspath load_demonstration demo_to_buffer dirname abspath MagicMock basic_options MagicMock parse_command_line parse_command_line MetaCurriculum increment_lessons Mock MetaCurriculum assert_called_with increment_lessons assert_not_called MetaCurriculum assert_called_with MetaCurriculum set_all_curricula_to_lesson_num MetaCurriculum loads MetaCurriculum Simple1DEnvironment _check_environment_trains reset_default_graph MultiGpuPPOPolicy create_mock_brainparams reset_default_graph create_mock_brainparams update Mock reset_default_graph MultiGpuPPOPolicy create_mock_brainparams MagicMock TFPolicy AgentGroupSpec basic_mock_brain basic_params get_action empty MagicMock TFPolicy basic_mock_brain BatchedStepResult basic_params get_action array MagicMock TFPolicy basic_mock_brain ActionInfo BatchedStepResult basic_params get_action array list evaluate group_spec_to_brain_parameters agent_id close get_agent_group_spec get_step_result reset MockCommunicator PPOPolicy reset_default_graph UnityEnvironment get_value_estimates items list next_obs to_agentbuffer make_fake_trajectory BrainParameters PPOPolicy reset_default_graph values get_batched_value_estimates 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 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 brain_name subscribe_trajectory_queue put advance make_fake_trajectory BrainParameters AgentManagerQueue add_policy PPOTrainer create_policy brain_name make_fake_trajectory _process_trajectory BrainParameters add_policy zeros PPOTrainer range create_policy run setup_mock_brain update SACPolicy PPOPolicy simulate_rollout evaluate_batch brain model simulate_rollout brain prepare_update _execute_model 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 FakeTrainer dummy_config create_mock_brain list create_rl_trainer end_episode episode_steps values items list construct_fake_buffer create_rl_trainer clear_update_buffer setup_mock_brain SACPolicy update list evaluate brain create_sac_policy_mock simulate_rollout agent_id create_batchedstep_from_brainparams reset_default_graph brain simulate_rollout create_sac_policy_mock update_reward_signals reset_default_graph update list evaluate brain create_sac_policy_mock simulate_rollout agent_id create_batchedstep_from_brainparams reset_default_graph update list evaluate brain create_sac_policy_mock simulate_rollout agent_id create_batchedstep_from_brainparams reset_default_graph update resequence_and_append list evaluate brain create_sac_policy_mock simulate_rollout agent_id AgentBuffer create_batchedstep_from_brainparams reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph str SACTrainer save_model brain simulate_rollout num_experiences setup_mock_brain add_policy brain_name create_policy SACTrainer make_brain_parameters SamplerManager sample_all sampler_config_1 sampler_config_2 SamplerManager SamplerManager sample_all incorrect_uniform_sampler incorrect_sampler_config Simple1DEnvironment _check_environment_trains Simple1DEnvironment _check_environment_trains clear assert_called_once_with Mock get_stats_summaries add_stat add_writer StatsReporter float range write_stats clear Mock write_text add_writer StatsReporter assert_called_once_with TrainerController assert_called_with MagicMock start_learning assert_called_once MagicMock assert_not_called start_learning assert_called_once MagicMock MagicMock assert_called_once MagicMock advance EnvironmentStep add assert_not_called assert_called_once_with assert_called_once MagicMock advance EnvironmentStep add assert_not_called assert_called_once_with BrainParametersMock BrainParametersMock TrainerFactory BrainParameters brain_name generate TrainerFactory BrainParameters _load_config StringIO 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 tuple vector_action_size mean reshape array data compressed_data reshape process_pixels shape array mean nan_to_num isnan warning array warning _process_vector_observation _process_visual_observation ones cast append sum _generate_split_indices discrete_action_branches astype is_action_discrete observation_shapes enumerate dot isnan nan_to_num any split bool array len range len perf_counter push reset method_handlers_generic_handler add_generic_rpc_handlers UnityEnvironment close MockCommunicator obs n_agents close get_agent_group_spec get_step_result reset MockCommunicator zip UnityEnvironment observation_shapes obs zip ones n_agents step close get_agent_group_spec get_step_result MockCommunicator set_actions zeros UnityEnvironment observation_shapes UnityEnvironment close MockCommunicator close RpcCommunicator close RpcCommunicator close RpcCommunicator list extend ObservationProto AgentInfoProto append prod range len fromarray uint8 BytesIO astype save ObservationProto generate_compressed_data extend shape ObservationProto shape tolist extend generate_compressed_data process_pixels rand generate_compressed_data process_pixels rand _process_vector_observation generate_list_agent_proto enumerate generate_compressed_proto_obs rand extend AgentInfoProto _process_visual_observation generate_uncompressed_proto_obs generate_compressed_proto_obs rand AgentInfoProto extend AgentGroupSpec CONTINUOUS batched_step_result_from_proto generate_list_agent_proto range AgentGroupSpec batched_step_result_from_proto DISCRETE generate_list_agent_proto action_mask AgentGroupSpec batched_step_result_from_proto DISCRETE generate_list_agent_proto action_mask AgentGroupSpec batched_step_result_from_proto DISCRETE generate_list_agent_proto action_mask AgentGroupSpec CONTINUOUS batched_step_result_from_proto generate_list_agent_proto action_mask BrainParametersProto agent_group_spec_from_proto extend _parse_side_channel_message _generate_side_channel_data send_int IntChannel FloatPropertiesChannel _parse_side_channel_message _generate_side_channel_data get_property set_property _parse_side_channel_message _generate_side_channel_data RawBytesChannel encode send_raw_data get_and_clear_received_messages set_gauge replace startswith format user_config_dir join expanduser prefix get join list python_version pypy_version_info machine linux_distribution system dict OPENSSL_VERSION filter get_installed_version libc_ver startswith zip release debug get_password get_credential get replace unquote search group lstrip lower raise_for_status match startswith lower url2pathname urlsplit lower unpack _get_used_vcs_backend backends url_without_fragment url_to_path int DownloadProgressProvider resp_read progress_indicator debug url_without_fragment format_size check_against_chunks getattr info consume written_chunks show_url join remove info move backup_dir exit copy warning filename ask_path_exists display_path exists is_dir_url isdir _check_download_dir url_without_fragment _copy_file unpack_file rmtree info check_against_path copytree url_to_path unpack_file_url PipSession unpack_vcs_link unpack_http_url is_file_url is_vcs_url write_delete_marker_file get sanitize_content_filename parse_header get join parse_content_disposition raise_for_status guess_extension filename join filename info check_against_path exists schemes endswith filename get raise_for_status head urlsplit _ensure_html_header redact_password_from_url get _ensure_html_header debug _ensure_html_response _is_url_like_archive raise_for_status debug meth urljoin debug _get_html_response _match_vcs_scheme urlparse check_requires_python requires_python compile join list format digest_count debug append link is_hash_allowed len enumerate _find_name_version_sep get findall parse_header quote unquote path url2pathname urlparse pathname2url get unescape Link urljoin _clean_link join check_requires_python debug map get_requires_python project_name join getcwd prefix running_under_virtualenv update join running_under_virtualenv get_command_obj Distribution dict parse_config_files finalize_options getattr prefix hasattr startswith join get_impl_version_info map get_config_var debug get_config_var get_abbr_impl get_flag replace get_config_var mac_ver replace split OrderedDict _supports_arch append append join range map get_impl_version_info sorted get_extension_suffixes list partition groups add append range format get_darwin_arches is_manylinux1_compatible reversed set get_all_minor_versions_as_strings startswith enumerate int extend is_manylinux2010_compatible match encode join getfilesystemencoding get isfile sha256 rstrip hash_file split isfile match listdir replace parse_map get dict join sorted basename defaultdict format list items executable add dirname append normcase pop list format tuple rehash warning append rstrip move clobber warning sep getvalue append distutils_scheme root_is_purelib get_entrypoints debug set make_multiple listdir pop join make message_about_scripts_not_on_PATH extend ScriptMaker tuple strip map get_metadata split parsestr join warning map VERBOSE compile is_wheel name link splitext info constraint format_command_args format name sorted format warning setlocale autocomplete parse_command install_warning_logger LC_ALL int get_path_completion_type join _short_opts auto_complete_paths print _long_opts exit key create_main_parser lower option_list_all startswith append option_list get_installed_distributions split join isdir abspath normcase split join format error fill split option add_option OptionGroup disallow_binaries map format_control warn any FormatControl any set create debug join extra_index_urls only_binary values no_binary _get_format_control handle_mutual_excludes only_binary values no_binary _get_format_control handle_mutual_excludes FormatControl set FormatControl set tuple split _convert_python_version format raise_option_error add_option python_version implementation platform abi TargetPython strtobool raise_option_error append error split join make_option_group disable_interspersed_args add_option_group get_pip_version ConfigOptionParser general_group get_summaries remove get_similar_commands write linesep exit create_main_parser print_help version append parse_args format info name implementation hasattr info format_given format get_tags info make_target_python len is_wheel_installed build distutils_scheme append str extend append join max get_installer location latest_filetype latest_version append outdated get_installer text_type location latest_filetype latest_version append outdated OrderedDict append get join get_distribution wrap highest_version info max get working_set sorted isinstance strip close DistInfoDistribution feed get_metadata_lines get_metadata splitlines startswith append FeedParser has_metadata strip enumerate _sort_commands items list list lower keys get_close_matches editable compile canonicalize_name version PackageDetails requires project_name get_installed_distributions sorted should_ignore evaluate set dict add canonicalize_name version requires project_name _simulate_installation_of create_package_set_from_installed _create_whitelist set add get_pkg_resources_distribution canonicalize_name version PackageDetails key requires make_distribution_for_install_requirement add requires set join sorted defaultdict iteritems list search set warning from_dist values get_installed_distributions get_src_requirement location debug as_requirement warning abspath normcase get_backend_for_dir project_name match group join format _strip_extras isdir path_to_url lower abspath isfile startswith make_pyproject_path egg_fragment exists Requirement parse_editable startswith url_to_path filename is_wheel is_archive_file Marker _strip_extras Link strip Wheel extras path Requirement is_url warning normpath abspath path_to_url egg_fragment split Requirement get_file_content preprocess process_line ignore_comments join_lines skip_regex splitlines expand_env_variables enumerate pre urljoin require_hashes search break_args_options parse_requirements abspath find_links get_default_values exists extra_index_urls editables dirname append encode parse_args check_install_build_global build_parser index_urls format format_control index_url set_allow_all_prereleases requirements join add_trusted_host extend SearchScope split append pop split add_option option_factory OptionParser append match strip strip sub filterfalse compile findall getenv replace append join dist_in_usersite join reader location endswith get_metadata_lines FakeFile split sorted add set any sep update sorted set dict add any difference_update sep walk join endswith compact map add set dirname normcase walk join info join _win_path_to_bytes getenv normpath expanduser _get_win_folder join getenv normpath expanduser _get_win_folder user_data_dir getenv join expanduser append getenv _get_win_folder normpath HKEY_CURRENT_USER QueryValueEx OpenKey create_unicode_buffer value GetShortPathNameW SHGetFolderPathW getpreferredencoding decode isinstance getattr encode isinstance hasattr O_RDONLY close st_uid O_NOFOLLOW open path hasattr abspath normcase issubclass warning _original_showwarning getLogger simplefilter showwarning join warn decode startswith get_path_uid lexists split gnu_get_libc_version decode CDLL c_char_p match warn glibc_version_string glibc_version_string getattr dictConfig join join dirname abspath makedirs basename chmod st_mode S_IREAD S_IWRITE func decode getfilesystemencoding isinstance decode getdefaultencoding getcwd abspath startswith encode normcase sep getfilesystemencoding str exists split get _check_no_input print lower eval input _check_no_input _check_no_input join isfile read lstrip split_leading_dir realpath expanduser abspath endswith makedirs move removedirs split normalize_path dist_location join path project_name isfile WorkingSet working_set join running_under_virtualenv virtualenv_no_global isfile append project_name egg_link_path umask join infolist dirname filename ensure_dir ZipFile open getmembers has_leading_dir join extractfile chmod isdir endswith name issym current_umask close utime mode warning dirname ensure_dir _extract_member open untar_file url realpath unzip_file unpack critical join format path_to_display format_command_args str_to_display rstrip log_subprocess wait returncode console_to_str warning finish DEBUG Popen append spin update readline format debug close copy info INFO pop format_command_args error make_subprocess_output_error write_delete_marker_file makedirs from_stream setattr getattr WorkingSet parse find deque list dict zip range len urljoin abspath normpath pathname2url rsplit tuple split split_auth_from_netloc urlunsplit netloc urlsplit transform_netloc _transform_url get_distribution str create parse make_search_scope strptime get_best get_installed_version utcnow SelfCheckState SelectionPreferences version save warning join SpecifierSet map parse location feed warning FeedParser display_path has_metadata get str get_metadata get_metadata_lines strip has_metadata append extend file encode getattr stdout stdout write InteractiveSpinner NonInteractiveSpinner hasattr __file__ join dirname abspath to_filename format join getenv normpath expanduser _get_win_folder getenv join site_data_dir user_data_dir getenv join expanduser user_data_dir join expanduser user_cache_dir str GetShortPathName SHGetFolderPath getattr MAX_PATH SHGFP_TYPE_CURRENT rstrip SHGetFolderPath INSTANCE getattr GetShortPathName zeros ShlObj stdout json getLogger addHandler name codename add_argument dumps StreamHandler version ArgumentParser info DEBUG setLevel parse_args isinstance isinstance split next iter _count_righthand_zero_bits min _ip ip _max_prefixlen _compat_bit_length pop get list sorted supernet append values sorted isinstance summarize_address_range extend set append _find_address_range ip isinstance frozenset __reduce__ IPv4Address IPv4Network frozenset __reduce__ IPv6Network replace zip rfind find print _ustr print str asList _ustr print _ustr extract_stack extract_tb getattr __name__ RLock type compile _trim_arity __name__ _ustr addParseAction copy Forward setName setParseAction append extend isinstance setName Forward _ustr addParseAction _ustr addParseAction copy Forward setName replace list isinstance insert masks warn isequal enumerate split locMarker copy endlocMarker ignoreExprs setParseAction setParseAction getattr __name__ Word Keyword ZeroOrMore _L isinstance tagStr name closeTag Combine addParseAction SkipTo Optional Group setName Suppress Dict setParseAction items list isinstance _FB Optional Group Forward expr OneOrMore setName setParseAction enumerate setName Forward setParseAction copy suppress LineEnd setFailAction Optional Group ignore OneOrMore setName suppress streamline addParseAction staticmethod addCondition setName setParseAction tokenMap __import__ _get_module name setattr delattr tp get stdout pop _print flush isinstance encode decode isinstance __str__ locals format __import__ globals groups adapter_class mount addHandler DEBUG setLevel StreamHandler controller CacheControlAdapter DictCache Session mount add_argument ArgumentParser get cache_response get_args get_session request print url cached_request raw setup_logging open remove hasattr O_WRONLY cache_url dirname compile UniversalDetector feed bytearray decode done bytearray close result feed UniversalDetector getfilesystemencoding isatty input description_of compile stdout wrap_stream register stderr init AnsiToWin32 stream should_wrap encode isinstance match compile property __hash__ __hash__ add_distribution matcher get_scheme add_edge DependencyGraph debug key provides run_requires match meta_requires append parse_name_and_version build_requires add_missing dev_requires pop make_graph append append make_graph pop Metadata ServerProxy property S X I compile I compile fget property name format_full_version hasattr version dict parse_marker update items list remove debug append sub replace I compile set isdir staticmethod startswith get __import__ getattr finder_maker type get join finder get_importer type startswith split startswith parse_marker strip lstrip match get_versions append urlparse find pop join rstrip iglob get_rel_path hasattr normcase fsdecode executable input lower isinstance split load items read list ConfigParser get_export_entry sections read_stream StringIO items list add_section ConfigParser name write set prefix flags values mkdtemp getcwd getdefaulttimeout setdefaulttimeout remove curdir split pop getattr __import__ split __hash__ groupdict count search ExportEntry split isdir mkdtemp access warning W_OK expanduser expandvars makedirs splitdrive sep replace abspath rsplit unquote split umask enumerate replace end search escape group match match groupdict remove add set warning startswith get load debug urlopen info urljoin _get_external_data urljoin getmembers decode endswith extractall len namelist check_path abspath getnames ZipFile open BytesIO len search join std_iglob lstrip normpath walk split dict value_converters int isdigit tuple strip groups match append split set str len join strip lower match sub append split replace lower sub startswith _normalized_key pop get_parts append startswith dict compile _operators is_semver groups join int get_suffixes insert sort groups match startswith append range enumerate Wheel read write path hasattr _samefile stat chmod hasattr st_mode stat S_IMODE chmod hasattr st_mode chflags stat st_flags utime S_IMODE join basename isdir copyfile copymode join basename isdir copystat copyfile join isdir readlink copystat set symlink ignore islink listdir copy_function makedirs join remove S_ISDIR st_mode islink rmdir listdir join _samefile isdir exists rename _basename abspath getgrnam getpwnam get add _get_uid dirname _get_gid info open makedirs spawn join isfile _call_external_zip write close dirname normpath info ZipFile walk makedirs sort curdir chdir getcwd debug func abspath sort items list _check_unpack_options dirname makedirs join read write infolist _ensure_directory filename ZipFile open extractall open endswith items list dict getcwd func _find_unpack_format join isfile set finder find _ensure_cfg_read items has_option tuple remove_section set dict sections sub has_section items list keys items _subst_vars _extend_dict normpath expanduser get_config_vars get get_config_var update int list remove str replace isinstance items tuple strip group match keys compile hasattr get_makefile_filename get_config_h_filename _parse_makefile _safe_realpath get_path dirname executable int readline group match compile get_path join _ensure_cfg_read get join int getcwd _getuserbase search group _safe_realpath abiflags sub normpath _init_non_posix append _init_posix get sorted findall tuple group find set lower match uname open compile get_config_vars print items sorted enumerate _get_default_scheme _print_dict print get_python_version get_config_vars get_paths get_platform encode find int range len insert encode range bytearray sum unpack divmod range append property close open HTMLParser getTreeBuilder HTMLParser getTreeBuilder OrderedDict viewkeys get isSurrogatePair ord isinstance start surrogatePairToCodepoint append enumerate getTreeWalker HTMLSerializer append match normaliseCharList append sorted append enumerate append chr escapeRegexp replace compile read hasattr isinstance property decode isinstance ord list frozenset append Attrs endDocument items list startElementNS endPrefixMapping startPrefixMapping endElementNS characters startDocument AttributesNSImpl property tag property InfosetFilter serializeElement serializeElement TreeBuilder lower isinstance lower TreeWalker append append items sorted concatenateCharacterTokens combining chr bidirectional enumerate get range ord len ord decode isinstance ord intranges_contain check_bidi check_initial_combiner check_nfc check_hyphen_ok enumerate str ulabel check_label encode _punycode decode check_label lower startswith encode ord enumerate decode alabel join isinstance append uts46_remap split ulabel isinstance append uts46_remap split sorted _encode_range append range len _decode_range _encode_range bisect_left open fdopen O_CREAT O_EXCL write close getpid O_WRONLY open remove warn memoryview read warn feed _got_extradata Unpacker _unpack Packer write isinstance isinstance get join serialize Specifier get value isinstance _get_env _eval_op append format releaselevel name format_full_version hasattr version VERBOSE IGNORECASE compile VERBOSE IGNORECASE compile search extend groups append split list insert append max takewhile len join epoch format local post dev Version sub append get split pop tuple lower startswith append _parse_version_parts VERBOSE IGNORECASE compile lower tuple list dropwhile reversed pip_install format getattr get_requires info join Pep517HookCaller mkdir_p list out_dir source_dir build check_build_wheel isfile check_build_sdist error Pep517HookCaller warning pjoin info check exit enable_colourful_output ansi setupterm setFormatter addHandler LogFormatter StreamHandler setLevel _load_pyproject Pep517HookCaller _load_pyproject Pep517HookCaller update copy check_call import_module getattr partition split _build_backend _build_backend append match namelist build_wheel join glob join print dirname copy2 _find_already_built_wheel _build_backend _build_backend read_json pjoin write_json hook makedirs update fromkeys items list globals items list globals clear update __setstate__ get_build_platform match _template getattr isinstance readPlist hasattr append exists split _macosx_vers replace get_platform int match group clear f_globals string_types get_provider parse isinstance evaluate_marker Marker namedtuple MemoizedZipManifests _find_adapter get_importer join _is_egg_path endswith zipimporter EggMetadata resource_listdir has_metadata dist_factory join _is_unpacked_egg _normalize_cached _by_version_descending safe_listdir factory endswith lower any map PY2 basename isdir PathMetadata FileMetadata dirname non_empty_lines list map get _rebuild_mod_path _find_adapter ModuleType handler get_importer load_module append _set_parent_ns __path__ __path__ sorted isinstance _handle_ns setdefault __import__ path acquire_lock append __path__ rpartition get _handle_ns acquire_lock join __path__ _normalize_cached pop join setattr split string_types isinstance strip splitlines compile urlparse startswith list partition filter iter next compile globals warn endswith yield_lines strip iter getmro _always_object type getattr dirname makedirs mkdir split endswith strip yield_lines startswith append open f update ResourceManager run_script update iter_entry_points list locals tuple require add_activation_listener map _build_master path add_entry _declare_state subscribe startswith get decode format replace isinstance _p_toml setdefault error object_pairs_hook append process_value _Source expect_re expect_re int expect_re consume_re group _chr append fail consume expect group pos replace _p_ws consume_re expect group last object_pairs_hook consume fail _p_basicstr_content _p_key _p_ews pos expect consume _p_value append _p_ws _p_key expect_eof _p_ews datetime isinstance match int list _TimeZone group map timedelta float utcoffset format microsecond dump StringIO append ord flush any datetime isinstance pop join format _escape_id isinstance write extend reversed append _format_value setdefault setdefault setdefault str format isinstance to_native_string strip warn encode msg MockRequest extract_cookies MockResponse MockRequest add_cookie_header append clear clear hasattr copy set_cookie update bool startswith set timegm strptime time int RequestsCookieJar create_cookie set_cookie update cookiejar_from_dict isinstance CookieJar python_implementation join python_version _implementation OPENSSL_VERSION_NUMBER get hook hasattr update dict_class to_key_val_list upper items list setattr int replace OpenKey HKEY_CURRENT_USER match I split getproxies_environment items list hasattr tell fileno hasattr len decode format exists isinstance expanduser authenticators urlparse getattr extract join gettempdir ZipFile exists split isinstance items list isinstance append _parse_list_header unquote_header_value _parse_list_header unquote_header_value split value compile warn strip find split get _parse_content_type_header decode len headers get_encoding_from_headers warn int chr split range len split inet_aton int inet_aton get get_proxy address_in_network is_valid_cidr hostname is_ipv4_address port urlparse should_bypass_proxies urlparse append strip split count urlparse urlparse isinstance urlparse _body_position body_seek getattr body encode decode isinstance encode append split list format map warn split match_hostname get_host strip normalize_host encode_rfc2231 decode encode isinstance binary_type join sub compile binary_type decode _replace_multiple isinstance urandom decode hexlify PY3 iter iteritems isinstance isinstance data str BytesIO text_type isinstance choose_boundary write render_headers iter_field_objects b get items list pop frozenset tuple copy lower _fields keys object itervalues iterkeys setFormatter getLogger addHandler debug StreamHandler Formatter setLevel simplefilter _validate_dependencies_met X509 decode idna_encode value hasattr to_cryptography _Certificate _x509 extend get socket gettimeout from_address recv_into get socket gettimeout string_at send CFStringEncoding len decode POINTER kCFStringEncodingUTF8 value CFStringGetCStringPtr CFStringGetCString c_void_p cast create_string_buffer SecCopyErrorMessageString _cf_string_to_unicode CFRelease SSLError kCFTypeArrayCallBacks CFArrayCreateMutable replace SecCertificateCreateWithData kCFAllocatorDefault _cf_data_from_bytes byref CFArrayAppendValue CFRelease SecCertificateGetTypeID SecIdentityGetTypeID _assert_no_error decode encode len mkdtemp byref b16encode urandom SecKeychainRef SecKeychainCreate CFDataCreate _assert_no_error CFTypeRef CFArrayRef CFRetain kCFAllocatorDefault _is_cert byref cast CFArrayGetCount _is_identity append CFArrayGetValueAtIndex SecItemImport range len _assert_no_error SecIdentityCreateWithCertificate pop CFArrayCreateMutable kCFTypeArrayCallBacks SecIdentityRef kCFAllocatorDefault extend byref CFArrayAppendValue _load_items_from_file chain CFRelease append BufferedWriter BufferedRWPair BufferedReader DEFAULT_BUFFER_SIZE SocketIO TextIOWrapper normalize decode encode rfind match replace find normalize_percent_characters findall upper replace set pop insert endswith append split bytearray ord extend to_bytes upper to_str findall range len rsplit startswith split int authority_info __hash__ frozenset userinfo split getattr sorted is_valid SUBAUTHORITY_MATCHER is_valid HOST_MATCHER int authority_info add set __hash__ join replace escape split append IGNORECASE compile count str ip_address rstrip get ip_address _to_unicode _dnsname_match append getattr socket settimeout getaddrinfo allowed_gai_family bind strip connect _set_socket_options startswith SOCK_STREAM setsockopt AF_UNSPEC AF_INET has_ipv6 socket bind is_appengine_sandbox close AF_INET6 join isinstance decode tell rewind_body get_payload getattr isinstance _method isinstance frozenset bytearray abs zip len get unhexlify lower encode digest len getattr isinstance getattr isinstance set_ciphers SSLContext load_verify_locations warn load_cert_chain create_urllib3_context load_default_certs decode find encode bytearray ord extend upper to_str findall range len validate groupdict IRIReference _encode_invalid_chars path Validator encode normalize parse_url append partial select _retry_on_intr register poll _retry_on_intr poll _have_working_poll hasattr append max extend function iter_decode set IncrementalEncoder IncrementalDecoder values assert_raises get strip Encoding ascii_lower lookup hasattr _get_encoding _detect_bom startswith _iter_decode_generator next IncrementalDecoder decode iter encode encode join FollowedBy driver join copystat copyfile progress_filter ensure_directory walk open chdir getcwd parse_configuration Distribution parse_config_files dirname abspath command_options append getattr format partial target_obj defaultdict _get_option set_options ConfigMetadataHandler parse ConfigOptionsHandler package_dir metadata pop split load read get_frozen_object load_module find_module compile Bytecode arg index opcode remove append newer_group range len warn StrictVersion getattr _read_field message_from_file StrictVersion _read_list split write_field get_long_description get_obsoletes get_description get_version str list rfc822_escape hasattr long_description_content_type download_url get_contact_email get_url get_keywords _write_list getattr python_requires PY2 get_provides get_name get_contact get_requires get_metadata_version get_platforms provides_extras items join get_classifiers get_license parse rpartition assert_string_list warn items list starmap list parse_requirements partition list parse_requirements isinstance SpecifierSet parse_map items list format assert_string_list warn build_ext gnu_get_libc_version decode CDLL c_char_p next _iglob lexists isdir has_magic glob1 glob2 glob_in_dir encode listdir curdir isinstance join lexists isdir _rlistdir curdir isinstance encode listdir search isinstance isinstance splitdrive sub isinstance read replace exec dict getattr compile next Extension Distribution Command _patch_distribution_metadata findall patch_for_msvc_specialized_compiler DistributionMetadata setattr getattr dist setattr getattr setdefault patch_func import_module partial join get_value isfile get_unpatched format lower get endswith lower startswith urlparse unquote split egg_info_for_url group distros_for_location match interpret_distro_name endswith parse_bdist_wininst Wheel len range split filterfalse seen_add __contains__ add set key strip search map groups set find finditer split compile group encode b64encode unquote decode str _splituser endswith url urlunparse Request opener _encode_auth add_header info find_credential urlparse rpartition join format isdir url2pathname isfile append listdir urlparse StringIO map join get_suffixes exec compile tempdir makedirs chdir getcwd update resume _clear_modules copy list __getstate__ join compile list _clear_modules filter modules dirname abspath _mk_single_path_wrapper hasattr _mk_query _mk_single_with_return _mk_dual_path_wrapper fromkeys lstrip get makepath insert len dict path getattr dirname pathsep load_module append find_module addsitedir split CertFile addstore list filter isfile decode text_type isinstance encode normalize text_type isinstance join list relpath reversed renames rmdir walk enumerate SetFileAttributesW SetFileAttributes __import__ BOOL setup_requires Distribution dict fetch_build_eggs parse_config_files _install_setup_requires __doc__ list partial relpath map _find_all_simple boolean_options user_options endswith sort walk lstrip remove next sorted_walk join list items walk_egg exists join list items close write unlink exists open load join read replace fromkeys close warn iter_symbols PY2 sep open string_types isinstance co_consts co_names warn close sorted_walk visit mkpath dirname info ZipFile customize_compiler copy link SHARED_LIBRARY filterfalse seen_add __contains__ add set key lstrip boolean_options user_options normpath text_type USER_SITE lstrip dict dedent append ENABLE_USER_SITE pathsep split get join list getsitepackages exec_prefix extend map USER_SITE append _pythonpath ENABLE_USER_SITE join list rstrip normalize_path close yield_lines listdir open decode read seek RawConfigParser readfp unpack _EndRecData getfilesystemencoding StringIO open decode read replace endswith insert sort infolist yield_lines reverse PY3 filename append ZipFile split _one_liner isinstance pattern reraise exc_info chmod S_IWRITE _remove_and_clear_zip_directory_cache_data normalize_path path_importer_cache _uncache _replace_zip_directory_cache_data append normalize_path len _collect_zipimporter_cache_entries _update_zipimporter_cache _update_zipimporter_cache _zip_directory_cache compile is_python startswith debug _chmod dict dict lstrip replace is_64bit resource_string PY2 append main dirname lstrip gen_usage escape split sep enumerate len property encode join egg_info distribution write_safety_flag metadata getattr info exists warn list yield_lines writelines map sorted _write_requirements format distribution install_requires write getvalue write_or_delete_file StringIO _write_requirements setup_requires getvalue write_or_delete_file StringIO join sorted write_file fromkeys write_arg distribution join write_or_delete_file getattr join parse_group sorted list items map entry_points append write_or_delete_file values exists warn boolean_options user_options dict __doc__ iter_entry_points tuple items read list RawConfigParser add_section debug remove_section remove_option set info boolean_options user_options boolean_options redact_auth_from_url _determine_base_url parse _create_link_from_element url findall content headers _get_encoding_from_headers join format isdir sort_path realpath warning is_url startswith isfile append listdir exists url_to_path get_url_scheme copy2_fixed dict copytree _copy_source_tree is_existing_dir file_path is_file is_vcs format get_major_minor_version create_command isinstance get_platform is_manylinux2014_compatible join create debug LinkCollector extra_index_urls ensure_binary hexdigest str create parse best_candidate make_link_collector strptime get_installed_version utcnow SelfCheckState SelectionPreferences version save warning get_export_entry PipScriptMaker list create_command write_output join warning import_module getattr command_class is_wheel debug get_password get_credential join listdir sort locate_editable_egg_info join debug unpacked_source_directory ensure_dir make_setuptools_shim_args isolated setup_py_path make_distribution_for_install_requirement parse_editable Requirement Link parse_req_from_editable startswith is_installable_dir warning split isfile filename is_wheel Marker _strip_extras Link strip Wheel _get_url_from_path path Requirement is_url normpath abspath convert_extras path_to_url egg_fragment split parse_req_from_line windll copy2 rename inject_into_urllib3 info format format urlparse build_url_from_netloc quote redact_auth_from_url add format set append extend isinstance reveal_command_args commonprefix abspath get_url_scheme samefile warning dirname append __bases__ __mro__ __context__ _trim_arity _NullToken Tag add split set format hasattr insert get_config_var append format range get_config_var range _py_interpreter_range startswith append extend format tuple map _mac_binary_formats _mac_arch append mac_ver range gnu_get_libc_version decode CDLL c_char_p match warn _glibc_version_string _normalize_string iter append _is_manylinux_compatible get_platform _normalize_string get_platform lower join map get_config_var _cpython_interpreter _pypy_tags _generic_abi _cpython_abis _cpython_tags _linux_platforms _generic_interpreter _pypy_interpreter _independent_tags _interpreter_name _generic_tags _generic_platforms _mac_platforms format join setdefault load_system get validate_system get join writestr BytesIO relpath write ZipFile walk pip_install info get_requires_for_build_wheel partial build_as_zip compat_system Path expanduser update check_output copy join normpath abspath normcase isabs normcase abspath get pathsep PY2 replace ensure_text pop insert endswith append split ensure_str isinstance search _encode_invalid_chars match span binary_type _encode_invalid_chars groups ensure_str int text_type isinstance _normalize_host groups ensure_text lower _remove_path_dot_segments join replace endswith add set walk join print extract_version_string set values print join | <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) ([latest release](https://github.com/Unity-Technologies/ml-agents/releases/tag/latest_release)) ([all releases](https://github.com/Unity-Technologies/ml-agents/releases)) **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, | 2,528 |
jlinear/ReMASC_Exp | ['voice anti spoofing'] | ['ReMASC: Realistic Replay Attack Corpus for Voice Controlled Systems'] | src/vlfeat-0.9.21/docsrc/wikidoc.py src/vlfeat-0.9.21/docsrc/doxytag.py src/vlfeat-0.9.21/docsrc/webdoc.py src/vlfeat-0.9.21/docsrc/formatter.py src/vlfeat-0.9.21/docsrc/mdoc.py Doxytag Terminal Lexer B PL L lex Formatter DL BL E extract towiki depth_first breadCrumb MFile Node runcmd xscan wikidoc usage bullet indent inner_content PL group match DL BL len pid Popen waitpid children group lstrip match startswith append open join addMFile addChildNode print sort MFile Node match listdir __next__ prev runcmd join wikidoc print print insert print readlines close len writelines append range exists open | jlinear/ReMASC_Exp | 2,529 |
jliphard/DeepEvolve | ['stochastic optimization'] | ['Adam: A Method for Stochastic Optimization'] | train.py allgenomes.py genome.py evolver.py brute.py idgen.py main.py AllGenomes generate_genome_list main train_genomes print_genomes Evolver Genome IDgen print_genomes train_genomes generate main get_average_accuracy get_mnist_mlp get_cifar10_mlp compile_model_mlp train_and_score LossHistory get_cifar10_cnn compile_model_cnn get_mnist_cnn update sorted print_genomes close tqdm print_genome train print_genome info append Genome set_genes_to generate_genome_list train_genomes info info evolve sorted print_genomes create_population train_genomes Evolver info range get_average_accuracy print pop range generate reshape to_categorical astype load_data to_categorical astype load_data reshape to_categorical astype load_data reshape to_categorical astype load_data Sequential add Dense nb_neurons info range compile Dropout compile Sequential add Dense MaxPooling2D nb_neurons info Conv2D range Flatten Dropout clear_session get_mnist_mlp info evaluate print get_cifar10_mlp compile_model_mlp LossHistory get_cifar10_cnn compile_model_cnn get_mnist_cnn fit | # DeepEvolve These days, it's relatively easy to *train* neural networks, but it's still difficult to figure out which network architectures and other hyperparameters to use - e.g. how many neurons, how many layers, and which activation functions? In the long term, of course, neural networks will learn how to architect themselves, without human intervention. Until then, the speed of developing application-optimized neural networks will remain limited by the time and expertise required to chose and refine hyperparameters. DeepEvolve is designed to help solve this problem, by rapidly returning good hyperparameters for particular datasets and classification problems. The code supports hyperparameter discovery for MLPs (ie. fully connected networks) and convolutional neural networks. If you had infinite time and infinite computing resources, you could brute-force the problem, and just compare and contrast all parameter combinations. However, in most real-world applications of neural networks, you will probably have to balance competing demands (time, cost, desire to continuously optimize AI performance in dynamic environments) and you may - for whatever reason - have a strong interest to be able to rapidly generate good networks for diverse datasets. In that case, genetic algorithms will be useful. ## Genetic Algorithms Genetic algorithms can be used to solve complex nonlinear optimization problems. DeepEvolve is a simple Keras framework for rapidly discovering good hyperparameters using cycles of mutation, recombination, training, and selection. The role of **point mutations** in genomes is readily apparent - create diversity - but the functions of other genome operations, such as **recombination**, are not as widely appreciated. Briefly, recombination addresses [**clonal interference**](https://en.wikipedia.org/wiki/Clonal_interference), which is a major kinetic bottleneck in discovering optimal genomes in evolving populations. Imagine that two (or more) *different* beneficial mutations in *different* genes arise independently in different individuals. These individuals will have higher fitness, but the algorithm (aka evolution) can not easily converge on the optimal genome. Evolution solves clonal interference through recombination, which allows two genomes to swap entire regions, increasing the likelihood of generating a single genome with *both* beneficial genes. If you are curious about clonal interference, a good place to start is [*The fate of competing beneficial mutations in an asexual population*](https://link.springer.com/article/10.1023%2FA%3A1017067816551). ## History of the codebase DeepEvolve is based on [Matt Harvey's Keras code](https://github.com/harvitronix/neural-network-genetic-algorithm), which in turns draws from [Will Larson, Genetic Algorithms: Cool Name & Damn Simple](https://lethain.com/genetic-algorithms-cool-name-damn-simple/). ## Important aspects of the code Each AI network architecture is represented as a string of genes. These architectures/genomes recombine with some frequency, at one randomly selected position along the genome. Note that a genome with *N* genes can recombine at *N* - 1 nontrivial positions (1, 2, 3, N-1). Specifically, ```recomb_loc = 0 || len(self.all_possible_genes)``` does not lead to recombination, but just returns the original parental genomes, and therefore ```recomb_loc = random.randint(1, len(self.all_possible_genes) - 1)```. | 2,530 |
jmairal/cyanure | ['stochastic optimization'] | ['Cyanure: An Open-Source Toolbox for Empirical Risk Minimization for Python, C++, and soon more'] | setup_cyanure_openblas.py doc/sphinx/source/conf.py test.py setup_cyanure_mkl.py cyanure.py setup_cyanure_mkl_no_openmp.py setup_cyanure_openblas_no_openmp.py setup_cyanure_mkl_windows.py MultiClassifier SKLearnClassifier LinearSVC MultiVariateRegression LogisticRegression preprocess BinaryClassifier Lasso Regression ERM L1Logistic issparse asfortranarray T | # Cyanure Cyanure: An Open-Source Toolbox for Empirical Risk Minimization for Python, C++, and soon more. The library should be fully compatible with scikit-learn. Cyanure is distributed under BSD-3-Clause license. Go to the [project website](https://inria-thoth.github.io/cyanure/welcome.html) for more information including information concerning the API. Installation ============ The recommanded installation procedure is to use either conda or pip. The package available on pip are shipped with OpenBLAS and OpenMP except for Windows which does not use OpenMP. | 2,531 |
jmfacil/single-view-place-recognition | ['autonomous navigation'] | ['Single-View Place Recognition under Seasonal Changes'] | test_nordland.py numberOfCorrectMatches extractFeatures closestFeatures main loadImage int arange ones DataFrame sort sort_values zeros sum values print reshape forward_all copy append zeros array loadImage str imread range model print zeros add_argument len numberOfCorrectMatches range extractFeatures set_mode_gpu Net params closestFeatures ArgumentParser parse_args input_season TEST reference_season | # Single-View-Place-Recognition Single-View Place Recognition under Seasonal Changes [[arXiv]](https://arxiv.org/pdf/1808.06516.pdf) Authors: Daniel Olid, [Jose M. Facil](http://webdiis.unizar.es/~jmfacil) and [Javier Civera](http://webdiis.unizar.es/~jcivera) Project Website: [[project web]](http://webdiis.unizar.es/~jmfacil/pr-nordland/) Partitioned Nordland Dataset: [[download]](http://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) This work was continued with a Multi-View version of our method, please find our latest work at [Condition-Invariant Multi-View Place Recognition ](http://webdiis.unizar.es/~jmfacil/cimvpr/) ## How to use: We recommend the use of a virtual enviroment for the use of this project. ([pew](https://github.com/berdario/pew)) ```bash | 2,532 |
jmlipman/RatLesNetv2 | ['medical image segmentation', 'lesion segmentation', 'semantic segmentation'] | ['RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation'] | train.py lib/RatLesNetv2.py lib/utils.py eval.py lib/losses.py lib/DataWrapper.py lib/RatLesNetv2Blocks.py lib/metric.py weight_init DataWrapper CrossEntropyDiceLoss CrossEntropyLoss DiceLoss Metric _border_np _border_distance RatLesNetv2 ResNet Bottleneck now np2cuda he_normal removeSmallIslands initB isinstance Conv3d bias initW weight sum log pow sum distance_transform_edt _border_np _calculate_fan_in_and_fan_out stack unique label argmax range len | RatLesNetv2 ====================== Repository of the paper [RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation](https://www.frontiersin.org/articles/10.3389/fnins.2020.610239/full).  ### Table of Contents * [1. Introduction](#1-introduction) * [1.1. Files](#11-files) * [2. Installation and Requirements](#2-installation-and-requirements) * [2.1. Requirements](#21-requirements) * [2.2. Installation](#22-installation) | 2,533 |
joaanna/something_else | ['action recognition'] | ['Something-Else: Compositional Action Recognition with Spatial-Temporal Interaction Networks'] | code/model/resnet3d_xl.py code/callbacks.py code/utils.py code/model/model.py code/model/nonlocal_helper.py code/model/model_lib.py code/data_utils/data_parser.py code/data_utils/data_loader_frames.py code/train.py code/data_utils/gtransforms.py annotate_videos.py annotate_video get_colormap annotate_frame Logger AverageMeter PlotLearning Progbar validate accuracy save_checkpoint adjust_learning_rate main train save_results load_args save_images_for_debug load_json_config setup_cuda_devices accuracy save_checkpoint config_init remove_module_from_checkpoint_state_dict VideoFolder WebmDataset DatasetBase GroupResize GroupCenterCrop GroupNormalize ToTensor LoopPad GroupRandomHorizontalFlip GroupRandomCrop GroupMultiScaleCrop TrainingScheduleError VideoModel VideoModelCoord VideoModelCoordLatentNL VideoModelCoordLatent VideoModelGlobalCoordLatentNL VideoModelGlobalCoordLatent VideoModelGlobalCoord VideoGlobalModel Nonlocal conv3x3x3 obtain_arc get_fine_tuning_parameters ResNet downsample_basic_block resnet50 Bottleneck resnet152 Net resnet34 resnet200 resnet18 resnet10 BasicBlock resnet101 FONT_HERSHEY_DUPLEX join fromarray vid_path out_vid_path tuple putText rectangle annotate_objects save array open list unique from_iterable join get_colormap out_vid_path annotate_frame makedirs validate ckpt SGD DataLoader adjust_learning_rate save_checkpoint Logger cuda lr_steps load_state_dict parse_args logdir CrossEntropyLoss range format logname VideoFolder start_epoch lower resume load join evaluate print VideoModel min parameters train epochs makedirs model clip_grad_norm_ zero_grad cuda list view clip_gradient OrderedDict update val format size item flush enumerate items time criterion backward print AverageMeter log_scalar accuracy parameters cpu step len save_results list OrderedDict update format concatenate size eval avg item flush enumerate items time evaluate print AverageMeter log_scalar len copyfile save param_groups sum lr add_argument exit print_help ArgumentParser parse_args OrderedDict items list device join format print print join logname ckpt join format print astype shape permute numpy imsave enumerate makedirs topk size t eq mul_ expand_as append sum max data isinstance FloatTensor Variable zero_ avg_pool3d cuda cat append format range named_parameters append range ResNet ResNet ResNet ResNet obtain_arc ResNet obtain_arc ResNet ResNet load items list format print named_parameters load_state_dict | # The Something-Else Annotations This repository provides instructions regarding the annotations used in the paper: 'Something-Else: Compositional Action Recognition with Spatial-Temporal Interaction Networks' (https://arxiv.org/abs/1912.09930), project website available at https://joaanna.github.io/something_else/. We collected annotations for 180049 videos from the Something-Something Dataset (https://20bn.com/datasets/something-something), that include per frame bounding box annotation for each object and hand in the human-object interaction in the video. The file containing annotations can be downloaded from: https://drive.google.com/open?id=1XqZC2jIHqrLPugPOVJxCH_YWa275PBrZ in four parts, it containes a dictionary mapping each video id, the name of the video file to the list of per-frame annotations. The annotations assume that the frame rate of the videos is 12. An example of per-frame annotation is shown below, the names and number of "something's" in the frame correspond to the fields 'gt_placeholders' and 'nr_instances', the frame path is given in the field 'name', 'labels' is a list of object's and hand's bounding boxes and names. ``` [ | 2,534 |
joansj/genmotif | ['time series'] | ['A genetic algorithm to discover flexible motifs with support'] | bin/run_multiple_times.py bin/run.py build.py | # GENMOTIF This is the source code for the paper: J. Serra, A. Matic, J.L. Arcos, & A. Karatzoglou, "A genetic algorithm to discover flexible motifs with support", arXiv:1511.04986, 2015. Abstract: Finding repeated patterns or motifs in a time series is an important unsupervised task that has still a number of open issues, starting by the definition of motif. In this paper, we revise the notion of motif support, characterizing it as the number of patterns or repetitions that define a motif. We then propose GENMOTIF, a genetic algorithm to discover motifs with support which, at the same time, is flexible enough to accommodate other motif specifications and task characteristics. GENMOTIF is an anytime algorithm that easily adapts to many situations: searching in a range of segment lengths, applying uniform scaling, dealing with multiple dimensions, using different similarity and grouping criteria, etc. GENMOTIF is also parameter-friendly: it has only two intuitive parameters which, if set within reasonable bounds, do not substantially affect its performance. We demonstrate the value of our approach in a number of synthetic and real-world settings, considering traffic volume measurements, accelerometer signals, and telephone call records. http://arxiv.org/abs/1511.04986 To compile, you'll need to run "python build.py folder" and have gcc installed. There is also a python wrapper for running the compiled binary in the bin/ folder. If using this code, parts of it, or developments from it, please cite the above reference. We do not provide any support or assistance for the supplied code nor we offer any other compilation/variant of it. In addition, we assume no responsibility regarding the provided code. | 2,535 |
joe-siyuan-qiao/ViP-DeepLab | ['depth estimation', 'monocular depth estimation', 'panoptic segmentation'] | ['ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation'] | semkitti-dvps/eval_dvpq.py cityscapes-dvps/copy_video_sequence.py semkitti-dvps/copy_images.py cityscapes-dvps/copy_gt.py cityscapes-dvps/copy_image.py semkitti-dvps/eval_dstq.py cityscapes-dvps/eval_dvpq.py copy_image main vpq_eval eval load_sequence process DSTQuality STQuality _update_dict_stats scan_sequence main main vpq_eval eval join basename replace copyfile makedirs iteritems _ids_to_counts concatenate prediction_void_overlap astype iterkeys set add zeros uint32 sum concatenate mean zip append vpq_eval abs range dict sorted append format print cpu_count mean scandir load_sequence append sum join scandir format copyfile numpy unique_with_counts zip sorted update_state replace resize_bilinear astype float32 shape scandir cast int32 zip numpy array open join list DSTQuality int result scan_sequence float range int int64 astype int32 sorted len | # ViP-DeepLab ## Introduction In this repository, we present the datasets and the toolkits of [ViP-DeepLab](https://arxiv.org/abs/2012.05258). ViP-DeepLab is a unified model attempting to tackle the long-standing and challenging inverse projection problem in vision, which we model as restoring the point clouds from perspective image sequences while providing each point with instance-level semantic interpretations. Solving this problem requires the vision models to predict the spatial location, semantic class, and temporally consistent instance label for each 3D point. ViP-DeepLab approaches it by jointly performing monocular depth estimation and video panoptic segmentation. We name this joint task as Depth-aware Video Panoptic Segmentation (DVPS), and propose a new evaluation metric along with two derived datasets for it. This repository includes the datasets SemKITTI-DVPS and Cityscapes-DVPS along with the evaluation toolkits. [](https://youtu.be/XR4HFiwwao0) | 2,536 |
joehalfish/-An-End-to-End-Neural-Network-for-Image-Cropping | ['image cropping'] | ['An End-to-End Neural Network for Image Cropping by Learning Composition from Aesthetic Photos'] | demo.py models/config.py models/RoiPoolingConv.py models/model.py utils.py main get_shape runn normalization recover_from_normalization recover_from_normalization_with_order add_offset Config EndToEndModel RoiPoolingConv_Boundary RoiPoolingConv save saliency_box_color log aesthetics_box_color open crop_out_path get_shape list copyMakeBorder log_path normalization append expand_dims predict asarray size astype copy mkdir scale ratio listdir crop join box_out_path isdir Draw convert draw rectangle recover_from_normalization_with_order array add_offset makedirs runn model image_path load_weights BuildModel int float min max | # -An-End-to-End-Neural-Network-for-Image-Cropping 文章：An-End-to-End-Neural-Network-for-Image-Cropping pdf链接：https://arxiv.org/abs/1907.01432?context=cs.CV 本文核心在于两部分： （1）从给定的图中找出注意力区域（蓝色框） （2）然后从锚定区域中获得具有最高美学得分的美学区域（黄色框） 修改参数在config.py文件中，可修改的参数： （1）scale： 224， 384， 512三种规模 （2）ratio：square or not 修改后对应的权重参数存在于文件weight中 | 2,537 |
joelmoniz/BLISS | ['word embeddings', 'bilingual lexicon induction'] | ['Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces'] | bliss/main.py bliss/data/data.py bliss/data/noise.py bliss/models/models.py bliss/utils.py bliss/data/__init__.py bliss/models/__init__.py bliss/eval.py CSLS _csls_test get_mean_similarity get_faiss_nearest_neighbours normalize get_arguments make_directory to_cuda print_metrics read_from_yaml setup_logger setup_output_dir to_numpy disp_params write_to_yaml MonoDictionary Batcher Language CrossLingualDictionary WordDictionary GaussianAdditive MonitorLR Generator GAN Evaluator Discriminator Hubness GpuIndexFlatIP GpuIndexFlatConfig astype add StandardGpuResources IndexFlatIP get_faiss_nearest_neighbours load CSLS print get_closest_csls_matches Language embeddings add_argument upper convert_to_boolean ArgumentParser parse_args print setFormatter basicConfig getLogger addHandler Formatter getattr FileHandler make_directory int join communicate strip setup_logger startswith split listdir max Popen write_to_yaml makedirs format info getLogger | # Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces Code for the paper Patra, Barun, et al. "Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces." Proceedings of the 57th Conference of the Association for Computational Linguistics. 2019. Run the code in this repository as: `python -m bliss.main --config_file ./configs/config.yaml --gpu True` ## References If you found the resources in this repository useful, please cite [Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces](https://aclweb.org/anthology/papers/P/P19/P19-1018/): @inproceedings{bliss, title={Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces}, author={Patra, Barun and Moniz, Joel Ruben Antony and Garg, Sarthak and Gormley, Matthew R and Neubig, Graham}, | 2,538 |
johanna-rock/imRICnn | ['denoising'] | ['Complex Signal Denoising and Interference Mitigation for Automotive Radar Using Convolutional Neural Networks'] | utils/rd_processing.py utils/distribution.py training/trainer.py run_scripts/run_training.py training/rd_evaluation.py models/ri_cnn_rp.py data_models/objective_func.py utils/loading.py run_scripts/run_evaluation.py utils/mem_usage.py training/sample_hyperparameters.py data_models/scaler.py training/early_stopping.py run_scripts/__init__.py training/evaluation_commons.py data_models/parameter_configuration.py datasets/radar_dataset.py utils/printing.py training/rd_log_mag_evaluation.py datasets/partition_random_sampler.py utils/plotting.py models/ri_cnn_rd.py data_models/evaluation_result.py PartitionRandomSampler DatasetPartition load_data_for_denoising_ri_angle_map load_data_for_denoising_ri_ramps DataContent calculate_rd_object_and_noise_masks load_data_for_denoising_ri_ramps_training_with_interfered_ramps_only calculate_cr_object_and_noise_masks split_indices_for_partitions calculate_aoa_object_and_noise_masks load_data_for_denoising_log_mag_range_doppler_map calculate_object_and_noise_masks load_data_for_denoising_ri_range_doppler_map DataSource complex_to_format RadarDataset EvaluationResult MSEWeightedMagPhase sinr_log_mag sinr rd_obj_peak_phase_mse rd_obj_peak_log_mag_mse sinr_1d evm_1d_norm ObjectiveFunction DeltaSNR evm_norm evm_1d evm MSE peak_mag_mse sinr_from_re_im_format SINRLoss count_parameters ParameterConfiguration ZeroMeanUnitVarianceScaler Scaler ComplexFeatureScaler MAG_CNN_RD RICNN_RD RICNN_RP PretrainedModels run_evaluation run_signal_denoising_cnn_re_im print_ EarlyStopping EvaluationFunction Signal evaluate_rd evaluate_rd_log_mag select_and_sample_hyperparameter_config_for_cnn train_with_hyperparameter_config construct_formatted_values_headline construct_formatted_values_line loguniform data_loader_for_dataset print_torch_mem_usage plot_angle_of_arrival_map plot_phase_amplitude_for_packet plot_object_mag_cuts plot_target_and_prediction plot_aoa_noise_mask plot_phase_amplitude_for_fts plot_values plot_data plot_interfered_original_clean_data plot_target_range_doppler_matrix_with_and_out_interference plot_losses plot_line_from_tuples plot_metrics_comparison plot_cross_ranges save_or_show_plot plot_classification_targets_and_predictions plot_rd_noise_mask plot_rd_map plot_rd_matrix_for_packet plot_distance_map plot_data_targets_predictions phase_by_rd plot_input_data plot_object_phase_cuts plot_row_column_cuts plot_stat_from_tuples plot_phase print_evaluation_summary PrintColors calculate_angle_fft hanning_window_fft3 v_vec_fft2 hanning_window_fft2 basis_vec_fft3 calculate_velocity_fft d_vec_fft2 hanning_window_fft1 basis_vec_cross_range calculate_range_fft calculate_cross_range_fft int list range int ones reshape transpose flatten shape calculate_aoa_object_and_noise_masks num_ramps_per_packet append calculate_rd_object_and_noise_masks array range int ones reshape transpose flatten shape calculate_aoa_object_and_noise_masks num_ramps_per_packet append calculate_rd_object_and_noise_masks array range int ones transpose calculate_velocity_fft flatten shape calculate_aoa_object_and_noise_masks num_ramps_per_packet append calculate_rd_object_and_noise_masks array range int ones transpose calculate_velocity_fft flatten shape log10 calculate_aoa_object_and_noise_masks num_ramps_per_packet append calculate_rd_object_and_noise_masks abs array range amax calculate_angle_fft int get_num_channels calculate_rd_object_and_noise_masks ones transpose flatten shape calculate_aoa_object_and_noise_masks num_ramps_per_packet append zeros array range shape concatenate len v_vec_fft2 d_vec_fft2 calculate_object_and_noise_masks arcsin linspace ones argmin min append zeros abs max range len ones abs argmin min linspace zeros arcsin max range len MSEWeightedMagPhase DeltaSNR MSE SINRLoss average abs average abs mean int masked_select abs abs amax abs print_ abs amax arctan astype real print_ mean_squared_error imag print_ mean_squared_error abs partial COMPLEX_FEATURE_SCALER TEST evaluate_rd MODEL_D DENOISE_REAL_IMAG_RD warn clone_for_new_active_partition to RadarDataset format DENOISE_REAL_IMAG_RD STD_SCALER select_and_sample_hyperparameter_config_for_cnn MSE device save to train_with_hyperparameter_config str write flush get_scene_rd_zero_substitude_in_time_domain get_scene_rd_interf get_num_channels num_samples_for_rd_evaluation num_ramps_per_packet print_ plot_object_mag_cuts get_scene_rd_original count_nonzero get_scene_cr_object_and_noise_masks print_evaluation_summary calculate_velocity_fft packet_in_target_format_to_complex get_target_original_scaled_re_im shape inverse_scale plot_values append to data_loader_for_dataset basis_vec_cross_range range calculate_cross_range_fft format get_scene_rd_object_and_noise_masks get_scene_rd_clean mean eval func nonzero plot_cross_ranges label enumerate int data_content time plot_rd_noise_mask plot_rd_matrix_for_packet extend plot_object_phase_cuts isnan zeros numpy array len get_scene_rd_zero_substitude_in_time_domain get_scene_rd_interf get_num_channels num_samples_for_rd_evaluation num_ramps_per_packet print_ plot_object_mag_cuts get_scene_rd_original count_nonzero print_evaluation_summary calculate_velocity_fft packet_in_target_format_to_complex get_target_original_scaled_re_im shape inverse_scale plot_values append to data_loader_for_dataset range format get_scene_rd_object_and_noise_masks get_scene_rd_clean mean eval func label enumerate int data_content time plot_rd_noise_mask plot_rd_matrix_for_packet extend isnan zeros numpy array len int num_values_per_sample mat_path pow ParameterConfiguration input_data_source randint RadarDataset max_batch_size scaler batch_size output_size model warn construct_formatted_values_line VALIDATION numpy print_torch_mem_usage print_ next_epoch dataset max loss_from_running_loss set_tensorboardx_logging_active load_state_dict scheduler_partial append to data_loader_for_dataset range construct_formatted_values_headline detach state_dict TEST format optimization_algo param_groups add_histogram init_hidden_state close mean plot_losses eval clone_for_new_active_partition item evaluate_rd_log_mag float num_epochs flush time learning_rate num_model_initializations criterion evaluate_rd EarlyStopping add_graph write named_parameters EvaluationResult reset train step add_scalar ENDC format GREEN PartitionRandomSampler DataLoader get_sample_start_and_end_indices_per_file format print size get_objects flush str close savefig tikz_save str format plot add_subplot title save_or_show_plot figure legend ticklabel_format plot title figure legend save_or_show_plot format arange plot title scatter figure legend save_or_show_plot range len format set_title plot suptitle reshape add_subplot save_or_show_plot figure real legend ticklabel_format imag format set_title plot suptitle reshape add_subplot save_or_show_plot figure real legend ticklabel_format imag format set_title plot suptitle reshape add_subplot save_or_show_plot figure real legend ticklabel_format imag format set_title plot suptitle reshape add_subplot save_or_show_plot figure legend ticklabel_format format plot suptitle add_subplot figure legend save_or_show_plot enumerate set_xscale subplots plot save_or_show_plot format suptitle plot sort add_subplot figure legend save_or_show_plot enumerate list subplots set save_or_show_plot boxplot append xticks range len format plot_rd_map plot_rd_map set_aspect set_cmap subplots v_vec_fft2 suptitle xlabel ylabel colorbar imshow log10 ticklabel_format save_or_show_plot abs amax v_vec_fft2 add_subplot abs set_aspect set_title set_xlabel imshow log10 ticklabel_format real format astype save_or_show_plot suptitle arctan set_ylabel figure imag amax min log10 plot_row_column_cuts nonzero abs range amax len phase_by_rd min plot_row_column_cuts nonzero range len astype imag real arctan format set_title v_vec_fft2 suptitle plot xlabel axvline basis_vec_fft3 add_subplot ylabel save_or_show_plot linspace figure ticklabel_format legend array range len plot_phase_amplitude_for_fts transpose min nonzero range len format subplots set_title suptitle arctan plot xlabel add_subplot save_or_show_plot log10 ticklabel_format real legend abs imag set_aspect set_cmap subplots suptitle xlabel ylabel colorbar imshow ticklabel_format save_or_show_plot abs set_aspect set_cmap subplots suptitle v_vec_fft2 xlabel astype ylabel colorbar imshow ticklabel_format save_or_show_plot suptitle xlabel view_init draw basis_vec_fft3 astype ylabel colorbar figure save_or_show_plot gca plot_surface suptitle xlabel view_init draw basis_vec_fft3 ylabel colorbar log10 figure save_or_show_plot gca plot_surface abs amax format suptitle plot xlabel len add_subplot axvline ylabel save_or_show_plot log10 figure nonzero legend abs range amax calculate_cross_range_fft print_ format range len hanning_window_fft1 sqrt fft fftshift transpose hanning_window_fft2 sqrt fft fftshift hanning_window_fft3 fft fftshift hanning_window_fft3 len repmat cos arange pi repmat cos arange pi repmat cos arange pi array linspace transpose cos pi linspace sin zeros arcsin range len arcsin cos linspace pi | johanna-rock/imRICnn | 2,539 |
johanofverstedt/sdt | ['template matching'] | ['Stochastic Distance Transform'] | sdt.py test_empty_set_2d _grid_diameter _knn_query det_sdt_multiset_naive test_singleton_set_2d test_singleton_set_3d test_empty_set_3d sum_pool point_set_to_multiset compute_k _array_grid main det_sdt or_pool int _grid_diameter ones reshape size min astype cKDTree shape point_set_to_multiset compute_k _array_grid full ceil power float array range int convolve ones tuple astype ndim isclose _grid_diameter ones reshape transpose sort astype square size power shape point_set_to_multiset sqrt nonzero _array_grid zeros sum array range ones range ndim ones len query dot clip zeros _grid_diameter det_sdt shape _grid_diameter print shape zeros det_sdt zeros _grid_diameter det_sdt shape _grid_diameter print shape zeros det_sdt seed test_empty_set_2d time print tuple test_singleton_set_2d distance_transform_edt sum_pool compute_k float det_sdt full len | # Project: sdt Python/NumPy/SciPy implementation of the deterministic version of the Stochastic Distance Transform (SDT). (Ref: https://arxiv.org/abs/1810.08097) The SDT is a tunably noise-insensitive distance transform (a distance map from all elements of an image domain to its nearest object element). Both binary and integer-valued images are supported, where the integer-valued images act as multisets. # License The SDT implementation is licensed under the permissive MIT license. # Author/Copyright Written by (and copyright reserved for) Johan Ofverstedt. | 2,540 |
johnny12150/NISER | ['session based recommendations'] | ['NISER: Normalized Item and Session Representations to Handle Popularity Bias'] | utils.py main.py model.py main GNN trans_to_cuda train_test trans_to_cpu SessionGraph forward data_masks build_graph split_validation Data load validation Data trans_to_cuda time epoch print train_test valid_portion SessionGraph split_validation dataset range open is_available is_available trans_to_cuda norm get_slice list view model size div stack expand_as float long hidden_size arange batch_size zero_grad forward list step mask append isin generate_batch mean eval zip long trans_to_cuda backward print tqdm loss_function train numpy len add_edge in_edges DiGraph nodes range len max int arange shuffle round len | johnny12150/NISER | 2,541 |
johnsonkee/graduate_design | ['adversarial defense', 'adversarial attack'] | ['Technical Report on the CleverHans v2.1.0 Adversarial Examples Library', 'The Limitations of Deep Learning in Adversarial Settings'] | cleverhans/experimental/certification/read_weights.py examples/nips17_adversarial_competition/eval_infra/code/eval_lib/tests/submissions_test.py cleverhans_tutorials/cifar10_tutorial_mifgsm.py cleverhans_tutorials/mnist_tutorial_keras_tf.py cleverhans/plot/image.py examples/multigpu_advtrain/utils_svhn.py image/tflib/ops/batchnorm.py examples/madry_lab_challenges/cifar10/attack_model.py cleverhans_tutorials/mnist_keras_at.py scripts/plot_success_fail_curve.py examples/nips17_adversarial_competition/dev_toolkit/sample_defenses/adv_inception_v3/defense.py cleverhans/plot/pyplot_image.py cleverhans_tutorials/mnist_tutorial_tfe.py cleverhans/initializers.py scripts/make_confidence_report_bundle_examples.py cleverhans_tutorials/test_blackacc.py examples/imagenet_featadvs/model.py tests_tf/test_evaluation.py tests_tf/test_serial.py tests_tf/test_mnist_tutorial_keras_tf.py image/tflib/ops/cond_batchnorm.py examples/nips17_adversarial_competition/eval_infra/code/eval_lib/tests/classification_results_test.py cleverhans/dataset.py cleverhans/devtools/autopep8_all.py cleverhans/experimental/certification/dual_formulation.py examples/multigpu_advtrain/model.py examples/nips17_adversarial_competition/eval_infra/code/worker.py examples/nips17_adversarial_competition/dataset/download_images.py setup.py image/tflib/ops/conv1d.py examples/imagenet_featadvs/attack_model_featadv.py examples/nips17_adversarial_competition/eval_infra/code/eval_lib/tests/image_batches_test.py cleverhans/experimental/certification/certify.py cleverhans/devtools/list_files.py examples/madry_lab_challenges/mnist/attack_model.py image/tflib/ops/layernorm.py examples/nips17_adversarial_competition/dev_toolkit/sample_targeted_attacks/iter_target_class/attack_iter_target_class.py examples/nips17_adversarial_competition/dev_toolkit/sample_defenses/base_inception_model/defense.py examples/multigpu_advtrain/runner.py cleverhans/__init__.py examples/facenet_adversarial_faces/set_loader.py advgan/utils/test_generator_cgan.py examples/nips17_adversarial_competition/eval_infra/code/eval_lib/tests/fake_cloud_client.py examples/test_imagenet_attacks.py examples/multigpu_advtrain/resnet_tf.py cleverhans/plot/save_pdf.py cleverhans_tutorials/cifar10_tutorial_bim.py cleverhans_tutorials/mnist_new.py image/mytest.py scripts/compute_accuracy.py tests_pytorch/test_mnist_tutorial_pytorch.py examples/nips17_adversarial_competition/eval_infra/code/eval_lib/tests/fake_cloud_client_test.py docs/conf.py image/tflib/ops/linear.py examples/nips17_adversarial_competition/dev_toolkit/run_attacks_and_defenses.py cleverhans/confidence_report.py cleverhans_tutorials/tutorial_models_tfe.py cleverhans/attacks_tf.py examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_and_copy_submissions.py tests_tf/test_utils_keras.py examples/nips17_adversarial_competition/eval_infra/code/eval_lib/tests/work_data_test.py image/search.py cleverhans/utils.py cleverhans/attacks/__init__.py examples/multigpu_advtrain/evaluator.py image/tflib/save_images.py examples/nips17_adversarial_competition/dev_toolkit/validation_tool/validate_submission.py examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_submission_lib.py cleverhans_tutorials/fashion_tutorial_keras_fgsm.py cleverhans/canary.py cleverhans/attack_bundling.py examples/nips17_adversarial_competition/dev_toolkit/sample_targeted_attacks/step_target_class/attack_step_target_class.py cleverhans/utils_mnist.py cleverhans/picklable_model.py examples/nips17_adversarial_competition/eval_infra/code/eval_lib/dataset_helper.py cleverhans_tutorials/mnist_tutorial_keras_lbfgs.py advgan/Cadvgan.py cleverhans/experimental/certification/tests/optimization_test.py image/mnist_wgan_inv.py image/tflib/__init__.py tests_tf/test_confidence_report.py tests_tf/test_mnist_tutorial_tf.py tests_tf/test_mnist_tutorial_cw.py cleverhans_tutorials/cifar10_tutorial_cw.py examples/madry_lab_challenges/mnist/madry_mnist_model.py examples/multigpu_advtrain/trainer.py examples/robust_vision_benchmark/cleverhans_attack_example/utils.py tests_tf/test_utils_tf.py cleverhans/evaluation.py image/mnist_natural_adversary.py tests_tf/test_attacks.py cleverhans/devtools/checks.py examples/RL-attack/model.py cleverhans/devtools/tests/test_format.py cleverhans_tutorials/utils/generate_image.py examples/nips17_adversarial_competition/dev_toolkit/sample_attacks/fgsm/attack_fgsm.py image/tflib/ops/deconv2d.py scripts/make_confidence_report.py tests_tf/test_attack_bundling.py image/tflib/ops/conv2d.py cleverhans/devtools/version.py cleverhans/model_zoo/madry_lab_challenges/cifar10_model.py tests_tf/test_dataset.py cleverhans/experimental/certification/utils.py examples/RL-attack/enjoy-adv.py cleverhans_tutorials/mnist_tutorial_picklable.py examples/nips17_adversarial_competition/eval_infra/code/eval_lib/image_batches.py image/tflib/mnist.py cleverhans_tutorials/mnist_tutorial_tf.py examples/multigpu_advtrain/attacks_multigpu.py cleverhans/model_zoo/madry_lab_challenges/__init__.py examples/multigpu_advtrain/test_run_multigpu.py tests_tf/test_picklable_model.py cleverhans/compat.py cleverhans/augmentation.py cleverhans_tutorials/mymodel.py cleverhans/utils_keras.py scripts/make_confidence_report_bundled.py tests_tf/test_projected_gradient_descent.py examples/multigpu_advtrain/run_multigpu.py examples/nips17_adversarial_competition/eval_infra/code/eval_lib/submissions.py cleverhans/experimental/certification/optimization.py scripts/show_images.py cleverhans/model_zoo/madry_lab_challenges/make_cifar10_joblib.py tests_tf/test_mnist_blackbox.py cleverhans/model_zoo/__init__.py examples/multigpu_advtrain/make_model.py cleverhans/plot/success_fail.py tests_tf/test_utils.py tests_tf/test_attacks_tf.py examples/nips17_adversarial_competition/eval_infra/code/master.py cleverhans_tutorials/evaluate_pickled_model.py cleverhans/loss.py examples/nips17_adversarial_competition/dev_toolkit/sample_attacks/noop/attack_noop.py advgan/advgan.py examples/nips17_adversarial_competition/dev_toolkit/validation_tool/submission_validator_lib.py examples/multigpu_advtrain/utils_cifar.py cleverhans_tutorials/mnist_blackbox.py examples/nips17_adversarial_competition/eval_infra/code/eval_lib/__init__.py examples/nips17_adversarial_competition/eval_infra/code/eval_lib/cloud_client.py tests_tf/test_defenses.py examples/multigpu_advtrain/test_runner.py cleverhans/train.py cleverhans/utils_tfe.py image/tflib/plot.py cleverhans/attacks_tfe.py cleverhans_tutorials/mnist_tutorial_keras.py cleverhans/experimental/certification/tests/dual_formulation_test.py cleverhans_tutorials/cifar10_tutorial_fgsm.py cleverhans/utils_pytorch.py examples/facenet_adversarial_faces/facenet_fgsm.py examples/nips17_adversarial_competition/dev_toolkit/sample_defenses/ens_adv_inception_resnet_v2/defense.py examples/nips17_adversarial_competition/eval_infra/code/eval_lib/classification_results.py tests_tf/test_model.py examples/robust_vision_benchmark/cleverhans_attack_example/main.py cleverhans/plot/__init__.py cleverhans/serial.py cleverhans/experimental/certification/neural_net_params.py scripts/print_report.py examples/multigpu_advtrain/test_attack_multigpu.py cleverhans/model_zoo/all_convolutional.py advgan/utils/test_generator.py cleverhans_tutorials/utils/save_image_to_png.py cleverhans/utils_tf.py cleverhans/devtools/mocks.py cleverhans_tutorials/tutorial_models.py cleverhans/experimental/certification/tests/neural_net_params_test.py scripts/make_confidence_report_spsa.py cleverhans_tutorials/__init__.py examples/nips17_adversarial_competition/dev_toolkit/sample_defenses/ens_adv_inception_resnet_v2/inception_resnet_v2.py examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py cleverhans/model.py cleverhans/plot/pyplot_defaults.py image/inv2z.py examples/nips17_adversarial_competition/dev_toolkit/sample_attacks/random_noise/attack_random_noise.py examples/multigpu_advtrain/utils.py examples/RL-attack/train.py cleverhans/experimental/certification/tests/utils_test.py tests_tf/test_mnist_tutorial_jsma.py discriminator_loss generator_loss train_step perturb_loss make_discriminator_model make_generator_model generate_and_save_images train classifier_loss total_loss discriminator_loss generator_loss train_step perturb_loss make_discriminator_model make_generator_model generate_and_save_images train classifier_loss total_loss CarliniWagnerL2 jsma_symbolic deepfool_batch jacobian pgd_attack apply_perturbations LBFGS_attack jsma saliency_map vatm UnrolledOptimizer TensorAdam parallel_apply_transformations jacobian_graph TensorOptimizer spm TensorGradientDescent deepfool_attack UnrolledGradientDescent margin_logit_loss SPSAAdam fgm ZERO jacobian_augmentation _apply_transformation _apply_black_border fgsm UnrolledAdam ElasticNetMethod Attack FastGradientMethod BasicIterativeMethod fixed_max_confidence_recipe unfinished_attack_configs bundle_examples_with_goal bundle_attacks Misclassify single_run_max_confidence_recipe AttackConfig _WrongConfidenceFactory run_batch_with_goal _ExtraCriteriaFactory basic_max_confidence_recipe MaxConfidence save random_search_max_confidence_recipe spsa_max_confidence_recipe bundle_attacks_with_goal AttackGoal _CriteriaFactory batch_augment random_crop_and_flip random_shift run_canary softmax_cross_entropy_with_logits reduce_any reduce_max reduce_sum reduce_function reduce_prod reduce_mean reduce_min make_confidence_report make_confidence_report_bundled ConfidenceReport ConfidenceReportEntry print_stats MNIST Factory data_mnist data_cifar10 CIFAR10 maybe_download_file Dataset download_and_parse_mnist_file _AttackFactory class_and_confidence correctness_and_confidence _check_x batch_eval batch_eval_multi_worker accuracy _CorrectFactory _CorrectAndProbFactory _ClassAndProbFactory run_attack _check_y HeReLuNormalInitializer LossMixUp WeightedSum MixUp Loss CrossEntropy FeaturePairing WeightDecay LossCrossEntropy LossFeaturePairing wrapper_warning_logits CallableModelWrapper Model NoSuchLayerError wrapper_warning ResidualWithGroupNorm Softmax Sigmoid TanH GroupNorm ReLU Flatten Add Layer Dropout BatchNorm LeakyReLU PicklableModel GlobalAveragePool MLP ELU Tanh PerImageStandardize ResidualWithBatchNorm Linear Conv2D Print SELU PicklableVariable load NoRefModel save train avg_grads get_log_level safe_zip ordered_union to_categorical grid_visual deep_copy create_logger get_logits_over_interval deterministic_dict TemporaryLogLevel set_log_level random_targets pair_visual batch_indices other_classes shell_call AccuracyReport linear_extrapolation_plot _ArgsWrapper KerasModelWrapper cnn_model conv_2d data_mnist download_and_parse_mnist_file maybe_download_mnist_file convert_pytorch_model_to_tf _py_func_with_gradient mul model_train div clip_by_value assert_less_equal infer_devices assert_greater_equal tf_model_load clip_eta kl_with_logits model_loss get_available_gpus l2_batch_normalize model_argmax assert_equal initialize_uninitialized_global_variables model_eval batch_eval op_with_scalar_cast train silence model_eval train model_argmax CarliniWagnerL2 projected_optimization LBFGS_impl LBFGS VirtualAdversarialMethod FastFeatureAdversaries Noise Semantic _project_perturbation vatm SPSA MadryEtAl FastGradientMethod fgm optimize_linear arg_type ProjectedGradientDescent DeepFool SpatialTransformationMethod Attack MaxConfidence ElasticNetMethod BasicIterativeMethod MomentumIterativeMethod SaliencyMapMethod CleverHansTest list_files _list_files SimpleDataset random_feed_dict dev_version append_dev_version update_whitelist test_format_pep8 main DualFormulation NeuralNetParams Optimization read_weights eig_one_step minimum_eigen_vector diag initialize_dual DualFormulationTest NeuralNetParamsTest OptimizationTest UtilsTest ModelAllConvolutional _stride_arr _relu Softmax make_wresnet ResNet _decay Linear Flatten _batch_norm _conv _residual Conv2D Input _global_avg_pool Layer main show as_pil make_grid save get_logits_over_interval pair_visual linear_extrapolation_plot grid_visual save_pdf plot_report_from_path make_curve plot_report cifar10_tutorial main cifar10_tutorial main cifar10_tutorial main cifar10_tutorial main main evaluate_model cifar10_tutorial main ModelSubstitute train_sub prep_bbox setup_tutorial mnist_blackbox main main mnist_tutorial main mnist_tutorial main mnist_tutorial main mnist_tutorial main mnist_tutorial main mnist_tutorial attack_selection modelC modelA conv_2d modelB make_basic_picklable_cnn ModelBasicCNN ModelBasicCNNTFE check_installation main mnist_tutorial TestInception _top_1_accuracy TestSPSA InceptionModel load_images InceptionResnetV1Model load_testset main make_imagenet_cnn ModelImageNetCNN main main MadryMNIST MadryEtAlMultiGPU Evaluator create_adv_by_name make_basic_ngpu make_model make_basic_cnn make_madry_ngpu Conv2DnGPU unify_device_name Softmax MLP MaxPool LayerNorm Linear MLPnGPU clone_variable ReLU Conv2D LinearnGPU LayernGPU Flatten Layer ResNetTF Runner RunnerSingleGPU RunnerMultiGPU main run_trainer TestMadryEtAlMultiGPU TestRunnerMultiGPU TestRunMultiGPU TrainManager TrainerSingleGPU TrainerMultiGPU preprocess_batch read_CIFAR10 read_CIFAR100 cifar_tf_preprocess unpickle read_SVHN svhn_tf_preprocess get_image parse_args main download_image load_defense_output read_submissions_from_directory Submission AttacksOutput DatasetMetadata Attack Defense main parse_args compute_and_save_scores_and_ranking load_images main save_images InceptionModel load_images main save_images load_images main save_images load_images main load_images main load_images main inception_resnet_v2_arg_scope inception_resnet_v2 inception_resnet_v2_base block8 block35 block17 load_images save_images load_target_class main load_images save_images load_target_class main get_extract_command_template make_directory_writable SubmissionValidator load_defense_output shell_call main print_in_box main EvaluationMaster print_header save_dict_to_file make_directory_writable WorkerError AttackSubmission ExecutableSubmission sudo_remove_dirtree is_docker_still_running get_id_of_running_docker EvaluationWorker main DefenseSubmission kill_docker_container ClassificationBatches ResultMatrix read_classification_results analyze_one_classification_result CompetitionStorageClient CompetitionDatastoreClient NoTransactionBatch iterate_with_exp_backoff download_dataset DatasetMetadata enforce_epsilon_and_compute_hash ImageBatchesBase AversarialBatches DatasetBatches participant_from_submission_path CompetitionSubmissions is_unclaimed get_integer_time WorkPiecesBase DefenseWorkPieces AttackWorkPieces FakeDatasetMeta ClassificationResultsTest FakeStorageClient make_entity FakeDatastoreClientTransaction FakeBlob FakeDatastoreKey FakeDatastoreClient FakeDatastoreEntity FakeDatastoreClientBatch FakeDatastoreClientTest FakeDatastoreKeyTest FakeStorageClientTest FakeDatastoreClientTransactionTest FakeDatastoreEntityTest AdversarialBatchesTest ImageBatchesBaseTest DatasetBatchesTest ParticipantFromSubmissionPathTest SubmissionsTest WorkPiecesBaseTest AttackWorkPiecesTest DefenseWorkPiecesTest main ValidationStats SubmissionValidator get_extract_command_template make_directory_writable SubmissionValidator load_defense_output shell_call DQNModel parse_args make_env play dueling_model model parse_args maybe_load_model maybe_save_model make_env attack cleverhans_attack_wrapper RVBCleverhansModel py_func_grad inv_fn inv_fn gen_fn save_adversary cla_fn MnistWganInv inf_train_gen recursive_search iterative_search save_images Batchnorm Conv1D enable_default_weightnorm Conv2D set_weights_stdev unset_weights_stdev enable_default_weightnorm Deconv2D set_weights_stdev unset_weights_stdev enable_default_weightnorm Layernorm enable_default_weightnorm disable_default_weightnorm set_weights_stdev unset_weights_stdev Linear main impl print_accuracies main main main main make_confidence_report_spsa current deprecated TestMNISTTutorialPytorch TestElasticNetMethod TestMadryEtAl TestSpatialTransformationMethod TestAttackClassInitArguments TestOptimizeLinear TestBasicIterativeMethod TestProjectedGradientDescent SimpleSpatialBrightPixelModel TestFastGradientMethod TestSaliencyMapMethod CommonAttackProperties TestDeepFool TestLBFGS TestCarliniWagnerL2 TestMomentumIterativeMethod TestParseParams DummyModel TestVirtualAdversarialMethod TestSPSA SimpleModel TrivialModel TestFastFeatureAdversaries TestAttackTF SimpleModel test_misclassify_request_examples test_unfinished_attack_configs test_make_confidence_report_bundled test_save_load_confidence_report test_confidence_report LightweightDataset TestDataset TestDefenses SimpleModel TestEvaluation TestMNISTBlackboxF TestMNISTTutorialCW TestMNISTTutorialJSMA TestMNISTTutorialKerasTF TestMNISTTutorialTF TestCallableModelWrapperInitArguments TestModelClass TestPerImageStandardize TestDropout test_rejects_callable test_no_logits TestSerial TestUtils TestKerasModelWrapper numpy_kl_with_logits TestUtilsTF discriminator_loss generator_loss train_step perturb_loss make_discriminator_model make_generator_model generate_and_save_images train classifier_loss total_loss discriminator_loss generator_loss train_step perturb_loss make_discriminator_model make_generator_model generate_and_save_images train classifier_loss total_loss CarliniWagnerL2 jsma_symbolic deepfool_batch jacobian pgd_attack apply_perturbations LBFGS_attack jsma saliency_map vatm UnrolledOptimizer TensorAdam parallel_apply_transformations jacobian_graph TensorOptimizer spm TensorGradientDescent deepfool_attack UnrolledGradientDescent margin_logit_loss SPSAAdam fgm ZERO jacobian_augmentation _apply_transformation _apply_black_border fgsm UnrolledAdam ElasticNetMethod Attack FastGradientMethod BasicIterativeMethod fixed_max_confidence_recipe unfinished_attack_configs bundle_examples_with_goal bundle_attacks Misclassify single_run_max_confidence_recipe AttackConfig _WrongConfidenceFactory run_batch_with_goal _ExtraCriteriaFactory basic_max_confidence_recipe MaxConfidence save random_search_max_confidence_recipe spsa_max_confidence_recipe bundle_attacks_with_goal AttackGoal _CriteriaFactory batch_augment random_crop_and_flip random_shift run_canary softmax_cross_entropy_with_logits reduce_any reduce_max reduce_sum reduce_function reduce_prod reduce_mean reduce_min make_confidence_report make_confidence_report_bundled ConfidenceReport ConfidenceReportEntry print_stats MNIST Factory data_mnist data_cifar10 CIFAR10 maybe_download_file Dataset download_and_parse_mnist_file _AttackFactory class_and_confidence correctness_and_confidence _check_x batch_eval batch_eval_multi_worker accuracy _CorrectFactory _CorrectAndProbFactory _ClassAndProbFactory run_attack _check_y HeReLuNormalInitializer LossMixUp WeightedSum MixUp Loss CrossEntropy FeaturePairing WeightDecay LossCrossEntropy LossFeaturePairing wrapper_warning_logits CallableModelWrapper Model NoSuchLayerError wrapper_warning ResidualWithGroupNorm Softmax Sigmoid TanH GroupNorm ReLU Flatten Add Layer Dropout BatchNorm LeakyReLU PicklableModel GlobalAveragePool MLP ELU Tanh PerImageStandardize ResidualWithBatchNorm Linear Conv2D Print SELU PicklableVariable load NoRefModel save train avg_grads get_log_level safe_zip ordered_union to_categorical grid_visual deep_copy create_logger get_logits_over_interval deterministic_dict TemporaryLogLevel set_log_level random_targets pair_visual batch_indices other_classes shell_call AccuracyReport linear_extrapolation_plot _ArgsWrapper KerasModelWrapper cnn_model conv_2d data_mnist download_and_parse_mnist_file maybe_download_mnist_file convert_pytorch_model_to_tf _py_func_with_gradient mul model_train div clip_by_value assert_less_equal infer_devices assert_greater_equal tf_model_load clip_eta kl_with_logits model_loss get_available_gpus l2_batch_normalize model_argmax assert_equal initialize_uninitialized_global_variables model_eval batch_eval op_with_scalar_cast train silence model_eval train model_argmax CarliniWagnerL2 projected_optimization LBFGS_impl LBFGS VirtualAdversarialMethod FastFeatureAdversaries Noise Semantic _project_perturbation vatm SPSA MadryEtAl FastGradientMethod fgm optimize_linear arg_type ProjectedGradientDescent DeepFool SpatialTransformationMethod Attack MaxConfidence ElasticNetMethod BasicIterativeMethod MomentumIterativeMethod SaliencyMapMethod CleverHansTest list_files _list_files SimpleDataset random_feed_dict dev_version append_dev_version update_whitelist test_format_pep8 main DualFormulation NeuralNetParams Optimization read_weights eig_one_step minimum_eigen_vector diag initialize_dual DualFormulationTest NeuralNetParamsTest OptimizationTest UtilsTest ModelAllConvolutional _stride_arr _relu Softmax make_wresnet ResNet _decay Linear Flatten _batch_norm _conv _residual Conv2D Input _global_avg_pool Layer main show as_pil make_grid save get_logits_over_interval pair_visual linear_extrapolation_plot grid_visual save_pdf plot_report_from_path make_curve plot_report cifar10_tutorial main cifar10_tutorial main cifar10_tutorial main cifar10_tutorial main main evaluate_model cifar10_tutorial main ModelSubstitute train_sub prep_bbox setup_tutorial mnist_blackbox main mnist_tutorial mnist_tutorial mnist_tutorial mnist_tutorial mnist_tutorial mnist_tutorial attack_selection modelC modelA conv_2d modelB make_basic_picklable_cnn ModelBasicCNN ModelBasicCNNTFE check_installation TestInception _top_1_accuracy TestSPSA InceptionModel load_images InceptionResnetV1Model load_testset main make_imagenet_cnn ModelImageNetCNN main main MadryMNIST MadryEtAlMultiGPU Evaluator create_adv_by_name make_basic_ngpu make_model make_basic_cnn make_madry_ngpu Conv2DnGPU unify_device_name Softmax MLP MaxPool LayerNorm Linear MLPnGPU clone_variable ReLU Conv2D LinearnGPU LayernGPU Flatten Layer ResNetTF Runner RunnerSingleGPU RunnerMultiGPU main run_trainer TestMadryEtAlMultiGPU TestRunnerMultiGPU TestRunMultiGPU TrainManager TrainerSingleGPU TrainerMultiGPU preprocess_batch read_CIFAR10 read_CIFAR100 cifar_tf_preprocess unpickle read_SVHN svhn_tf_preprocess get_image parse_args main download_image load_defense_output read_submissions_from_directory Submission AttacksOutput DatasetMetadata Attack Defense main parse_args compute_and_save_scores_and_ranking load_images main save_images InceptionModel load_images main save_images load_images main save_images load_images main load_images main load_images inception_resnet_v2_arg_scope inception_resnet_v2 inception_resnet_v2_base block8 block35 block17 load_target_class load_target_class get_extract_command_template make_directory_writable SubmissionValidator load_defense_output shell_call print_in_box EvaluationMaster print_header save_dict_to_file make_directory_writable WorkerError AttackSubmission ExecutableSubmission sudo_remove_dirtree is_docker_still_running get_id_of_running_docker EvaluationWorker DefenseSubmission kill_docker_container ClassificationBatches ResultMatrix read_classification_results analyze_one_classification_result CompetitionStorageClient CompetitionDatastoreClient NoTransactionBatch iterate_with_exp_backoff download_dataset DatasetMetadata enforce_epsilon_and_compute_hash ImageBatchesBase AversarialBatches DatasetBatches participant_from_submission_path CompetitionSubmissions is_unclaimed get_integer_time WorkPiecesBase DefenseWorkPieces AttackWorkPieces FakeDatasetMeta ClassificationResultsTest FakeStorageClient make_entity FakeDatastoreClientTransaction FakeBlob FakeDatastoreKey FakeDatastoreClient FakeDatastoreEntity FakeDatastoreClientBatch FakeDatastoreClientTest FakeDatastoreKeyTest FakeStorageClientTest FakeDatastoreClientTransactionTest FakeDatastoreEntityTest AdversarialBatchesTest ImageBatchesBaseTest DatasetBatchesTest ParticipantFromSubmissionPathTest SubmissionsTest WorkPiecesBaseTest AttackWorkPiecesTest DefenseWorkPiecesTest main ValidationStats SubmissionValidator get_extract_command_template make_directory_writable SubmissionValidator load_defense_output shell_call DQNModel parse_args make_env play dueling_model model parse_args maybe_load_model maybe_save_model make_env attack cleverhans_attack_wrapper RVBCleverhansModel py_func_grad inv_fn inv_fn gen_fn save_adversary cla_fn MnistWganInv inf_train_gen recursive_search iterative_search save_images Batchnorm Conv1D enable_default_weightnorm Conv2D set_weights_stdev unset_weights_stdev enable_default_weightnorm Deconv2D set_weights_stdev unset_weights_stdev enable_default_weightnorm Layernorm enable_default_weightnorm disable_default_weightnorm set_weights_stdev unset_weights_stdev Linear main impl print_accuracies main main main main make_confidence_report_spsa current deprecated TestMNISTTutorialPytorch TestElasticNetMethod TestMadryEtAl TestSpatialTransformationMethod TestAttackClassInitArguments TestOptimizeLinear TestBasicIterativeMethod TestProjectedGradientDescent SimpleSpatialBrightPixelModel TestFastGradientMethod TestSaliencyMapMethod CommonAttackProperties TestDeepFool TestLBFGS TestCarliniWagnerL2 TestMomentumIterativeMethod TestParseParams DummyModel TestVirtualAdversarialMethod TestSPSA SimpleModel TrivialModel TestFastFeatureAdversaries TestAttackTF SimpleModel test_misclassify_request_examples test_unfinished_attack_configs test_make_confidence_report_bundled test_save_load_confidence_report test_confidence_report LightweightDataset TestDataset TestDefenses SimpleModel TestEvaluation TestMNISTBlackboxF TestMNISTTutorialCW TestMNISTTutorialJSMA TestMNISTTutorialKerasTF TestMNISTTutorialTF TestCallableModelWrapperInitArguments TestModelClass TestPerImageStandardize TestDropout test_rejects_callable test_no_logits TestSerial TestUtils TestKerasModelWrapper numpy_kl_with_logits TestUtilsTF discriminator_loss generator_loss train_step perturb_loss make_discriminator_model make_generator_model generate_and_save_images train classifier_loss total_loss discriminator_loss generator_loss train_step perturb_loss make_discriminator_model make_generator_model generate_and_save_images train classifier_loss total_loss CarliniWagnerL2 jsma_symbolic deepfool_batch jacobian pgd_attack apply_perturbations LBFGS_attack jsma saliency_map vatm UnrolledOptimizer TensorAdam parallel_apply_transformations jacobian_graph TensorOptimizer spm TensorGradientDescent deepfool_attack UnrolledGradientDescent margin_logit_loss SPSAAdam fgm ZERO jacobian_augmentation _apply_transformation _apply_black_border fgsm UnrolledAdam ElasticNetMethod Attack FastGradientMethod BasicIterativeMethod fixed_max_confidence_recipe unfinished_attack_configs bundle_examples_with_goal bundle_attacks Misclassify single_run_max_confidence_recipe AttackConfig _WrongConfidenceFactory run_batch_with_goal _ExtraCriteriaFactory basic_max_confidence_recipe MaxConfidence save random_search_max_confidence_recipe spsa_max_confidence_recipe bundle_attacks_with_goal AttackGoal _CriteriaFactory batch_augment random_crop_and_flip random_shift run_canary softmax_cross_entropy_with_logits reduce_any reduce_max reduce_sum reduce_function reduce_prod reduce_mean reduce_min make_confidence_report make_confidence_report_bundled ConfidenceReport ConfidenceReportEntry print_stats MNIST Factory data_mnist data_cifar10 CIFAR10 maybe_download_file Dataset download_and_parse_mnist_file _AttackFactory class_and_confidence correctness_and_confidence _check_x batch_eval batch_eval_multi_worker accuracy _CorrectFactory _CorrectAndProbFactory _ClassAndProbFactory run_attack _check_y HeReLuNormalInitializer LossMixUp WeightedSum MixUp Loss CrossEntropy FeaturePairing WeightDecay LossCrossEntropy LossFeaturePairing wrapper_warning_logits CallableModelWrapper Model NoSuchLayerError wrapper_warning ResidualWithGroupNorm Softmax Sigmoid TanH GroupNorm ReLU Flatten Add Layer Dropout BatchNorm LeakyReLU PicklableModel GlobalAveragePool MLP ELU Tanh PerImageStandardize ResidualWithBatchNorm Linear Conv2D Print SELU PicklableVariable load NoRefModel save train avg_grads get_log_level safe_zip ordered_union to_categorical grid_visual deep_copy create_logger get_logits_over_interval deterministic_dict TemporaryLogLevel set_log_level random_targets pair_visual batch_indices other_classes shell_call AccuracyReport linear_extrapolation_plot _ArgsWrapper KerasModelWrapper cnn_model conv_2d data_mnist download_and_parse_mnist_file maybe_download_mnist_file convert_pytorch_model_to_tf _py_func_with_gradient mul model_train div clip_by_value assert_less_equal infer_devices assert_greater_equal tf_model_load clip_eta kl_with_logits model_loss get_available_gpus l2_batch_normalize model_argmax assert_equal initialize_uninitialized_global_variables model_eval batch_eval op_with_scalar_cast train silence model_eval train model_argmax CarliniWagnerL2 projected_optimization LBFGS_impl LBFGS VirtualAdversarialMethod FastFeatureAdversaries Noise Semantic _project_perturbation vatm SPSA MadryEtAl FastGradientMethod fgm optimize_linear arg_type ProjectedGradientDescent DeepFool SpatialTransformationMethod Attack MaxConfidence ElasticNetMethod BasicIterativeMethod MomentumIterativeMethod SaliencyMapMethod CleverHansTest list_files _list_files SimpleDataset random_feed_dict dev_version append_dev_version update_whitelist test_format_pep8 main DualFormulation NeuralNetParams Optimization read_weights eig_one_step minimum_eigen_vector diag initialize_dual DualFormulationTest NeuralNetParamsTest OptimizationTest UtilsTest ModelAllConvolutional _stride_arr _relu Softmax make_wresnet ResNet _decay Linear Flatten _batch_norm _conv _residual Conv2D Input _global_avg_pool Layer main show as_pil make_grid save get_logits_over_interval pair_visual linear_extrapolation_plot grid_visual save_pdf plot_report_from_path make_curve plot_report cifar10_tutorial main cifar10_tutorial main cifar10_tutorial main cifar10_tutorial main main evaluate_model cifar10_tutorial main ModelSubstitute train_sub prep_bbox setup_tutorial mnist_blackbox mnist_tutorial mnist_tutorial mnist_tutorial mnist_tutorial mnist_tutorial mnist_tutorial attack_selection modelC modelA conv_2d modelB make_basic_picklable_cnn ModelBasicCNN ModelBasicCNNTFE check_installation TestInception _top_1_accuracy TestSPSA InceptionModel load_images InceptionResnetV1Model load_testset make_imagenet_cnn ModelImageNetCNN MadryMNIST MadryEtAlMultiGPU Evaluator create_adv_by_name make_basic_ngpu make_model make_basic_cnn make_madry_ngpu Conv2DnGPU unify_device_name Softmax MLP MaxPool LayerNorm Linear MLPnGPU clone_variable ReLU Conv2D LinearnGPU LayernGPU Flatten Layer ResNetTF Runner RunnerSingleGPU RunnerMultiGPU main run_trainer TestMadryEtAlMultiGPU TestRunnerMultiGPU TestRunMultiGPU TrainManager TrainerSingleGPU TrainerMultiGPU preprocess_batch read_CIFAR10 read_CIFAR100 cifar_tf_preprocess unpickle read_SVHN svhn_tf_preprocess get_image parse_args main download_image load_defense_output read_submissions_from_directory Submission AttacksOutput DatasetMetadata Attack Defense main parse_args compute_and_save_scores_and_ranking load_images main save_images InceptionModel load_images main save_images load_images main save_images load_images main load_images load_images inception_resnet_v2_arg_scope inception_resnet_v2 inception_resnet_v2_base block8 block35 block17 load_target_class load_target_class get_extract_command_template make_directory_writable SubmissionValidator load_defense_output shell_call print_in_box EvaluationMaster print_header save_dict_to_file make_directory_writable WorkerError AttackSubmission ExecutableSubmission sudo_remove_dirtree is_docker_still_running get_id_of_running_docker EvaluationWorker DefenseSubmission kill_docker_container ClassificationBatches ResultMatrix read_classification_results analyze_one_classification_result CompetitionStorageClient CompetitionDatastoreClient NoTransactionBatch iterate_with_exp_backoff download_dataset DatasetMetadata enforce_epsilon_and_compute_hash ImageBatchesBase AversarialBatches DatasetBatches participant_from_submission_path CompetitionSubmissions is_unclaimed get_integer_time WorkPiecesBase DefenseWorkPieces AttackWorkPieces FakeDatasetMeta ClassificationResultsTest FakeStorageClient make_entity FakeDatastoreClientTransaction FakeBlob FakeDatastoreKey FakeDatastoreClient FakeDatastoreEntity FakeDatastoreClientBatch FakeDatastoreClientTest FakeDatastoreKeyTest FakeStorageClientTest FakeDatastoreClientTransactionTest FakeDatastoreEntityTest AdversarialBatchesTest ImageBatchesBaseTest DatasetBatchesTest ParticipantFromSubmissionPathTest SubmissionsTest WorkPiecesBaseTest AttackWorkPiecesTest DefenseWorkPiecesTest ValidationStats SubmissionValidator get_extract_command_template make_directory_writable SubmissionValidator load_defense_output shell_call DQNModel parse_args make_env play dueling_model model parse_args maybe_load_model maybe_save_model make_env attack cleverhans_attack_wrapper RVBCleverhansModel py_func_grad inv_fn inv_fn gen_fn save_adversary cla_fn MnistWganInv inf_train_gen recursive_search iterative_search save_images Batchnorm Conv1D enable_default_weightnorm Conv2D set_weights_stdev unset_weights_stdev enable_default_weightnorm Deconv2D set_weights_stdev unset_weights_stdev enable_default_weightnorm Layernorm enable_default_weightnorm disable_default_weightnorm set_weights_stdev unset_weights_stdev Linear main impl print_accuracies main main main main make_confidence_report_spsa current deprecated TestMNISTTutorialPytorch TestElasticNetMethod TestMadryEtAl TestSpatialTransformationMethod TestAttackClassInitArguments TestOptimizeLinear TestBasicIterativeMethod TestProjectedGradientDescent SimpleSpatialBrightPixelModel TestFastGradientMethod TestSaliencyMapMethod CommonAttackProperties TestDeepFool TestLBFGS TestCarliniWagnerL2 TestMomentumIterativeMethod TestParseParams DummyModel TestVirtualAdversarialMethod TestSPSA SimpleModel TrivialModel TestFastFeatureAdversaries TestAttackTF SimpleModel test_misclassify_request_examples test_unfinished_attack_configs test_make_confidence_report_bundled test_save_load_confidence_report test_confidence_report LightweightDataset TestDataset TestDefenses SimpleModel TestEvaluation TestMNISTBlackboxF TestMNISTTutorialCW TestMNISTTutorialJSMA TestMNISTTutorialKerasTF TestMNISTTutorialTF TestCallableModelWrapperInitArguments TestModelClass TestPerImageStandardize TestDropout test_rejects_callable test_no_logits TestSerial TestUtils TestKerasModelWrapper numpy_kl_with_logits TestUtilsTF discriminator_loss generator_loss train_step perturb_loss make_discriminator_model make_generator_model generate_and_save_images train classifier_loss total_loss discriminator_loss generator_loss train_step perturb_loss make_discriminator_model make_generator_model generate_and_save_images train classifier_loss total_loss CarliniWagnerL2 jsma_symbolic deepfool_batch jacobian pgd_attack apply_perturbations LBFGS_attack jsma saliency_map vatm UnrolledOptimizer TensorAdam parallel_apply_transformations jacobian_graph TensorOptimizer spm TensorGradientDescent deepfool_attack UnrolledGradientDescent margin_logit_loss SPSAAdam fgm ZERO jacobian_augmentation _apply_transformation _apply_black_border fgsm UnrolledAdam ElasticNetMethod Attack FastGradientMethod BasicIterativeMethod fixed_max_confidence_recipe unfinished_attack_configs bundle_examples_with_goal bundle_attacks Misclassify single_run_max_confidence_recipe AttackConfig _WrongConfidenceFactory run_batch_with_goal _ExtraCriteriaFactory basic_max_confidence_recipe MaxConfidence save random_search_max_confidence_recipe spsa_max_confidence_recipe bundle_attacks_with_goal AttackGoal _CriteriaFactory batch_augment random_crop_and_flip random_shift run_canary softmax_cross_entropy_with_logits reduce_any reduce_max reduce_sum reduce_function reduce_prod reduce_mean reduce_min make_confidence_report make_confidence_report_bundled ConfidenceReport ConfidenceReportEntry print_stats MNIST Factory data_mnist data_cifar10 CIFAR10 maybe_download_file Dataset download_and_parse_mnist_file _AttackFactory class_and_confidence correctness_and_confidence _check_x batch_eval batch_eval_multi_worker accuracy _CorrectFactory _CorrectAndProbFactory _ClassAndProbFactory run_attack _check_y HeReLuNormalInitializer LossMixUp WeightedSum MixUp Loss CrossEntropy FeaturePairing WeightDecay LossCrossEntropy LossFeaturePairing wrapper_warning_logits CallableModelWrapper Model NoSuchLayerError wrapper_warning ResidualWithGroupNorm Softmax Sigmoid TanH GroupNorm ReLU Flatten Add Layer Dropout BatchNorm LeakyReLU PicklableModel GlobalAveragePool MLP ELU Tanh PerImageStandardize ResidualWithBatchNorm Linear Conv2D Print SELU PicklableVariable load NoRefModel save train avg_grads get_log_level safe_zip ordered_union to_categorical grid_visual deep_copy create_logger get_logits_over_interval deterministic_dict TemporaryLogLevel set_log_level random_targets pair_visual batch_indices other_classes shell_call AccuracyReport linear_extrapolation_plot _ArgsWrapper KerasModelWrapper cnn_model conv_2d data_mnist download_and_parse_mnist_file maybe_download_mnist_file convert_pytorch_model_to_tf _py_func_with_gradient mul model_train div clip_by_value assert_less_equal infer_devices assert_greater_equal tf_model_load clip_eta kl_with_logits model_loss get_available_gpus l2_batch_normalize model_argmax assert_equal initialize_uninitialized_global_variables model_eval batch_eval op_with_scalar_cast train silence model_eval train model_argmax CarliniWagnerL2 projected_optimization LBFGS_impl LBFGS VirtualAdversarialMethod FastFeatureAdversaries Noise Semantic _project_perturbation vatm SPSA MadryEtAl fgm optimize_linear arg_type ProjectedGradientDescent DeepFool SpatialTransformationMethod MaxConfidence ElasticNetMethod MomentumIterativeMethod SaliencyMapMethod CleverHansTest list_files _list_files SimpleDataset random_feed_dict dev_version append_dev_version update_whitelist test_format_pep8 main DualFormulation NeuralNetParams Optimization read_weights eig_one_step minimum_eigen_vector diag initialize_dual DualFormulationTest NeuralNetParamsTest OptimizationTest UtilsTest ModelAllConvolutional _stride_arr _relu Softmax make_wresnet ResNet _decay Linear Flatten _batch_norm _conv _residual Conv2D Input _global_avg_pool Layer main show as_pil make_grid save get_logits_over_interval pair_visual linear_extrapolation_plot grid_visual save_pdf plot_report_from_path make_curve plot_report cifar10_tutorial cifar10_tutorial main cifar10_tutorial main cifar10_tutorial main main evaluate_model cifar10_tutorial main ModelSubstitute train_sub prep_bbox setup_tutorial mnist_blackbox mnist_tutorial mnist_tutorial mnist_tutorial mnist_tutorial mnist_tutorial mnist_tutorial attack_selection modelC modelA conv_2d modelB make_basic_picklable_cnn ModelBasicCNN ModelBasicCNNTFE check_installation TestInception _top_1_accuracy TestSPSA InceptionModel load_images InceptionResnetV1Model load_testset make_imagenet_cnn ModelImageNetCNN MadryMNIST MadryEtAlMultiGPU Evaluator create_adv_by_name make_basic_ngpu make_model make_basic_cnn make_madry_ngpu Conv2DnGPU unify_device_name Softmax MLP MaxPool LayerNorm Linear MLPnGPU clone_variable ReLU Conv2D LinearnGPU LayernGPU Flatten Layer ResNetTF Runner RunnerSingleGPU RunnerMultiGPU main run_trainer TestMadryEtAlMultiGPU TestRunnerMultiGPU TestRunMultiGPU TrainManager TrainerSingleGPU TrainerMultiGPU preprocess_batch read_CIFAR10 read_CIFAR100 cifar_tf_preprocess unpickle read_SVHN svhn_tf_preprocess get_image parse_args main download_image load_defense_output read_submissions_from_directory Submission AttacksOutput DatasetMetadata Attack Defense main parse_args compute_and_save_scores_and_ranking load_images save_images InceptionModel load_images main save_images load_images save_images load_images load_images load_images inception_resnet_v2_arg_scope inception_resnet_v2 inception_resnet_v2_base block8 block35 block17 load_target_class load_target_class get_extract_command_template make_directory_writable SubmissionValidator load_defense_output shell_call print_in_box EvaluationMaster print_header save_dict_to_file make_directory_writable WorkerError AttackSubmission ExecutableSubmission sudo_remove_dirtree is_docker_still_running get_id_of_running_docker EvaluationWorker DefenseSubmission kill_docker_container ClassificationBatches ResultMatrix read_classification_results analyze_one_classification_result CompetitionStorageClient CompetitionDatastoreClient NoTransactionBatch iterate_with_exp_backoff download_dataset DatasetMetadata enforce_epsilon_and_compute_hash ImageBatchesBase AversarialBatches DatasetBatches participant_from_submission_path CompetitionSubmissions is_unclaimed get_integer_time WorkPiecesBase DefenseWorkPieces AttackWorkPieces FakeDatasetMeta ClassificationResultsTest FakeStorageClient make_entity FakeDatastoreClientTransaction FakeBlob FakeDatastoreKey FakeDatastoreClient FakeDatastoreEntity FakeDatastoreClientBatch FakeDatastoreClientTest FakeDatastoreKeyTest FakeStorageClientTest FakeDatastoreClientTransactionTest FakeDatastoreEntityTest AdversarialBatchesTest ImageBatchesBaseTest DatasetBatchesTest ParticipantFromSubmissionPathTest SubmissionsTest WorkPiecesBaseTest AttackWorkPiecesTest DefenseWorkPiecesTest ValidationStats SubmissionValidator get_extract_command_template make_directory_writable SubmissionValidator load_defense_output shell_call DQNModel parse_args make_env play dueling_model model parse_args maybe_load_model maybe_save_model make_env attack cleverhans_attack_wrapper RVBCleverhansModel py_func_grad inv_fn inv_fn gen_fn save_adversary cla_fn MnistWganInv inf_train_gen recursive_search iterative_search save_images Batchnorm Conv1D enable_default_weightnorm Conv2D set_weights_stdev unset_weights_stdev enable_default_weightnorm Deconv2D set_weights_stdev unset_weights_stdev enable_default_weightnorm Layernorm enable_default_weightnorm disable_default_weightnorm set_weights_stdev unset_weights_stdev Linear main impl print_accuracies main main main make_confidence_report_spsa current deprecated TestMNISTTutorialPytorch TestElasticNetMethod TestMadryEtAl TestSpatialTransformationMethod TestAttackClassInitArguments TestOptimizeLinear TestBasicIterativeMethod TestProjectedGradientDescent SimpleSpatialBrightPixelModel TestFastGradientMethod TestSaliencyMapMethod CommonAttackProperties TestDeepFool TestLBFGS TestCarliniWagnerL2 TestMomentumIterativeMethod TestParseParams DummyModel TestVirtualAdversarialMethod TestSPSA SimpleModel TrivialModel TestFastFeatureAdversaries TestAttackTF SimpleModel test_misclassify_request_examples test_unfinished_attack_configs test_make_confidence_report_bundled test_save_load_confidence_report test_confidence_report LightweightDataset TestDataset TestDefenses SimpleModel TestEvaluation TestMNISTBlackboxF TestMNISTTutorialCW TestMNISTTutorialJSMA TestMNISTTutorialKerasTF TestMNISTTutorialTF TestCallableModelWrapperInitArguments TestModelClass TestPerImageStandardize TestDropout test_rejects_callable test_no_logits TestSerial TestUtils TestKerasModelWrapper numpy_kl_with_logits TestUtilsTF Conv2DTranspose Sequential add Conv2D LeakyReLU BatchNormalization Sequential add Dense Conv2D LeakyReLU Flatten Dropout ones_like zeros_like cross_entropy reduce_max reduce_sum zeros trainable_variables apply_gradients gradient zip subplot format model axis imshow savefig figure range time format clear_output train_step print DataFrame to_csv numpy save zip append round range makedirs Reshape Dense Reshape warn inputs warn minimum maximum argmax int list fill_diagonal discard reshape set abs max range len update reshape other_classes run zeros sum enumerate append xrange gradients int product reshape debug float jacobian copy model_argmax shape saliency_map apply_perturbations floor info bool len int value fill_diagonal constant ones reshape while_loop float32 warn cast int32 floor bool update get_shape list min vstack zip range deepfool_attack sum norm clip inf debug squeeze model_argmax copy flatten shape info zeros abs array range run warn to_float one_hot reduce_max reduce_sum warn resize_images convert_to_tensor min astype rotate translate sqrt pad int32 float to_float list product reduce_max parallel_apply_transformations reduce_sum choice map_fn stack gather_nd linspace zip stop_gradient get_probs argmax equal einsum convert_to_tensor reshape map_fn _apply_black_border tile to_float str one_hot ones bundle_attacks ProjectedGradientDescent copy AttackConfig int32 Noise range append to_float str one_hot ones bundle_attacks ProjectedGradientDescent copy AttackConfig int32 Noise range append to_float str one_hot ones bundle_attacks ProjectedGradientDescent copy AttackConfig int32 Noise range append Noise bundle_attacks AttackConfig str correctness_and_confidence copy ConfidenceReport mean ConfidenceReportEntry info zeros bundle_attacks_with_goal str get_criteria start mean run_batch_with_goal save info request_examples time hasattr get_criteria params print_progress attack new_wins ConfidenceReportEntry save get_attack_config run_attack enumerate print print_stats str min mean info append str zeros_like ConfidenceReport mean new_wins ConfidenceReportEntry info save range len to_float str one_hot ones bundle_attacks copy AttackConfig int32 append SPSA range pad Graph time vstring warn op_func stop_gradient softmax_cross_entropy_with_logits_v2 hasattr get_set run_recipe set_log_level getattr factory max_val callable dataset_factory Session INFO mean sum print maximum Semantic time correctness_and_confidence get_set run_attack print set_log_level set_random_seed ConfidenceReport MaxConfidence ConfidenceReportEntry save print_stats dataset_factory factory Session INFO join urlretrieve gettempdir isfile maybe_download_file join open expand_dims to_categorical download_and_parse_mnist_file print reshape to_categorical astype shape load_data _check_x _CorrectFactory batch_eval_multi_worker _check_y _check_x min batch_eval_multi_worker _ClassAndProbFactory append max _check_y _check_x min batch_eval_multi_worker _CorrectAndProbFactory max _check_y _AttackFactory _check_x batch_eval_multi_worker _check_y infer_devices int str update debug extend ceil run_canary run zip append float range enumerate len update str debug warn dict run zip append range len split warn warn dump batch_size randint warn get_next avg_grads xrange run infer_devices str list ema_decay placeholder ceil update RandomState shuffle initialize_uninitialized_global_variables info float nb_epochs _ArgsWrapper int evaluate run_canary AdamOptimizer int32 ExponentialMovingAverage global_variables_initializer callable len append add_n zip len int list range remove zeros ravel warn sum to_categorical astype choice other_classes shape int32 xrange zeros argmax warn warn warn warn setLevel setFormatter getLogger addHandler StreamHandler Formatter OrderedDict sorted keys append len join list debug group match range compile len copy Sequential model Activation add join warn warn MNIST warn get_default_graph getrandbits out_features softmax_cross_entropy_with_logits inputs op warn reduce_mean variables_initializer len global_variables run fprop argmax equal _ArgsWrapper update run minimum get_shape list maximum reduce_sum square sqrt div clip_by_value xrange abs len RandomState minimize warn model_loss AdamOptimizer _ArgsWrapper get_available_gpus warn list_local_devices cast_clip is_scalar dtype cast gradient assign Saver apply_gradients filename eager train_dir batch_indices get_params join Variable int str batch_size Variable debug min get_probs range assign generate ceil zeros float eager len Variable get_probs eager to_float dtype stop_gradient softmax_cross_entropy_with_logits optimize_linear gradients assert_greater_equal reduce_max reduce_sum cast assert_less_equal clip_by_value append equal append tuple dtype as_np_dtype to_float get_shape list mul reduce_max maximum reduce_sum square sign sqrt xrange stop_gradient abs equal len dtype init_state nest while_loop flatten shape project_perturbation cast assert_less_equal random_uniform no_op join isdir _list_files abspath pardir endswith listdir append isdir astype md5 update sorted list_files dev_version extend join relpath append shell_call pardir adv_class set_verbosity input_minval num_classes model_json input_maxval read_weights range sizes true_class NeuralNetParams get_full_psd_matrix set_differentiable_objective init_dual_file info checkpoint print reshape DualFormulation verbosity initialize_dual epsilon get_tensor load_checkpoint transpose reshape append keys get_variable_to_shape_map str reshape num_hidden_layers astype float32 item append range get_variable norm reshape transpose square matmul vector_prod_fn reduce_sum eig_one_step range l2_normalize debug get_shape append trainable_variables l2_loss ResNet checkpoint_dir latest_checkpoint exit set_log_level DEBUG Session mkstemp close shell_call save fromarray dtype min issubdtype max floating int concatenate shape sqrt ceil zeros float split show squeeze pause add_subplot axis set_window_title imshow figure ion enumerate ioff show add_subplot axis set_window_title imshow figure range get_logits l2_normalize reshape float lin_space placeholder expand_dims generate FastGradientMethod show use plot xlabel ylabel set_window_title clf savefig figure linspace legend xlim argmax range gcf PdfPages close savefig load plot_report max plot concatenate make_curve xlabel print ylabel get_color array str sorted warn append float len do_eval set_random_seed CrossEntropy stop_gradient DEBUG Session map placeholder set_log_level attack prefetch generate range get_logits RandomState CIFAR10 get_set batch print AccuracyReport float32 dict train BasicIterativeMethod ModelAllConvolutional cifar10_tutorial check_installation CarliniWagnerL2 FastGradientMethod Saver save MomentumIterativeMethod MNIST do_eval get_logits get_set float32 placeholder set_log_level dict set_random_seed generate FastGradientMethod Session INFO evaluate_model model set_image_dim_ordering restore set_learning_phase set_session format get_checkpoint_state mkdir ConfigProto time collect evaluate model_eval cnn_model load_data set_random_seed str get_logits print model_eval CrossEntropy ModelBasicCNN train ModelSubstitute str int get_logits print hstack jacobian_augmentation CrossEntropy xrange argmax jacobian_graph MNIST str Session get_logits RandomState train_sub prep_bbox print model_eval float32 placeholder set_log_level generate DEBUG FastGradientMethod argmax get_set mnist_blackbox save_model model set_image_dim_ordering set_random_seed CrossEntropy Saver save stop_gradient KerasModelWrapper Session restore set_learning_phase set_session exit placeholder generate range format RandomState get_checkpoint_state FastGradientMethod ConfigProto get_set MNIST time collect print AccuracyReport model_eval float32 the_model evaluate2 train BasicIterativeMethod MomentumIterativeMethod makedirs mnist_tutorial to_categorical LBFGS run ones mkdir evaluate Variable cnn_model global_variables_initializer fc_modelC do_eval DEBUG make_basic_picklable_cnn set_log_level get_factory attack get_logits dict ModelBasicCNN keys clip ModelBasicCNNTFE attack_class attack_selection Sequential Activation add Sequential model Activation add Sequential MLP warn join int index xrange zeros asarray read_pairs min choice load_data append get_paths max range len make_imagenet_cnn set_random_seed random_uniform generate FastFeatureAdversaries abs ys to_categorical CIFAR10Data dataset_dir xs float32 placeholder MadryMNIST Saver data_mnist FastGradientMethod BasicIterativeMethod get_probs MLP make_basic_cnn layers MLPnGPU MLPnGPU basicConfig model_train eval TrainerSingleGPU TrainerMultiGPU info HParams keys namedtuple run_trainer load close open join unpickle concatenate reshape transpose mean append range len join concatenate reshape transpose mean append unpickle random_crop per_image_standardization random_saturation random_flip_left_right resize_image_with_crop_or_pad random_brightness random_contrast join concatenate transpose flatten mean append loadmat range len print resize_image_with_crop_or_pad random_crop add_argument ArgumentParser print str join read convert urlopen save resize crop exists join format partial write index set add ThreadPool enumerate imap_unordered close parse_args flush len set_defaults join Attack Defense append listdir items list join targeted_attack_names image_by_base_filename len dataset_image_count write_ranking write_score_matrix get_target_class zeros sum keys attack_names get_true_label dataset_metadata save_all_classification load_defense_output intermediate_results_dir name DatasetMetadata AttacksOutput compute_and_save_scores_and_ranking mkdir output_dir save_target_classes clip_and_copy_attack_outputs run basename Glob append enumerate max_epsilon INFO load_images save_images input_dir warn iter_alpha num_iter load_target_class endswith iteritems error shell_call print len seed use_gpu SubmissionValidator mkdtemp temp_dir submission_filename call submission_type validate_submission print_in_box print len prepare_attacks compute_results limited_dataset EvaluationMaster show_status cleanup_datastore round_name cleanup_defenses blacklisted_submissions command warning prepare_defenses cleanup_attacks_with_zero_images cleanup_failed_attacks check_output shell_call get_id_of_running_docker run_work EvaluationWorker shell_call decode int BytesIO seek reader size get_blob warning download_to_file StringIO iteritems read_classification_results hasattr iter join convert astype hexdigest BICUBIC warning save resize array clip data join iteritems basename bucket_name copyfile call mkdir exists endswith isdigit basename startswith time FakeDatastoreKey isinstance boolean_flag SimpleMonitor make wrap_dqn str print close capture_frame render reset VideoRecorder float step len join time format put relatively_safe_pickle_dump save_state log get join pickle_load format log load_state exists FastGradientMethod str get_default_graph randint str subplots reshape text axis close imshow savefig train_gen inv_fn norm randn print reshape multiply rand printout cla_fn gen_fn inv_fn norm randn print reshape multiply rand printout cla_fn max gen_fn int isinstance imsave reshape transpose astype sqrt zeros floating as_list embedding_lookup param ones batch_normalization zeros moments param ones reshape batch_normalization zeros moments get_set set_log_level set_random_seed impl dataset_factory factory Session INFO Semantic time print accuracy ProjectedGradientDescent print_accuracies make_confidence_report make_confidence_report_bundled train_start get_set bundle_examples_with_goal report_path warn which_set MaxConfidence factory max_val dataset_factory test_end test_start train_end get_set float32 set_log_level set_random_seed NB_CLASSES dataset_factory factory spsa_max_confidence_recipe Session INFO make_confidence_report_spsa hasattr print warn mean correctness completed mean print warn append array AttackConfig unfinished_attack_configs request_examples list Misclassify AttackConfig array ConfidenceReportEntry ConfidenceReport SimpleDataset MLP make_confidence_report_bundled get_factory Session Linear load copy ConfidenceReport ConfidenceReportEntry save zeros assert_raises ones ProjectedGradientDescent NoLogitsModel generate Session assert_raises Session exp sum numpy_softmax log | # CleverHans (latest release: v3.0.1) <img src="https://github.com/tensorflow/cleverhans/blob/master/assets/logo.png?raw=true" alt="cleverhans logo"> [](https://travis-ci.org/tensorflow/cleverhans) [](https://cleverhans.readthedocs.io/en/latest/?badge=latest) This repository contains the source code for CleverHans, a Python library to benchmark machine learning systems' vulnerability to [adversarial examples](http://karpathy.github.io/2015/03/30/breaking-convnets/). You can learn more about such vulnerabilities on the accompanying [blog](http://cleverhans.io). The CleverHans library is under continual development, always welcoming [contributions](https://github.com/tensorflow/cleverhans#contributing) | 2,542 |
johnsun03/myTest | ['style transfer', 'semantic segmentation'] | ['DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs', 'Deep Photo Style Transfer'] | gen_all.py | # deep-photo-styletransfer Code and data for paper "[Deep Photo Style Transfer](https://arxiv.org/abs/1703.07511)" ## Disclaimer **This software is published for academic and non-commercial use only.** ## Setup This code is based on torch. It has been tested on Ubuntu 14.04 LTS. Dependencies: * [Torch](https://github.com/torch/torch7) (with [matio-ffi](https://github.com/soumith/matio-ffi.torch) and [loadcaffe](https://github.com/szagoruyko/loadcaffe)) * [Matlab](https://www.mathworks.com/) or [Octave](https://www.gnu.org/software/octave/) CUDA backend: | 2,543 |
jon--lee/aor | ['imitation learning'] | ['Dynamic Regret Convergence Analysis and an Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning'] | exp_mujoco/mj_ig.py expert/run_expert.py exp_mujoco/mj_mig.py exp/analyze_trials.py tools/lrc.py exp_mujoco/analyze_hopper.py exp/analyze_cartpole6.py exp_mujoco/analyze_walker_instantaneous.py exp/analyze_lambdas.py exp_mujoco/analyze_hopper_alpha.py tools/supervisor.py exp_mujoco/analyze_walker.py tools/lqr.py exp_mujoco/analyze_hopper_static.py exp/analyze_cartpole6_instantaneous.py exp/analyze_cartpole6_static.py exp/analyze_alpha.py exp_mujoco/analyze_hopper_instantaneous.py tools/online_est.py exp_mujoco/analyze_walker_alpha.py tools/learner.py expert/load_policy.py tools/svm.py tools/poly_est.py tools/lr.py exp/cartpole_ig.py tools/statistics.py exp/cartpole_mig.py exp/cartpole_dagger.py exp_mujoco/mj_dagger.py tools/svm2.py exp_mujoco/analyze_walker_static.py expert/tf_util.py plot_results aggregate plot_results aggregate plot_results aggregate plot_results aggregate aggregate aggregate_save load_data main CartpoleDagger CartpoleDaggerLambda CartpoleIG main CartpoleIGLambda main CartpoleMIGLambda CartpoleMIG load_policy main function GetFlat l2loss single_threaded_session save_state fancy_slice_2d argmax max lrelu initialize set_value get_placeholder mem_friendly_function numel conv2d batchnorm intprod SetFromFlat sum make_session module switch densenobias dropout concatenate flattenallbut0 normc_initializer in_session mean get_parents eval topsorted wndense load_state get_placeholder_cached _MemFriendlyFunction dense var _Function get_session Module min lengths_to_mask categorical_sample_logits reset var_shape flatgrad scope_vars std plot_results aggregate plot_results aggregate plot_results aggregate plot_results aggregate plot_results aggregate plot_results aggregate plot_results aggregate plot_results aggregate main MujocoDagger MujocoIG main main MujocoMIG Learner run LR LRC PolyEst eval_agent_statistics_discrete collect_traj mean evaluate_lnr_discrete mean_sem stats F F2 ste Supervisor Supervisor3 Supervisor2 SVM svm_sgd evaluate load arange close mean_sem open zeros enumerate len errorbar arange xlabel ylabel tight_layout title legend len cumsum subplot ylim clf max subplot title savefig legend mean_sem append errorbar plot xscale close tight_layout cla enumerate join T zeros len T max append load join close zeros enumerate open run_trials add_argument CartpoleDagger ArgumentParser vars parse_args CartpoleIG CartpoleMIG build_policy function float32 placeholder expert_policy_file load_policy log mean get_shape copy set_shape cast cond shape random_uniform ConfigProto ConfigProto update variables_initializer global_variables set run assign run restore Saver get_session get_session Saver dirname save makedirs matmul get_variable square matmul sqrt sum get_variable switch extend square mean sqrt get_variable _Function isinstance values _MemFriendlyFunction isinstance append get_parents get pop gradients shape int64 cast reshape convert_to_tensor log placeholder reset_default_graph MujocoDagger MujocoIG MujocoMIG render reset step array range str norm coef_ print square mean alpha append hinge_loss array range collect_traj len mean append array range collect_traj mean append array range collect_traj sem mean ste eval_agent_statistics_discrete intended_action render reset sample_action append step range dot norm mean square evaluate len append zeros range enumerate | jon--lee/aor | 2,544 |
jonasrauber/linear-region-attack | ['adversarial attack'] | ['Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks'] | qpsolver.py lr_attack.py staxmod.py utils.py main.py examples.py _cifar_example ConvNet get_example find_starting_point_likely_class_strategy find_starting_point load_cifar10 get_cifar_example load_params find_starting_point_simple_strategy flatten_predict flatten l2_distance get_other_classes layer_size line_search run return_classes_logits_layer_sizes calculate_normalizer get_region estimate_layer_norms init_region generic_get_A operator_norm_lower_bound flatten_dims misclassification_polytope accuracy relu_polytope biased_direction main solve state_update_fun solve_jit step cond_fun serial affine leaky_relu track_input_no_params affine_no_params parallel scatter is_device_array onehot join asarray transpose astype extend float32 append expanduser device_put tree_map nth_likely_class_starting_point info reshape squeeze argsort device_get sum enumerate reshape squeeze argsort info device_get sum enumerate partial architecture init load_cifar10 load_params trange predict_class len atleast_1d squeeze astype float32 sign shuffle isinstance shape list f map flatten info partial tuple shape jvp relu_polytope jvp_fun vjp misclassification_polytope reshape get_A fori_loop info get_A Adot info zip concatenate ones estimate_layer_norms shape info append get_A list norm ATdot len extend mean vmap info sample zeros range append enumerate predict_class info reshape ndim device_get flatnonzero norm debug device_put uniform device_get biased_direction normal norm reshape pi dot uniform flatten_predict regions predict_class_logits_layer_sizes jit warning linspace get_other_classes line_search seed calculate_normalizer squeeze get_region onehot solve shape find_starting_point device_get append sum range init_region partial concatenate operator_norm_lower_bound info max_other_classes time reshape l2 accuracy device_put len add_argument makedirs warning dirname ArgumentParser save parse_args setLevel exists INFO run norm info ATdot maximum dot Adot sum get_A amax logical_or logical_and info gt step info max partial zeros_like while_loop where info isposinf array solve_jit time info zip len zip len zeros vjp grad float32 astype | jonasrauber/linear-region-attack | 2,545 |
jonatasbcchagas/aco_thop | ['combinatorial optimization'] | ['Ants can orienteer a thief in their robbery'] | src/run_all_experiments.py src/santos_and_chagas_programs/run_algorithms.py launcher launcher system | # Ants can orienteer a thief in their robbery This repository contains the source code and data associated to the paper ["Ants can orienteer a thief in their robbery"](https://www.sciencedirect.com/science/article/abs/pii/S0167637720301255) by Jonatas B. C. Chagas and Markus Wagner. The paper presents a Swarm Intelligence Based on Ant Colony Optimization (ACO) for solving the Thief Orienteering Problem (ThOP). ### Compiling the code Before running our ACO algorithm, it is needed to compile its code. To this end, just run the following command: ```console $ make ``` ### Usage: ```console $ ./acothop [parameters] | 2,546 |
jonathanherzig/semantic-parsing-annotation | ['semantic parsing'] | ["Don't paraphrase, detect! Rapid and Effective Data Collection for Semantic Parsing"] | nsp/__init__.py grammar_generation/grammar_gen.py nsp/models/copynet_seq2seq.py nsp/dataset_readers/__init__.py nsp/metrics/denotation_accuracy.py nsp/models/__init__.py nsp/run.py nsp/metrics/__init__.py nsp/metrics/token_sequence_accuracy.py nsp/dataset_readers/nsp_reader.py Grammar is_error execute_lfs write_all_derivs find_valid_lf prune_generated _parse_args rep_to_empty_list format_lf tupleit _run_experiment _run_all _get_logger _parse_args NamespaceSwappingField CopyNetDatasetReader is_error DenotationAccuracy format_lf lexicalize_entity pick_derivations rep_to_empty_set TokenSequenceAccuracy CopyNetSeq2Seq print check_output close NamedTemporaryFile devnull flush open sum replace execute_lfs print min append enumerate len join apply_semantic_functions format find_valid_lf print len append max enumerate print add_argument ArgumentParser join setFormatter format getLogger addHandler strftime Formatter dirname info DEBUG setLevel FileHandler train_model_from_file _run_experiment load format info open append range len | # Don’t paraphrase, detect! Rapid and Effective Data Collection for Semantic Parsing Author implementation of the following [EMNLP 2019 paper](https://www.aclweb.org/anthology/D19-1394.pdf). ## Setup 1. Install AllenNLP by following [these instructions](https://github.com/allenai/allennlp#installation). 2. Unzip GeoQuery and Scholar databases ``` unzip lib/data/overnight/dbs.zip -d lib/data/overnight/ ``` | 2,547 |
jonathanherzig/zero-shot-semantic-parsing | ['semantic parsing'] | ['Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing'] | src/py/artificial.py src/py/attnspec.py src/py/outputlayer.py src/py/overnight.py src/py/encdecspec.py src/py/zero_shot/paths_utils.py src/py/zero_shot/attention_utils.py src/py/encoderdecoder.py src/py/atis.py src/py/geo880.py src/py/zero_shot/delex_data.py src/py/atislexicon.py src/py/example.py src/py/zero_shot/parse_lexicons.py src/py/verify_same_params.py fast_align/src/force_align.py src/py/zero_shot/prepare_cross_domain.py src/py/augmentation.py src/py/lexicon.py src/py/domain_stats_vocab.py src/py/main.py src/py/lstm.py src/py/gru.py src/py/neural.py src/py/zero_shot/fast_align_utils.py src/py/derivation.py src/py/domains.py src/py/attention.py src/py/zero_shot/dataset.py src/py/grammar.py src/py/zero_shot/create_dev_split.py src/py/zero_shot/print_results.py src/py/vanillarnn.py src/py/zero_shot/aligner.py src/py/zero_shot/preprocess.py src/py/spec.py src/py/rnnlayer.py src/py/zero_shot/inference.py fast_align/build/force_align.py src/py/vocabulary.py popen_io main Aligner popen_io main Aligner augment_entities write_data gen_nested augment_nesting sample_nested get_templates main split_logical_form read_examples process main print_aligned handle_dollars read_db handle_flight_numbers clean_id clean_name get_ccg_lexicon get_lexicon parse_entry get_manual_lexicon handle_times AttentionModel AttentionSpec main Augmenter Derivation AtisDomain GeoqueryDomain ArtificialDomain new test_get_nesting_alignments test_get_entity_alignments pick_derivations test_process_lf Domain main OvernightDomain to_lisp_tree DomainStatsVocab EncoderDecoderSpec EncoderDecoderModel Example read_examples split_logical_form write reduce_copying main process Grammar GRULayer strip_unk Lexicon LSTMLayer decode evaluate_dev evaluate_train find_kb_const get_augmenter update_model write_stats run_shell run init_spec baseline_replace make_heatmap get_spec print_accuracy_metrics get_similar_from_schema load_dataset preprocess_data get_input_vocabulary load_raw_all _parse_args configure_theano get_lexicon main run_server evaluate get_output_vocabulary get_model NeuralModel OutputLayer main split_lf process concat_all RNNLayer load Spec VanillaRNNLayer main read check Vocabulary sims_to_indices _parse_args Loaded_model create_sims_matrix train_model_ce BiRNN get_matrices get_tokens get_matrix get_ground_sim train_domain deanonymize_entity calc_pred_sim calc_sim get_trained_model get_mask argmax_x get_pred_embed deanonymize_number deanonymize_date get_similar_from_schema deanonymize_time get_similar_from_schema_multiple argmax_y write_domain create_dev_split process_file Dataset Delexicalizer globaly_delex_adj replace_adjs delex_domains get_domain_adj_stats inspect_alignment_file load_cond_prob load_cond_probs inspect_alignment load_alignment train_alignment create_align_input Predictor parse_lexicon update write_domain process_domain process_cross_domain print_result start Popen readline format align strip write exit close Aligner flush join list product shuffle append join add set get_templates list sorted get_templates set print join seed write_data gen_nested new sample_nested sample Augmenter replace split read_examples join basename print split_logical_form append join sorted glob process makedirs strip replace replace join add_handler add_handler add_handler handle_dollars handle_flight_numbers read_db add_entries Lexicon handle_times print ljust zip append max len join add_entries Lexicon int print split join dirname abspath append range len join dirname abspath join dirname abspath recurse split startswith print postprocess_lf enumerate preprocess_lf print get_entity_alignments enumerate preprocess_lf print get_nesting_alignments enumerate preprocess_lf test_get_nesting_alignments test_get_entity_alignments test_process_lf join write reduce_copying PorterStemmer match join print add_argument exit print_help ArgumentParser RNN_TYPES parse_args theano_profile theano_fast_compile float32 float32 in_vocabulary set_out_vocabulary spec get_output_vocabulary set_in_vocabulary get_input_vocabulary get_model out_vocabulary in_vocabulary domain_stats_vocab Example lexicon append out_vocabulary print exit copy startswith get_spec_class constructor float32 format print float sum len items list lexicon enumerate update Delexicalizer list format print get_pred_embed dict find_kb_const get_similar_from_schema lexicon y_toks enumerate pos join format print baseline_replace len decode_greedy x_str decode_beam baseline split append classify score_derivation y_toks x_toks preprocess_time_2 enumerate items sorted list mean cosine append array get_embedding decode x_str y_toks_delex Delexicalizer x_orig p in_vocabulary compare_answers print_accuracy_metrics append sum format get_trained_model infer_succ Predictor float y_toks out_vocabulary enumerate join y_str print delexicalize y_orig subdomain x_toks len decode join print in_vocabulary strip Example lexicon out_vocabulary len css_color append range enumerate print Bottle run seed int print shuffle dev_seed dev_data dev_frac load_dataset round train_data len augment Augmenter split use_lexicon load print DomainStatsVocab get_spec get_output_vocabulary get_lexicon load_file train_data get_input_vocabulary print evaluate print preprocess_data evaluate update_model stats_file print close dumps open evaluate_dev shell model_seed save get_augmenter write_stats seed run_shell init_spec new domain server save_file preprocess_data get_model load_raw_all configure_theano run_server print train _parse_args run replace concat_all load get_params read all print zip abs check join Delexicalizer format print len load_alignment split append enumerate open append split print fit vocabulary_ get_matrix len len zeros max enumerate split zeros where transform BasicLSTMCell DropoutWrapper bidirectional_dynamic_rnn Saver save random_uniform argmax max transpose get_matrices placeholder matmul identity reduce_sum int64 cast expand_dims embedding_lookup greater_equal global_variables_initializer VocabularyProcessor create_sims_matrix tile equal join constant truncated_normal Variable not_equal minimize float32 sequence_loss zeros Dataset makedirs seed list train_model_ce shuffle set_random_seed zip get_ground_sim ones enumerate len argmax get_similar_from_schema get_embedding calc_pred_sim list items sorted get_pred_embed calc_sim append mean array get_embedding sqrt sum find_entity_spans sorted split parse replace month day year hour parse replace int seed join int rstrip format print write_domain len shuffle append round open process_file join replace_adjs dict get_domain_adj_stats exists update Counter split enumerate open append split open set Delexicalizer format __del__ globaly_delex_adj print delex_domain sleep format print len split append open join print wait returncode Popen append join open join endswith dict open float split load_cond_prob zip join inspect_alignment split append enumerate open get int str print enumerate split join parseString tuple strip nestedExpr set add open join rstrip format print len append open DATA_DEV_MODE_PATH DATA_DEV_MODE_CROSS_PATH sorted DATA_TEST_MODE_CROSS_PATH write_domain dict DATA_TEST_MODE_PATH process_domain get format print sort append len | ## Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing ### Jonathan Herzig and Jonathan Berant Code for the zero-shot semantic parser described in our EMNLP 2018 [paper](https://arxiv.org/pdf/1804.07918.pdf). The structure mapper implementation is an extension of [this code](https://worksheets.codalab.org/worksheets/0x50757a37779b485f89012e4ba03b6f4f/). #### Setup 1. Install [Miniconda2](https://conda.io/miniconda.html) 1. Install Stanford CoreNLP: ```bash $ wget http://nlp.stanford.edu/software/stanford-corenlp-full-2016-10-31.zip | 2,548 |
jonfd/kvistur | ['part of speech tagging'] | ['Kvistur 2.0: a BiLSTM Compound Splitter for Icelandic'] | kvistur.py train.py scripts/preprocess_germanet12.py main Node Kvistur CharEncoder main encode_data read_data main read_germanet get_tree print form flatten Kvistur get_binary split shuffle range pad_sequences array Bidirectional Sequential model_dir ArgumentParser max values ModelCheckpoint read_data Embedding Adam add parse_args vocab val update set Dense mkdir TimeDistributed InputLayer compile join encode_data add_argument summary LSTM train fit strip format read_germanet len | # Kvistur Kvistur is a BiLSTM-based compound word analyzer. The work is described in the paper [Kvistur 2.0: a BiLSTM Compound Splitter for Icelandic](https://arxiv.org/abs/2004.07776). # Train on GermaNet 12.0 ``` wget http://www.sfs.uni-tuebingen.de/GermaNet/documents/compounds/split_compounds_from_GermaNet12.0.txt python scripts/preprocess_germanet12.py split_compounds_from_GermaNet12.0.txt germanet.txt python train.py --train germanet.txt --model-dir germanet ``` # Requirements * Python 3.7+ | 2,549 |
jonsafari/clustercat | ['chunking', 'word alignment', 'machine translation'] | ['BIRA: Improved Predictive Exchange Word Clustering'] | bin/ngram_counts.py python/clustercat.py load tag_stdin cluster save main tag_string split print tag_string stdin join str list items int print check_output exit close dirname abspath isfile append find_executable Popen split load tag_stdin print add_argument cluster tag ArgumentParser save parse_args out | # ClusterCat: Fast, Flexible Word Clustering Software [](https://travis-ci.org/jonsafari/clustercat) [](http://www.gnu.org/licenses/lgpl-3.0) [](https://opensource.org/licenses/MPL-2.0) ## Overview ClusterCat induces word classes from unannotated text. It is programmed in modern C, with no external libraries. A Python wrapper is also provided. Word classes are unsupervised part-of-speech tags, requiring no manually-annotated corpus. Words are grouped together that share syntactic/semantic similarities. | 2,550 |
jonvw28/SLICUAV | ['superpixels'] | ['SLIC-UAV: A Method for monitoring recovery in tropical restoration projects through identification of signature species using UAVs'] | manuscript_code/3_Landscape_mapping/2019_10_23_1_compute_superpixel_features/2019_10_23_stack_feats_grid_segs_2.py manuscript_code/3_Landscape_mapping/2020_04_28_1_forward_prediction/STEP_2a_2020_01_27_check_Harapan_location.py manuscript_code/1_Assessing_models/2019_09_19_1_generate_superpixel_features/trees/treemap.py manuscript_code/3_Landscape_mapping/2019_10_23_1_compute_superpixel_features/trees/treemap.py slicuav/superpixelfeatures.py manuscript_code/1_Assessing_models/2019_09_12_6_generate_crown_superpixels/2019_09_12_complete_clustering_pal_ran.py manuscript_code/3_Landscape_mapping/2020_04_28_1_forward_prediction/Step_2b_2020_04_17_check_deforestation_location.py manuscript_code/3_Landscape_mapping/2019_09_03_1_generate_grid_superpixels/2019_09_03_grid_clustering_5.py manuscript_code/3_Landscape_mapping/2019_09_03_1_generate_grid_superpixels/trees/treemap.py manuscript_code/3_Landscape_mapping/2019_10_23_1_compute_superpixel_features/2019_10_23_stack_feats_grid_segs_9.py manuscript_code/3_Landscape_mapping/2019_09_03_1_generate_grid_superpixels/2019_09_03_grid_clustering_4.py manuscript_code/3_Landscape_mapping/2019_10_23_1_compute_superpixel_features/2019_10_23_stack_feats_grid_segs_10.py manuscript_code/3_Landscape_mapping/2019_09_03_1_generate_grid_superpixels/2019_09_03_grid_clustering_7.py manuscript_code/3_Landscape_mapping/2019_10_23_1_compute_superpixel_features/2019_10_23_stack_feats_grid_segs_12.py manuscript_code/3_Landscape_mapping/2019_10_23_1_compute_superpixel_features/2019_10_23_stack_feats_grid_segs_1.py manuscript_code/3_Landscape_mapping/2019_10_23_1_compute_superpixel_features/2019_10_23_stack_feats_grid_segs_4.py slicuav/__init__.py manuscript_code/3_Landscape_mapping/2019_10_23_1_compute_superpixel_features/trees/clusterfeatures.py slicuav/treemap.py manuscript_code/3_Landscape_mapping/2019_10_23_1_compute_superpixel_features/2019_10_23_stack_feats_grid_segs_8.py manuscript_code/1_Assessing_models/2019_09_12_6_generate_crown_superpixels/trees/clusterfeatures.py manuscript_code/1_Assessing_models/2019_09_12_6_generate_crown_superpixels/2019_09_12_complete_clustering_trees.py manuscript_code/3_Landscape_mapping/2019_09_03_1_generate_grid_superpixels/2019_09_03_grid_clustering_6.py manuscript_code/3_Landscape_mapping/2019_10_23_1_compute_superpixel_features/2019_10_23_stack_feats_grid_segs_11.py manuscript_code/1_Assessing_models/2019_09_12_5_generate_crown_features/trees/clusterfeatures.py manuscript_code/1_Assessing_models/2019_09_19_1_generate_superpixel_features/2019_09_19_stack_feats_segs_3.py manuscript_code/3_Landscape_mapping/2019_10_23_1_compute_superpixel_features/2019_10_23_stack_feats_grid_segs_6.py manuscript_code/1_Assessing_models/2019_09_12_6_generate_crown_superpixels/2019_09_12_complete_clustering_misc.py manuscript_code/3_Landscape_mapping/2019_09_03_1_generate_grid_superpixels/trees/clusterfeatures.py manuscript_code/3_Landscape_mapping/2019_10_23_1_compute_superpixel_features/2019_10_23_stack_feats_grid_segs_5.py manuscript_code/3_Landscape_mapping/2019_10_23_1_compute_superpixel_features/trees0/clusterfeatures.py manuscript_code/3_Landscape_mapping/2019_10_23_1_compute_superpixel_features/trees0/treemap.py manuscript_code/3_Landscape_mapping/2019_10_23_1_compute_superpixel_features/2019_10_23_stack_feats_grid_segs_3.py manuscript_code/3_Landscape_mapping/2019_10_23_1_compute_superpixel_features/2019_10_23_stack_feats_grid_segs_13.py manuscript_code/3_Landscape_mapping/2019_09_03_1_generate_grid_superpixels/2019_09_03_grid_clustering_1.py manuscript_code/3_Landscape_mapping/2019_09_03_1_generate_grid_superpixels/2019_09_03_grid_clustering_3.py manuscript_code/1_Assessing_models/2019_09_19_1_generate_superpixel_features/2019_09_19_stack_feats_segs_2.py example/example_pipeline.py manuscript_code/1_Assessing_models/2019_09_19_1_generate_superpixel_features/2019_09_19_stack_feats_segs_1.py manuscript_code/1_Assessing_models/2019_09_12_5_generate_crown_features/trees/treemap.py manuscript_code/1_Assessing_models/2019_09_12_6_generate_crown_superpixels/trees/treemap.py manuscript_code/1_Assessing_models/2019_09_12_5_generate_crown_features/2019_09_12_stack_spec_text_dsm_feats_all_classes.py manuscript_code/1_Assessing_models/2019_09_19_1_generate_superpixel_features/trees/clusterfeatures.py manuscript_code/3_Landscape_mapping/2019_09_03_1_generate_grid_superpixels/2019_09_03_grid_clustering_2.py manuscript_code/3_Landscape_mapping/2019_10_23_1_compute_superpixel_features/2019_10_23_stack_feats_grid_segs_7.py ClusterFeatures TreeMap ClusterFeatures TreeMap ClusterFeatures TreeMap ClusterFeatures TreeMap ClusterFeatures TreeMap ClusterFeatures TreeMap SuperpixelFeatures TreeMap | # SLIC-UAV This is the python library SLIC-UAV which we have written as part of our accompanying manuscript (to which a link will be added in due course). This library is used for mapping tropical tree species from UAV imagery. Our pipeline centres around segmenting UAv-captures orthmosaic imagery into superpixels for which we are then able to compute a combination of features based on imagery bands and texture. We then use these features as input to modelling approaches to predict the species of each superpixel based upon a set of manually labelled crowns. This then allows extension of labels to all regions of the imagery. Full details on the method are outlined in our manuscript, but we include brief details on the steps here to enable you to run our pipeline. ## Loading Library We recommend that you download a copy of this repository and make sure you know where it is saved. To load our library in python is then as simple as calling: ``` import slicuav ``` where you may need to be careful to ensure that you either in the parent directory for slicuav, or that you use appropriate paths to connect to the library (it is not designed to load as part of your standard python libraries, though you can move it to your libraries location to be able to always load if you know how to do this). This will then load the definitions of two classes used for our pipeline as now detailed ### TreeMap | 2,551 |
josdyr/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 size range reversed zeros_like asarray tolist discount_rewards UnityEnvironment close MockCommunicator UnityEnvironment close MockCommunicator reset str local_done print agents step close reset MockCommunicator UnityEnvironment len UnityEnvironment close MockCommunicator reset_default_graph close reset_default_graph reset_default_graph reset_default_graph reset_default_graph flatten list range len get_batch Buffer assert_array append_update_buffer make_mini_batch append reset_agent array range Curriculum Curriculum Curriculum MetaCurriculum assert_has_calls MetaCurriculumTest increment_lessons assert_called_with MetaCurriculumTest increment_lessons assert_called_with assert_not_called MetaCurriculumTest set_all_curriculums_to_lesson_num MetaCurriculumTest dict update MetaCurriculumTest reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph assert_array_almost_equal array discount_rewards TrainerController | <img src="docs/images/unity-wide.png" align="middle" width="3000"/> <img src="docs/images/image-banner.png" align="middle" width="3000"/> # Unity ML-Agents Toolkit (Beta) **The Unity Machine Learning Agents Toolkit** (ML-Agents) is an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. We also provide implementations (based on TensorFlow) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be | 2,552 |
josedolz/IVD-Net | ['medical image segmentation'] | ['IVD-Net: Intervertebral disc localization and segmentation in MRI with a multi-modal UNet'] | optimizer.py Blocks.py medicalDataLoader.py progressBar.py IVD_Net.py layers.py utils.py main.py conv_block maxpool conv_decod_block conv_block_Asym_Inception Conv_residual_conv_Inception_Dilation IVD_Net_asym croppCenter Conv_residual_conv_Inception_Dilation_asymmetric bottleNeck convBatch residualConv upSampleConv conv ResidualConv downSampleConv runTraining weights_init make_dataset MedicalImageDataset Adam hide_cursor printProgressBar print_flush show_cursor verbose _CursorInfo getOneHotSegmentation DicesToDice to_var getSingleImageBin saveImages computeDiceOneHotBinary inference getTargetSegmentation predToSegmentation BatchNorm2d Sequential Conv2d BatchNorm2d Sequential ReLU Conv2d BatchNorm2d Sequential ConvTranspose2d MaxPool2d shape zeros int Sequential layer activ insert append BatchNorm2d WScaleLayer data normal_ xavier_normal fill_ printProgressBar DicesToDice Softmax zero_grad where DataLoader ReduceLROnPlateau saveImages save cuda getOneHotSegmentation FloatTensor ones step Adam apply from_numpy append inference IVD_Net_asym CrossEntropyLoss cat range format to_var softMax param_groups Compose mean is_available type MedicalImageDataset net predToSegmentation getTargetSegmentation CE_loss enumerate join backward print Dice_ makedirs parameters computeDiceOneHotBinary train numpy len join sort zip append listdir int decode format hasattr print float max len tuple printFunc print flush GetConsoleCursorInfo write byref _CursorInfo GetStdHandle flush SetConsoleCursorInfo GetConsoleCursorInfo write byref _CursorInfo GetStdHandle flush SetConsoleCursorInfo is_available cuda sum zeros to_var view predToSegmentation cat data str printProgressBar to_var join Softmax softMax FloatTensor makedirs getSingleImageBin enumerate eval split save_image type net cat len data printProgressBar DicesToDice where cuda getOneHotSegmentation FloatTensor from_numpy append cat to_var softMax dice eval type net predToSegmentation enumerate zeros numpy len | josedolz/IVD-Net | 2,553 |
josepatino/Voice-Privacy-Challenge-2020 | ['speaker recognition'] | ['Speaker anonymisation using the McAdams coefficient'] | baseline/local/featex/create_xvector_f0_data.py baseline/local/results_to_latex.py baseline/local/anon/anonymise_dir_mcadams.py baseline/local/featex/f0_yaapt/amfm_decompy/basic_tools.py baseline/local/featex/f0_yaapt/get_f0.py baseline/local/plot/plot_spk_xvectors_voxceleb.py baseline/local/prepare_for_eer.py baseline/local/featex/split_am_nsf_data.py baseline/local/vc/nsf/config_libri_nsf.py baseline/local/featex/check_pitch_feats.py baseline/local/featex/create_ppg_data.py baseline/local/plot/plot_spk_xvectors.py baseline/local/vc/am/config_libri_am.py baseline/local/featex/split_test_data.py baseline/local/anon/gen_pseudo_xvecs.py baseline/local/fix_eval2.py baseline/local/featex/f0_yaapt/amfm_decompy/pYAAPT.py baseline/local/create_uniform_segments.py baseline/local/plot/plot_spk_dur.py baseline/local/anon/anonymise_dir_mcadams_rand_seed.py baseline/local/featex/create_melspec_data.py baseline/local/featex/f0_yaapt/amfm_decompy/pyQHM.py baseline/local/anon/compute_spk_pool_cosine.py baseline/local/scoring/linkability/compute_linkability.py segment prepare_segments_file get_wave_segments get_combined_line_asr get_combined_line_asv get_table enhance_table print_table generate_asv_line get_o_or_a parse_scores sort_table generate_asr_line finalise_table get_extra_dataset initialise_table combine_tables anonym get_seed_from_string anonym select_random_xvec extractF0 SignalObj pcm2float nlfer dynamic5 peaks PitchObj refine dynamic spec_track stride_matrix time_track path1 BandpassFilter crs_corr cmp_rate ClassProperty yaapt exp_matrix g1 qhm SampleWindow qhm_iteration eaqhm R_eq aqhm_iteration least_squares freq_correction HM_run error_calc ComponentObj ModulatedSign g0 aqhm get_cmap get_cmap segment prepare_segments_file get_wave_segments get_combined_line_asr get_combined_line_asv get_table enhance_table print_table generate_asv_line get_o_or_a parse_scores sort_table generate_asr_line finalise_table get_extra_dataset initialise_table combine_tables anonym get_seed_from_string anonym select_random_xvec extractF0 SignalObj pcm2float nlfer dynamic5 peaks PitchObj refine dynamic spec_track stride_matrix time_track path1 BandpassFilter crs_corr cmp_rate ClassProperty yaapt exp_matrix g1 qhm SampleWindow qhm_iteration eaqhm R_eq aqhm_iteration least_squares freq_correction HM_run error_calc ComponentObj ModulatedSign g0 aqhm get_cmap get_cmap ceil int float pop segment check_output float split join int format readlines len append get_wave_segments range split get_table print_table combine_tables remove close dirname abspath isfile open writelines makedirs get_combined_line_asr get_combined_line_asv zip append finalise_table initialise_table join split join split enhance_table generate_asv_line strip len extend sort_table generate_asr_line finalise_table range append initialise_table sorted append str join append get_extra_dataset join strip get_o_or_a join get_o_or_a lfilter arange pi abs max poly exp angle len real sum eps size astype sqrt hanning load minimum lpc write float32 zeros array makedirs print print sum mean sample zeros range enumerate dtype asarray print tofile samp_values close SignalObj isfile open yaapt dtype asarray ClassProperty data refine SignalObj filtered_version PitchObj spec_track get fs nlfer concatenate dynamic size time_track pop int print fix set_values BandpassFilter len set_energy rfft arange around set_frames_pos filtered sum frame_jump nfft size float empty new_fs int fix frame_size stride_matrix zeros len lfilter rfft peaks nframes abs max std ones tolist argmin filtered ceil append sum prod range frame_jump dynamic5 nfft medfilt mean float enumerate kaiser int new_fs fix frame_size stride_matrix zeros array len fs int frame_jump data size maximum fix filtered stride_matrix vstack crs_corr cmp_rate zeros abs range new_fs len minimum vuv insert sort medfilt energy argsort append zeros minimum ones reshape insert mean nframes path1 tile zeros abs int format print ones tolist logical_and min fix mean ceil zeros float max append len zeros abs path1 reshape ones transpose argmin zeros range dot stride_matrix sqrt zeros sum len int tolist logical_and fix zeros float amax as_strided HM_run data update_values arange phase_edges n_harm max values synthesize srer edges append fs size func zip zeros int fix ModulatedSign interpolate_samp array exp_matrix ones reshape length least_squares freq_correction error_calc zeros abs range len exp_matrix T exp length reshape ones pi least_squares freq_correction error_calc n_harm abs range length zeros dot reshape pi sign real zeros abs imag dot sum data real exp length reshape pi cumprod conj | # Recipe for VoicePrivacy Challenge 2020 Please visit the [challenge website](https://www.voiceprivacychallenge.org/) for more information about the Challenge. ## Install 1. `git clone --recurse-submodules https://github.com/Voice-Privacy-Challenge/Voice-Privacy-Challenge-2020.git` 2. ./install.sh ## Running the recipe The recipe uses the pre-trained models of anonymization. To run the baseline system with evaluation: 1. `cd baseline` 2. run `./run.sh`. In run.sh, to download models and data the user will be requested the password which is provided during the Challenge registration. ## General information | 2,554 |
josepatino/pyBK | ['speaker diarization'] | ['The EURECOM Submission to the First DIHARD Challenge'] | main.py diarizationFunctions.py calcClusters getSegmentationFile readSADfile smooth getBestClustering getVgMatrix binarizeFeatures performClustering binaryKeySimilarity_cdist performClusteringLinkage get_py_webrtcvad_segments trainKBM performResegmentation getSADfile py_webrtcvad getLikelihoodTable readUEMfile getSegmentBKs getSegmentTable getSpectralClustering getFeatures extractFeatures unravelMask HTKFile runDiarization load int T log2 ceil load HTKFile asarray data print ones close ndenumerate splitlines floor append zeros float enumerate open print ones close ndenumerate splitlines append zeros float round enumerate open load get_py_webrtcvad_segments remove readSADfile py_webrtcvad write close getfloat mkdir isfile range open T Vad resample squeeze size astype warn pad zeros max set_mode zeros_like where float diff range column_stack minimum arange maximum unravelMask vstack empty range insert size append diff minimum int T fill_diagonal inf multivariate_normal ones cdist size mean floor append zeros std range print getLikelihoodTable zeros size range logpdf int arange print size zeros binarizeFeatures range zeros sum floor unique T arange print zeros linkage calcClusters arange where around binarizeFeatures binaryKeySimilarity_cdist str ones append range nanmax fill_diagonal nanargmax size astype nan int T print unravel_index zeros int T arange size nan append zeros binarizeFeatures print cdist T arange cdist subtract size square where maximum mean argsort sqrt unique zeros expand_dims sum nanmax minimum T fill_diagonal arange size argmin maximum copy binaryKeySimilarity_cdist dot eigh nan unique zeros abs gaussian_filter ones convolve arange arange smooth vstack argmax seed ones append size hstack astype unique empty diff minimum int T sort unravelMask zeros GaussianMixture score_samples fit str arange print size astype write close vstack around writelines append zeros empty open getSegmentationFile arange readSADfile getfloat getBestClustering floor getVgMatrix performClustering digitize str performClusteringLinkage ones squeeze logical_and trainKBM sum performResegmentation getSADfile size astype getint readUEMfile mkdir unique getSegmentBKs getSegmentTable int print getSpectralClustering getFeatures extractFeatures isfile zeros | # pyBK - Speaker diarization python system based on binary key speaker modelling The system provided performs speaker diarization (speech segmentation and clustering in homogeneous speaker clusters) on a given list of audio files. It is based on the [binary key speaker modelling ](https://www.isca-speech.org/archive/archive_papers/interspeech_2010/i10_2118.pdf) technique. Thanks to the in-session training of a binary key background model (KBM), the system does not require any external training data, providing an easy to run and tune option for speaker diarization tasks. ## Description This implementation is based on that of [Delgado](https://ieeexplore.ieee.org/document/7268861), which is also available for [MATLAB](https://github.com/h-delgado/binary-key-diarizer). Besides the binary key related code, useful functions for a speaker diarization system pipeline are included. Extra details and functionalities were added, following our participation at [EURECOM](http://audio.eurecom.fr/) on the [Albayzin 2016 Speaker Diarization Evaluation](https://iberspeech2016.inesc-id.pt/index.php/albayzin-evaluation/) described [here](https://pdfs.semanticscholar.org/05eb/6b90ceac6ad5a6de3b54885c6b12e9c9c689.pdf), the [first DIHARD challenge](https://coml.lscp.ens.fr/dihard/2018/index.html), detailed in the [Interspeech 2018 paper](https://www.isca-speech.org/archive/Interspeech_2018/pdfs/2172.pdf), and the [IberSPEECH-RTVE Speaker Diarization Evaluation](http://iberspeech2018.talp.cat/index.php/speaker-diarization-challenge/), explained [here](https://www.isca-speech.org/archive/IberSPEECH_2018/pdfs/IberS18_AE-5_Patino.pdf). ## Installation This code is written and tested in python 3.6 using conda. It relies on a few common packages to get things done: * [numpy](https://www.numpy.org/) * [scipy](https://www.scipy.org/) * [scikit-learn](https://scikit-learn.org/) * [librosa](https://librosa.github.io/) for audio processing and feature extraction | 2,555 |
joshuadickey/seis-sim | ['template matching'] | ['Beyond Correlation: A Path-Invariant Measure for Seismogram Similarity'] | Downloader.py UTILS.py Trainer.py shakenbake get_pairwise_dists plot_cat_map catalog_snr load_dat NN_classifier compile_custom_model plot_confusion score_to_pred load_custom_model get_random_pos_pairs silentremove oneshotPlot get_cat channel_normalization encoder_network shakenbake2 get_stream_from_X haversine dataset_to_featureset prune_featureset test_model NN_score agg_dat generator_hard scatter param2name detrend normalize name2param yrmm_range extract_features residual_by_feat clean_csv association_residualizer get_triplet_dists cat_intfr calc_snr read_cat cat_to_dat test_all_models classPerf residual_by_dist get_emb get_random_neg_pairs tsnePlot get_callbacks assoc_test_gen histPlot plot_stream url_open get_stream_from_ISC generator_hard2 load_custom_model2 get_loss_acc residual_block test_dataset_waveforms url_maker subplots set_yticklabels round list sorted axvline map append range plot set_xticklabels astype bandpass reversed set mean enumerate int print set_xticks zeros array print concat unique append sort_values load join iterrows load_model values print calc_snr to_csv read_cat SPdiff zeros predict len remove ones astype range print urlretrieve flush sleep to_numeric replace to_datetime readlines where apply read_csv join reset_index clean_csv print sort_values range to_csv url_open read_cat is_file append DataFrame url_maker to_datetime astype read_csv join STA basename T iterrows print load_dat concat to_csv Client stack mkdir dirname save append TIME resample append load join reset_index concatenate print concat to_csv index read_cat take save zip append listdir join encoder_network basename load_model print param2name get_loss_acc name2param int list map dict match float keys compile split basename encoder_network load_model get_loss_acc name2param Adam compile get_loss_acc join basename TensorBoard EarlyStopping ModelCheckpoint name2param append keys abs max add print add Model append residual_block Input range int pad randint len int pad randint len shakenbake iterrows reset_index value_counts EVENTID sample choice unique append zeros DataFrame shakenbake iterrows reset_index value_counts EVENTID sample choice unique append zeros DataFrame values expand_dims gather transpose softplus relu concatenate reshape transpose slice matmul reduce_sum sqrt append reduce_min range load join list set_index print test_model to_csv set read_csv exists values load join classPerf print assoc_test_gen extend read_cat load_custom_model save detrend normalize name2param exists predict score_to_pred NN_score mean copy values join iterrows subplots TSNE concat tight_layout index subplots_adjust take scatter savefig sample fit_transform join subplots get_random_neg_pairs set_xlabel tight_layout hist set_ylabel savefig legend get_random_pos_pairs norm norm subplots grid values set_title set_xlabel NN_score savefig legend append range plot tight_layout mean keys enumerate auc join text roc_curve subplots_adjust set_ylabel zeros len max format text get_xticklabels set imshow savefig setp array range cla LAKES COASTLINE set_extent add_geometries Stamen max drop_duplicates values set_title PlateCarree LAT LAND legend append plot cla LON add_feature RIVERS add_image LON_1 print coastlines min NaturalEarthFeature get_legend_handles_labels Patch LAT_1 Trace STA UTCDateTime Stream append enumerate TIME STA get_waveforms UTCDateTime TIME data subplots plot filter times autofmt_xdate xaxis_date detrend normalize enumerate plot_stream get_stream_from_ISC get_stream_from_X Client groupby y axis x set_ticks DataFrame str list set_xlabel map set_path_effects append set_xlim set unique zip keys int get_group text dict median set_ylim cla str value_counts print classification_report roc_curve astype copy round argmax array len haversine norm iterrows reset_index values astype score_to_pred nan_to_num sample DataFrame range append min astype copy rename vstack max print concat range score_to_pred load join print compile_custom_model perf_counter load_custom_model summary detrend normalize name2param save zeros predict len zeros norm range zeros norm range update value_counts print astype round DataFrame cos map sin load join iterrows load_model values print read_cat SPdiff save zeros extract_features predict print take reset_index index subplots set_yticklabels round list sorted axvline map append range plot set_xticklabels astype bandpass reversed set mean enumerate int print set_xticks zeros array | # SEISMOGRAM SIMILARITY --- This repository presents a novel seismogram similarity measure that is explicitly invariant to path. The work is based on the paper _"Beyond Correlation: A Path-Invariant Measure for Seismogram Similarity"_ by Joshua Dickey, Brett Borghetti, William Junek and Richard Martin, which can be viewed in full on the ArXiv: https://arxiv.org/pdf/1904.07936.pdf. This repository contains three parts: 1) __SeisSim.ipynb__ - This Jupyter Notebook explores the application of our trained similarity model to the test set. To run this notebook, simply clone this directory onto your machine. No GPU is required. 2) __Downloader.py__ - This program will download both the catalogs and waveforms necesssary to train and test a seismic similarity model. The catalogs are downloaded through a web query of the International Seismological Centre (ISC) Bulletin for seismic arrivals: http://www.isc.ac.uk/iscbulletin/search/arrivals/; and the waveforms are downloaded from the Incorporated Research Institutions for Seismology (IRIS) Database using ObsPy. This process may take several days, even with a fast internet connection. 3) __Trainer.py__ - This program will train a similarity model and save it to disk. It is suggested to have at least a TitanV GPU and 64GB of System RAM. --- ### BACKGROUND #### Path-Dominant Similarity: | 2,556 |
jpainam/SLS_ReID | ['person re identification', 'data augmentation'] | ['Sparse Label Smoothing Regularization for Person Re-Identification'] | eval_cuhk03.py prepare_gan_data.py random_erasing.py prepare.py train.py test_viper.py data_generator.py dataset.py evaluate.py utils.py clustering/utils.py DCGAN/download.py eval_viper.py DCGAN/utils.py test.py evaluate_rerank.py DCGAN/model.py test_cuhk03.py re_ranking.py DCGAN/main.py model.py cuhk03_rerank.py dataset_loader.py data_manager.py DCGAN/ops.py sls_train.py clustering/clustering.py create_dataset.py evaluate_gpu.py re_index.py create_dataset re_ranking Dataset ImageDataset read_image VideoDataset DataGenerator Market1501 init_img_dataset CUHK03 DukeMTMCreID get_names VIPeR CUHK01 compute_mAP evaluate compute_mAP evaluate compute_mAP evaluate eval_cuhk03 ft_net_dense ClassBlock ft_net weights_init_classifier ft_net_middle weights_init_kaiming generate_labels_for_gan get_gan_data load_gan RandomErasing copyfolder k_reciprocal_neigh re_ranking train_model save_network SLSloss dcganDataset load_network get_id extract_feature fliplr load_network test extract_feature _re_assign_labels test _cmc_core load_network fliplr train_model save_network AverageMeter read_json get_gan_data write_json mkdir_if_missing separate_files compare_silhouette feature_extraction load_features main plot_clusters get_train_list LossHistory simpleaxis load_y_data load_test_data extract_feature load_y_train_data top_10_accuracy safe_mkdirs top_5_accuracy random_crop load_train_data safe_remove top_1_accuracy smooth_labels top_20_accuracy load_gan_data write partial_lrso_loss load_data progress download_mnist download_file_from_google_drive save_response_content _list_categories unzip prepare_data_dir get_confirm_token _download_lsun download_celeb_a download_lsun download main conv_out_size_same DCGAN lrelu batch_norm linear concat conv2d deconv2d conv_cond_concat get_image make_gif to_json visualize save_images transform image_manifold_size center_crop merge_images save_images2 show_all_variables inverse_transform imread imsave merge minimum exp zeros_like concatenate transpose astype float32 mean int32 unique append zeros sum max range len convert setdiff1d compute_mAP dot argsort intersect1d argwhere append flatten argwhere in1d zero_ range len view numpy cpu mm cumsum list defaultdict shape append sum range format asarray astype choice mean enumerate invert items print float32 argsort int32 zeros len data normal_ kaiming_normal_ __name__ constant_ data normal_ __name__ constant_ int join sorted basename glob print strip close write cluster_path open range split strip floor open basename append range concatenate glob close shuffle unique gan_path enumerate join int print dict cluster_path zeros array listdir mkdir copyfile around list int argpartition k_reciprocal_neigh data model zero_grad argmax max cuda FloatTensor load_state_dict append range state_dict format save_network item type time criterion backward print Variable train step join save is_available cuda state_dict load join which_epoch load_state_dict index_select long norm use_dense view FloatTensor print Variable size model div cuda zero_ expand_as cpu PCB fliplr range cat append int format print AverageMeter expand t eval avg savemat numpy addmm_ model_path LongTensor argsort zeros range asarray unique re_ranking transpose _re_assign_labels pairwise_distances choice dot shape unique append zeros re_rank range enumerate sum makedirs dirname mkdir_if_missing flatten basename load_model Model array append expand_dims predict img_to_array glob shuffle join preprocess_input load_img savez print summary progress len load array feature_extraction join makedirs copy array labels_ load_features progress enumerate fit show format set_yticks cluster_centers_ ylabel set_xticks scatter simpleaxis savefig load_features gca fit_transform enumerate fit calinski_harabaz_score print silhouette_samples labels_ silhouette_score load_features fit_transform fit get join sorted basename glob write close range open plot_clusters compare_silhouette get_train_list separate_files randint int write float round flush tick_bottom tick_left set_visible load join concatenate print dict shape listdir open load join list keys open load join list concatenate keys open join sorted basename int load_img img_to_array preprocess_input glob to_categorical array split append expand_dims progress enumerate len load join open load join open sum makedirs remove exists int join img_to_array load_img preprocess_input sorted squeeze len append expand_dims listdir progress predict split join int read str print write close urlopen flush open get get_confirm_token save_response_content Session items list startswith get int print remove dirname join remove download_file_from_google_drive format print rename exists urlopen print join call format join print _download_lsun mkdir exists join format print call mkdir exists mkdir checkpoint_dir input_height sample_dir output_height pprint __flags ConfigProto makedirs get_shape as_list trainable_variables analyze_vars imread zeros enumerate squeeze merge int round center_crop imresize VideoClip write_gif arange save_images batch_size sample_size run make_gif str sampler strftime uniform append ceil range gmtime save_images2 mkdir tile enumerate int print zeros output_path makedirs ceil int floor sqrt | ### SLS ReID  ### Datasets - Download [Market1501 Dataset](http://www.liangzheng.org/Project/project_reid.html) - Download [DukeMTMC-reID Dataset](https://github.com/layumi/DukeMTMC-reID_evaluation) - Download [CUHK03 Dataset](http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html) - Download [VIPeR Dataset](https://vision.soe.ucsc.edu/node/178) ### Dataset preperation Change the `download_path` to point to your dataset folder. Comment out `multi-query` except for `Market1501` dataset. ```bash | 2,557 |
jpainam/self_attention_grid | ['person re identification'] | ['Self Attention Grid for Person Re-Identification'] | resnet_default.py vgg_attention.py eval_cuhk03.py basic_layers.py image_folder_loader.py train2.py random_erasing.py resnet_attention.py prepare.py train.py reid_attention.py attention.py evaluate.py utils.py test.py evaluate_rerank.py pytorch_CAM.py test_cuhk03.py re_ranking.py test_model.py visualize_attention.py residual_attention_network.py model.py TestAttention.py cuhk03_rerank.py viewresnet.py hacnn.py attention_module.py visualize_net_attention.py dataset_loader.py data_manager.py test_reid.py evaluate_gpu.py re_index.py returnCAM hook_feature AttentionModule AttentionModule ResidualBlock re_ranking ImageDataset read_image VideoDataset Market1501 iLIDS PRID2011 CUHK03 DukeMTMCVidReID init_img_dataset DukeMTMCreID GRID get_names VIPeR CUHK01 MSMT17 Mars PRID450S iLIDSVID init_vid_dataset compute_mAP evaluate compute_mAP evaluate compute_mAP evaluate eval_cuhk03 HACNN InceptionB ChannelAttn SoftAttn SpatialAttn HarmAttn HardAttn InceptionA ConvBlock is_image_file find_classes make_dataset default_loader ImageFolderLoader ClassBlock weights_init_classifier ResNetAttentionModel weights_init_kaiming RandomErasing VGG_16 ResidualAttentionModel DenseNetModel ClassBlock weights_init_classifier ResNetAttention weights_init_kaiming ResNet resnet50 Bottleneck conv3x3 BasicBlock weights_init_kaiming copyfolder k_reciprocal_neigh re_ranking extract_feature get_id load_network fliplr load_network1 spatial_transformer gaussian_mask gaussian_glimpse load_network test extract_feature get_id load_network fliplr load_network1 load_network evaluate test train_model save_network train_model imshow show_databatch visualize_model AverageMeter read_json write_json mkdir_if_missing VGG_16 denorm ResNet34AT denorm visualize_test_set ResidualAttentionModel append numpy uint8 reshape min dot shape resize append max minimum exp zeros_like concatenate transpose astype float32 mean int32 unique append zeros sum max range len convert setdiff1d compute_mAP dot argsort intersect1d argwhere append flatten argwhere in1d zero_ range len view numpy cpu mm cumsum list defaultdict shape append sum range format asarray astype choice mean enumerate invert items print float32 argsort int32 zeros len sort listdir is_image_file join int format append listdir split data normal_ kaiming_normal_ __name__ constant_ data normal_ __name__ constant_ load_url ResNet load_state_dict listdir mkdir copyfile around list int argpartition k_reciprocal_neigh model_path join load load_state_dict load join list items OrderedDict load_state_dict model_path index_select long norm FloatTensor print Variable size model div cuda zero_ expand_as cpu fliplr range cat append int basename to_float exp reshape square reduce_sum range ndims gaussian_mask matmul split warper AffineGridWarper no_shear_2d resampler format print AverageMeter expand t eval avg savemat numpy addmm_ evaluate invert format asarray print cumsum astype float32 shape mean int32 sum range data model zero_grad max cuda open load_state_dict append range state_dict dump format save_network join time criterion backward print Variable train step join save is_available cuda state_dict save eval item vgg enumerate deepcopy empty_cache len transpose pause axis title savefig imshow make_grid data show_databatch print cpu training eval vgg empty_cache train max enumerate makedirs dirname mkdir_if_missing data model batchsize denorm numpy resize save_image cuda fromarray view range imsave format size astype ANTIALIAS Variable print moveaxis makedirs | COMING SOON | 2,558 |
jpconnel/fbrs-segmentation | ['interactive segmentation'] | ['f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation'] | train.py demo.py demo_v2.py BetterThanApp.py App_v2.py AppReplacement AppReplacement main parse_args main parse_args main load_module parse_args INTERACTIVE_MODELS_PATH mainloop load_is_model deiconify Tk InteractiveDemoApp find_checkpoint device parse_args minsize checkpoint add_argument cfg load_config_file ArgumentParser device cpu AppReplacement COLOR_BGR2RGB print getsizeof cvtColor type imread init_experiment set_sharing_strategy load_module model_path spec_from_file_location exec_module module_from_spec | ## f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation [[Paper]](https://arxiv.org/abs/2001.10331) [[PyTorch]](https://github.com/saic-vul/fbrs_interactive_segmentation/tree/master) [[MXNet]](https://github.com/saic-vul/fbrs_interactive_segmentation/tree/mxnet) [[Video]](https://youtu.be/ArcZ5xtyMCk) ## Previous repository as before, but demo_v2.py and App_v2.py scripts are new This repository provides code for training and testing state-of-the-art models for interactive segmentation with the official PyTorch implementation of the following paper: > **f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation**<br> > [Konstantin Sofiiuk](https://github.com/ksofiyuk), [Ilia Petrov](https://github.com/ptrvilya), [Olga Barinova](https://github.com/OlgaBarinova), [Anton Konushin](https://scholar.google.com/citations?user=ZT_k-wMAAAAJ)<br> > Samsung AI Center Moscow <br> > https://arxiv.org/abs/2001.10331 Please see [the video](https://youtu.be/ArcZ5xtyMCk) below explaining how our algorithm works: <p align="center"> <a href="https://youtu.be/ArcZ5xtyMCk"> | 2,559 |
jpcorb20/bet-backtranslation-paraphrase-experiment | ['paraphrase identification', 'data augmentation'] | ['BET: A Backtranslation Approach for Easy Data Augmentation in Transformer-based Paraphrase Identification Context'] | finetuneutils.py google-translate-backtranslation-da/backtranslate.py datautils.py run_loop.py torchutils.py main.py prepare_mrpc_data rename_mrpc parse_tpc_df prepare_quora_data normalize_quora prepare_data read_tsv_mrpc parse_tpc_scores prepare_tpc_data rename_quora remove_duplicates balanced_downsampling simple_accuracy process_prediction set_seed reset_gpu set_args process_train create_training_config evaluate train print_gpu all_metrics_computation debug_gpu save_perf compute_metrics create_prediction_config load_and_cache_examples process_model BinaryProcessor InputFeatures DataProcessor InputExample batch_sentences batch_translate translate_text flatten_batches DataFrame rename rename_mrpc concat to_csv read_tsv_mrpc read_excel train_test_split remove_duplicates balanced_downsampling rename index apply apply list parse_tpc_df to_csv drop parse_tpc_scores remove_duplicates read_csv train_test_split range balanced_downsampling len sample concat rename list to_csv drop apply remove_duplicates read_csv rename_quora train_test_split range balanced_downsampling len print exit seed str manual_seed_all manual_seed int floor from_pretrained to train load_and_cache_examples from_pretrained to load join get_train_examples get_labels BinaryProcessor get_dev_examples glue_convert_examples_to_features TensorDataset save get_test_examples tensor model get_linear_schedule_with_warmup tuple clip_grad_norm_ zero_grad DataLoader DataParallel DistributedDataParallel save max exists initialize list set_seed load_state_dict master_params state_dict format close mean save_pretrained trange enumerate load join int items evaluate backward AdamW print makedirs dumps tqdm parameters step len argmax update join tuple squeeze tqdm DataLoader DataParallel eval numpy compute_metrics zip append SequentialSampler load_and_cache_examples max makedirs items list print tolist read_csv append list all_metrics_computation exists argmax tuple tqdm DataParallel DataLoader eval numpy save_perf append empty_cache SequentialSampler load_and_cache_examples max print append is_tensor print get_objects empty_cache print_gpu collect debug_gpu process_prediction reset_gpu set_args process_train create_training_config create_prediction_config list unescape Client translate sleep append int list ceil append range len | # BET 🤞 We bet on it and it worked. This repository includes the scripts to train transformers (_finetuneutils.py_ and _torchutils.py_) from Huggingface library with Pytorch. We load the data from module _datautils.py_. We prepared the _main.py_ script to run the training and evaluation. Finally, we use the _run_loop.py_ to run iteratively all the configuration for BET experiment: datasets, transformers and different augmentation languages. ## Clustering Languages We clustered all the Google Translation languages into the related language families based on the information provided in the Wikipedia info-boxes. The Romance branch is illustrated in the following figure.  ## Backtranslation process  | 2,560 |
jphall663/interpretable_machine_learning_with_python | ['explainable artificial intelligence'] | ['Proposed Guidelines for the Responsible Use of Explainable Machine Learning'] | auto_ph.py cv_model_rank plot_coefs air gbm_grid get_percentile_dict plot_pd_ice cv_model_rank_select glm_grid hist_mean_pd_ice_plot marginal_effect pd_ice gbm_forward_select_train get_confusion_matrix smd train H2OGeneralizedLinearEstimator H2OGridSearch train H2OGridSearch update list asfactor print gbm_grid len copy dict H2OFrame zip append DataFrame range auc format subplots plot auc enumerate seed update sorted list isinstance copy eval unique append randint DataFrame keys cv_model_rank str print mean range len arange min as_data_frame copy append round max enumerate sort_values reset_index range copy subplots plot subplots set_title plot concat set_xlabel tight_layout subplots_adjust plot_pd_ice mean hist set_ylabel twinx legend set_ylim str list sort copy where unique DataFrame enumerate print title float sum print title float sum mean title print std | # Responsible Machine Learning with Python Examples of techniques for training interpretable machine learning (ML) models, explaining ML models, and debugging ML models for accuracy, discrimination, and security. ### Overview Usage of artificial intelligence (AI) and ML models is likely to become more commonplace as larger swaths of the economy embrace automation and data-driven decision-making. While these predictive systems can be quite accurate, they have often been inscrutable and unappealable black boxes that produce only numeric predictions with no accompanying explanations. Unfortunately, recent studies and recent events have drawn attention to mathematical and sociological flaws in prominent weak AI and ML systems, but practitioners don’t often have the right tools to pry open ML models and debug them. This series of notebooks introduces several approaches that increase transparency, accountability, and trustworthiness in ML models. If you are a data scientist or analyst and you want to train accurate, interpretable ML models, explain ML models to your customers or managers, test those models for security vulnerabilities or social discrimination, or if you have concerns about documentation, validation, or regulatory requirements, then this series of Jupyter notebooks is for you! (But *please* don't take these notebooks or associated materials as legal compliance advice.) The notebooks highlight techniques such as: * [Monotonic XGBoost models, partial dependence, individual conditional expectation plots, and Shapley explanations](https://github.com/jphall663/interpretable_machine_learning_with_python#enhancing-transparency-in-machine-learning-models-with-python-and-xgboost---notebook) * [Decision tree surrogates, reason codes, and ensembles of explanations](https://github.com/jphall663/interpretable_machine_learning_with_python#increase-transparency-and-accountability-in-your-machine-learning-project-with-python---notebook) * [Disparate impact analysis](https://github.com/jphall663/interpretable_machine_learning_with_python#increase-fairness-in-your-machine-learning-project-with-disparate-impact-analysis-using-python-and-h2o---notebook) * [LIME](https://github.com/jphall663/interpretable_machine_learning_with_python#explain-your-predictive-models-to-business-stakeholders-with-lime-using-python-and-h2o---notebook) * [Sensitivity and residual analysis](https://github.com/jphall663/interpretable_machine_learning_with_python#testing-machine-learning-models-for-accuracy-trustworthiness-and-stability-with-python-and-h2o---notebook) | 2,561 |
jraiskin/arxiv2kindle_pdf | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | arxiv2kindle_pdf.py main get_args yn_to_bool landscape add_argument yn_to_bool ArgumentParser parse_args open_pdf landscape search rename output_dir list encoding mkdtemp open_pdf range line_filter_gen format get_args chdir glob insert readlines group article_id DOTALL join print system dict sub len | # Arxiv 2 Kindle PDF arxiv2kindle_pdf: Recompiles an arxiv paper for kindle-sized screens. This code heavily builds [on `bshillingford`'s code](https://gist.github.com/bshillingford/6259986edca707ca58dd). # Usage Provide the Arxiv's article ID or the URL as an argument, e.g. ` python arxiv2kindle_pdf.py -i=https://arxiv.org/abs/1508.06576 `. Use `--help` to the get a complete list of arguments. *optional* shell-executable | 2,562 |
jrmcornish/cif | ['density estimation', 'normalising flows'] | ['Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows'] | cif/models/factory.py cif/datasets/supervised_dataset.py cif/models/components/bijections/actnorm.py cif/models/components/densities/wrapper.py cif/models/components/bijections/planar.py cif/models/components/bijections/bijection.py config/two_d.py cif/models/components/conditional_densities/bernoulli.py cif/models/components/networks.py cif/models/components/bijections/resflow.py cif/experiment.py cif/datasets/loaders.py cif/visualizer.py cif/writer.py tests/test_bijection.py cif/models/components/bijections/batchnorm.py tests/test_schemas.py cif/models/components/bijections/affine.py cif/models/components/conditional_densities/concrete.py cif/datasets/two_d.py cif/models/components/densities/__init__.py cif/datasets/tabular.py cif/models/components/conditional_densities/gaussian.py config/schemas.py cif/datasets/gaussian.py cif/models/components/conditional_densities/__init__.py config/config.py cif/models/components/bijections/invconv.py config/dsl.py config/gaussian.py cif/models/__init__.py cif/models/components/bijections/sos.py cif/models/components/bijections/ode.py cif/models/components/bijections/nsf.py cif/models/components/densities/gaussian.py cif/models/components/densities/split.py cif/models/components/densities/density.py config/images.py cif/datasets/image.py main.py config/__init__.py cif/models/components/couplers.py cif/models/components/bijections/math.py tests/test_density.py cif/trainer.py cif/datasets/__init__.py tests/test_neural_nets.py cif/models/components/bijections/acl.py cif/models/components/bijections/reshaping.py tests/test_config.py cif/models/components/densities/marginal.py cif/models/components/densities/flow.py setup.py cif/models/components/bijections/__init__.py cif/metrics.py config/tabular.py cif/models/components/bijections/made.py cif/models/components/bijections/bnaf.py cif/models/components/densities/cif.py cif/models/components/conditional_densities/conditional_density.py cif/models/components/bijections/linear.py parse_config_arg get_lr_scheduler load_run setup_density_and_loaders print_model print_num_params get_opt get_train_metrics print_test_metrics num_params setup_experiment train get_q_loss metrics rws rws_dreg iwae iwae_alt AverageMetric Trainer TwoDimensionalDensityVisualizer ImageDensityVisualizer DensityVisualizer DummyDensityVisualizer DummyWriter Tee Writer get_linear_gaussian_datasets get_well_conditioned_gaussian_datasets get_linear_gaussian_dataset get_gaussian_dataset get_image_datasets get_train_valid_image_datasets image_tensors_to_supervised_dataset NotMNIST get_raw_image_tensors get_test_image_dataset get_loaders get_loader SupervisedDataset get_gas_raw make_tabular_train_valid_split get_tabular_datasets get_miniboone_raw get_power_raw make_tabular_train_valid_test_split get_hepmass_raw get_bsds300_raw get_raw_tabular_datasets normalize_raw_data get_2d_data get_2d_datasets get_bijection get_coupler_with_shared_net get_likelihood get_bijection_density get_standard_gaussian_density get_conditional_density get_marginal_density get_uniform_density get_density_recursive get_density get_acl_bijection get_coupler get_activation get_lipschitz_net get_coupler_with_independent_nets get_net IndependentCoupler ChunkedSharedCoupler IndexedSharedCoupler SharedCoupler get_mlp LipschitzNetwork ResidualBlock MaskedLinear get_glow_cnn get_lipschitz_mlp ScaledTanh2dModule ConstantNetwork get_lipschitz_cnn AutoregressiveMLP get_resnet _get_lipschitz_conv_layer _get_lipschitz_linear_layer AffineCouplingBijection MaskedChannelwiseAffineCouplingBijection ChannelwiseAffineCouplingBijection SplitChannelwiseAffineCouplingBijection Checkerboard2dAffineCouplingBijection AlternatingChannelwiseAffineCouplingBijection ActNormBijection ConditionalAffineBijection AffineBijection BatchNormBijection CompositeBijection ConditionedBijection Bijection InverseBijection IdentityBijection LeakyReLU SoftLeakyReLU Nonlinearity BlockNeuralAutoregressiveBijection BruteForceInvertible1x1ConvBijection LUInvertible1x1ConvBijection Invertible1x1ConvBijection LULinearBijection MADEBijection ScalarMultiplicationBijection TanhBijection ScalarAdditionBijection LogitBijection ElementwiseBijection RationalQuadraticSplineBijection AutoregressiveRationalQuadraticSplineBijection CoupledRationalQuadraticSplineBijection FFJORDBijection ODEVelocityFunction _batch_dot planar_map PlanarBijection ConditionalPlanarBijection ResidualFlowBijection FlipBijection ReshapingBijection ViewBijection RandomChannelwisePermutationBijection Squeeze2dBijection SumOfSquaresPolynomialBijection BernoulliConditionalDensity concrete_sample ConcreteConditionalDensity concrete_log_prob ConditionalDensity DiagonalGaussianConditionalDensity CIFDensity Density FlowDensity DiagonalGaussianDensity diagonal_gaussian_entropy diagonal_gaussian_log_prob diagonal_gaussian_sample MarginalDensity SplitDensity PassthroughBeforeEvalDensity DequantizationDensity WrapperDensity BinarizationDensity get_base_config get_config get_datasets get_models expand_grid get_config_group expand_grid_generator get_model_config provides group GridParams base config vae config glow realnvp bernoulli_vae resflow get_st_coupler_config remove_non_normalise_layers get_preproc_schema get_cond_affine_schema get_planar_schema get_flat_resflow_schema get_logit_tf_schema add_lipschitz_config_to_resblocks get_q_coupler_config get_p_coupler_config add_cond_affine_before_each_normalise get_base_schema remove_normalise_layers get_nsf_schema get_bnaf_schema get_flat_realnvp_schema get_multiscale_resflow_schema replace_normalise_with_batch_norm get_bernoulli_vae_schema get_gaussian_vae_schema get_affine_schema get_ffjord_schema get_maf_schema replace_normalise_with_act_norm get_cond_affine_layer get_glow_schema get_coupler_config get_sos_schema apply_pq_coupler_config_settings get_binarize_schema get_coupler_net_config get_schema get_centering_tf_schema get_multiscale_realnvp_schema config linear_cond_affine_like_resflow maf realnvp nsf nonlinear_cond_affine_like_resflow resflow sos cond_affine config bnaf affine ffjord maf dlgm_deep realnvp maf_grid nsf cond_affine_grid resflow sos planar TestBatchNormBijection TestScalarMultiplicationBijection TestViewBijection _TestBijection get_channelwise_masked_acl_test TestUnconditionalBruteForceInvertible1x1ConvBijection TestUnconditionalLUInvertible1x1ConvBijection TestLogitBijection TestScalarAdditionBijection TestResidualFlowBijection TestFFJORDBijection TestConditionalLUInvertible1x1ConvBijection TestRandomChannelwisePermutationBijection TestConditionalBruteForceInvertible1x1ConvBijection TestCompositeBijection TestFlipBijection TestMADEBijection TestSqueeze2dBijection TestNonImageConditionalLUInvertible1x1ConvBijection TestUnconditionalInvertible1x1ConvBijectionEquality TestFFJORDConditionalBijection get_checkerboard_acl_test test_cif_realnvp_config test_baseline_realnvp_config TestConcreteConditionalDensity TestDiagonalGaussianDensity TestDiagonalGaussianConditionalDensity TestCIFDensity TestGlowCNN TestAutoregressiveMLP TestGetResnet test_cif_multiscale_realnvp_schema load_schema test_baseline_multiscale_realnvp_schema literal_eval split decode print dumps write_json write_textfile setup_experiment print setup_experiment dumps setup_density_and_loaders print setup_density_and_loaders print to get_density get_loaders load setup_density_and_loaders print load_state_dict Path seed manual_seed setup_density_and_loaders TwoDimensionalDensityVisualizer ImageDensityVisualizer Trainer Writer get_train_metrics device DummyWriter DummyDensityVisualizer parameters q_parameters p_parameters get_opt get_q_loss Adamax Adam SGD elbo int logsumexp mean prod log exp view exp view view detach stdout stderr long randn get_gaussian_dataset tensor randn_like view randn get_linear_gaussian_dataset data join CIFAR10 labels targets unsqueeze permute SVHN tensor dataset_class to long int image_tensors_to_supervised_dataset randperm get_raw_image_tensors cat get_raw_image_tensors get_train_valid_image_datasets get_test_image_dataset get_image_datasets print get_tabular_datasets get_loader get_linear_gaussian_datasets get_2d_datasets int int make_tabular_train_valid_split load join normalize_raw_data mean make_tabular_train_valid_test_split vstack std join get_gas_correlation_numbers any read_pickle to_numpy drop join T make_tabular_train_valid_split Counter mean append to_numpy std read_csv drop load join normalize_raw_data rand hstack shuffle delete mean make_tabular_train_valid_test_split vstack zeros std SupervisedDataset tensor get_raw_tabular_datasets permutation arange randn rand random cos pi floor vstack linspace einsum exp sin append range normal concatenate astype sqrt stack empty zeros util_shuffle T reshape repeat cbrt randint array len SupervisedDataset get_2d_data DiagonalGaussianConditionalDensity get_density_recursive get_likelihood BernoulliConditionalDensity DiagonalGaussianConditionalDensity get_density_recursive get_bijection full append ResidualBlock BatchNorm2d ReLU zero_ Conv2d append LogSoftmax activation Linear enumerate _get_lipschitz_conv_layer empty sum tanh softplus _batch_dot abs log arange logsumexp flatten shape sum log ones_like zeros_like logsumexp sample Gumbel log pi flatten shape sum log flatten shape items list list values list values pop items list isinstance dict list values warn warn add_cond_affine_before_each_normalise replace_normalise_with_batch_norm get_base_schema get_preproc_schema remove_non_normalise_layers apply_pq_coupler_config_settings remove_normalise_layers replace_normalise_with_act_norm append append append get_cond_affine_layer append isinstance append append range range append range append range append range append range range range add_lipschitz_config_to_resblocks append range add_lipschitz_config_to_resblocks enumerate int cond_affine_grid get_config get_config get_config get_schema load_schema get_config get_schema load_schema | # README This is the code we used to produce the experiments in our paper [Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows](https://arxiv.org/abs/1909.13833) (ICML 2020). It is a fork of our original codebase, which was previously maintained at https://github.com/jrmcornish/lgf. This code may be useful to anyone interested in CIFs, as well as normalising flows more generally. In particular, this code allows specifying a large variety of different common architectures -- including various primitive flow steps and multi-scale configurations -- via an intuitive intermediate representation, which is then converted into an actual flow object. Please see the "Specifying models" section below for more details. ## Setup First, install submodules: $ git submodule init $ git submodule update Next, install dependencies. If you use `conda`, the following will create an environment called `cif`: conda env create -f environment-lock.yml Activate this with | 2,563 |
jrmcornish/lgf | ['density estimation', 'normalising flows'] | ['Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows'] | lgf/models/components/networks.py lgf/datasets/__init__.py lgf/trainer.py lgf/models/components/densities/density.py lgf/writer.py lgf/models/components/bijections/affine.py lgf/visualizer.py lgf/models/components/bijections/reshaping.py lgf/models/components/densities/elbo.py lgf/models/components/bijections/acl.py lgf/datasets/gaussian.py lgf/models/components/bijections/made.py tests/test_bijection.py lgf/datasets/loaders.py lgf/models/components/densities/gaussian.py lgf/models/components/densities/__init__.py lgf/models/components/bijections/invconv.py lgf/models/components/bijections/__init__.py lgf/models/components/couplers.py lgf/models/__init__.py lgf/models/components/bijections/nsf.py lgf/models/components/bijections/math.py lgf/models/components/densities/concrete.py lgf/datasets/supervised_dataset.py lgf/models/components/densities/wrapper.py lgf/models/components/densities/split.py lgf/models/schemas.py lgf/experiment.py lgf/datasets/two_d.py lgf/models/components/bijections/batchnorm.py lgf/models/components/bijections/bnaf.py main.py lgf/models/components/bijections/bijection.py tests/test_density.py lgf/datasets/uci.py lgf/models/components/bijections/sos.py tests/test_neural_nets.py setup.py config.py lgf/datasets/image.py lgf/models/factory.py lgf/models/components/densities/exact.py lgf/models/components/bijections/linear.py get_uci_config get_config get_config_base get_images_config something get_2d_config parse_config_arg setup_density_and_loaders print_model print_schema num_params setup_experiment train AverageMetric Trainer TwoDimensionalDensityVisualizer ImageDensityVisualizer DensityVisualizer DummyDensityVisualizer DummyWriter Writer get_well_conditioned_gaussian_datasets get_gaussian_dataset get_image_datasets get_train_valid_image_datasets image_tensors_to_supervised_dataset NotMNIST get_raw_image_tensors get_test_image_dataset get_loaders get_loader SupervisedDataset get_2d_data get_2d_datasets get_gas_raw make_UCI_train_valid_test_split get_miniboone_raw get_power_raw get_raw_UCI_datasets get_hepmass_raw make_UCI_train_valid_split normalize_raw_data get_UCI_datasets get_bijection get_coupler_with_shared_net get_coupler_net get_standard_gaussian_density get_bijection_density get_conditional_density get_uniform_density get_density_recursive get_density get_acl_bijection get_coupler get_activation get_coupler_with_independent_nets get_st_coupler_config get_preproc_schema get_pure_cond_affine_schema get_logit_tf_schema get_q_coupler_config get_p_coupler_config get_base_schema process_batch_norm_layers get_nsf_schema get_bnaf_schema get_flat_realnvp_schema get_maf_schema get_cond_affine_layer get_schema_from_base get_glow_schema get_coupler_config get_sos_schema get_coupler_net_config get_schema get_centering_tf_schema get_multiscale_realnvp_schema IndependentCoupler ChunkedSharedCoupler IndexedSharedCoupler SharedCoupler get_mlp ResidualBlock MaskedLinear get_glow_cnn ScaledTanh2dModule ConstantNetwork AutoregressiveMLP get_resnet AffineCouplingBijection MaskedChannelwiseAffineCouplingBijection ChannelwiseAffineCouplingBijection SplitChannelwiseAffineCouplingBijection Checkerboard2dAffineCouplingBijection AlternatingChannelwiseAffineCouplingBijection ConditionalAffineBijection AffineBijection BatchNormBijection InverseBijection IdentityBijection Bijection CompositeBijection LeakyReLU SoftLeakyReLU Nonlinearity BlockNeuralAutoregressiveBijection BruteForceInvertible1x1ConvBijection LUInvertible1x1ConvBijection Invertible1x1ConvBijection LULinearBijection MADEBijection ScalarMultiplicationBijection TanhBijection ScalarAdditionBijection LogitBijection ElementwiseBijection RationalQuadraticSplineBijection AutoregressiveRationalQuadraticSplineBijection CoupledRationalQuadraticSplineBijection FlipBijection ReshapingBijection ViewBijection RandomChannelwisePermutationBijection Squeeze2dBijection SumOfSquaresPolynomialBijection concrete_sample ConcreteConditionalDensity concrete_log_prob Density ELBODensity BijectionMixtureDensity BijectionDensity DiagonalGaussianDensity DiagonalGaussianConditionalDensity diagonal_gaussian_entropy diagonal_gaussian_log_prob diagonal_gaussian_sample SplitDensity PassthroughBeforeEvalDensity DequantizationDensity TestBatchNormBijection TestScalarMultiplicationBijection TestViewBijection _TestBijection get_channelwise_masked_acl_test TestUnconditionalBruteForceInvertible1x1ConvBijection TestUnconditionalLUInvertible1x1ConvBijection TestLogitBijection TestScalarAdditionBijection TestConditionalLUInvertible1x1ConvBijection TestRandomChannelwisePermutationBijection TestConditionalBruteForceInvertible1x1ConvBijection TestCompositeBijection TestFlipBijection TestMADEBijection TestSqueeze2dBijection TestNonImageConditionalLUInvertible1x1ConvBijection TestUnconditionalInvertible1x1ConvBijectionEquality get_checkerboard_acl_test TestELBODensity TestConcreteConditionalDensity TestDiagonalGaussianDensity TestDiagonalGaussianConditionalDensity TestGlowCNN TestAutoregressiveMLP TestGetResnet literal_eval split print setup_experiment dumps write_json setup_density_and_loaders print device print get_schema dumps to get_loaders seed manual_seed setup_density_and_loaders LambdaLR Adamax TwoDimensionalDensityVisualizer ImageDensityVisualizer Adam SGD Trainer opt_class parameters Writer device DummyWriter CosineAnnealingLR DummyDensityVisualizer long randn get_gaussian_dataset data join CIFAR10 labels targets unsqueeze permute SVHN tensor dataset_class to long int image_tensors_to_supervised_dataset randperm get_raw_image_tensors cat get_raw_image_tensors get_train_valid_image_datasets get_test_image_dataset get_image_datasets print get_loader get_UCI_datasets get_2d_datasets permutation arange randn rand cos pi floor vstack linspace einsum exp sin append range normal concatenate astype sqrt stack zeros util_shuffle T reshape repeat cbrt randint array len SupervisedDataset get_2d_data int int make_UCI_train_valid_split load join normalize_raw_data make_UCI_train_valid_test_split mean vstack std join get_gas_correlation_numbers any read_pickle to_numpy drop join T Counter mean append to_numpy make_UCI_train_valid_split std read_csv drop load join normalize_raw_data make_UCI_train_valid_test_split rand hstack shuffle delete mean vstack zeros std get_raw_UCI_datasets tensor SupervisedDataset get_density_recursive get_bijection full get_base_schema get_preproc_schema append get_cond_affine_layer isinstance append append range range append range append range append range append range append ResidualBlock BatchNorm2d ReLU zero_ Conv2d append LogSoftmax activation Linear arange logsumexp flatten shape sum log ones_like zeros_like logsumexp sample Gumbel log pi flatten shape sum log randn_like pi flatten shape sum log flatten shape | # This codebase is out of date Please see https://github.com/jrmcornish/cif for code to reproduce our updated version of this paper, [Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows](https://arxiv.org/abs/1909.13833). # README Code release for [Localised Generative Flows](https://arxiv.org/abs/1909.13833) (LGFs). ## Usage ### Setup First, install submodules: $ git submodule init $ git submodule update Next, install dependencies. If you use `conda`, the following will create an environment called `lgf`: | 2,564 |
jsl03/apricot | ['gaussian processes'] | ['Thoughts on Massively Scalable Gaussian Processes'] | apricot/core/sampling/lhs.py apricot/extra/visualisation.py apricot/core/models/build/noise_parts.py apricot/extra/testfunctions.py apricot/core/models/build/filenames.py apricot/core/models/interface.py apricot/core/math.py apricot/tests/emulator_tests.py apricot/core/models/prior/__init__.py apricot/core/linear.py apricot/core/models/__init__.py apricot/core/optimisation.py apricot/core/fit.py apricot/core/models/prior/ls_inv_gamma.py apricot/core/emulator.py apricot/cache/__init__.py apricot/core/models/build/__init__.py apricot/core/sampling/sobol.py apricot/core/models/mle.py apricot/__init__.py apricot/core/__init__.py apricot/core/models/glue.py apricot/core/utils.py apricot/core/models/cv.py apricot/core/models/model_cache.py apricot/core/sampling/hypercube.py apricot/core/sampling/__init__.py apricot/core/sampling/adaptive.py apricot/core/models/build/code_snippets.py apricot/tests/sampling_tests.py apricot/core/models/parse.py apricot/core/models/optimisation_utils.py apricot/core/models/initialisation.py apricot/core/models/build/mean_parts.py apricot/core/logger.py apricot/core/models/hmc.py apricot/core/models/type_aliases.py apricot/core/models/build/builder.py apricot/core/models/build/components.py setup.py apricot/core/models/build/kernel_parts.py apricot/core/models/build/core_parts.py apricot/core/exceptions.py apricot/core/models/map.py apricot/core/visualisation.py apricot/tests/kernel_tests.py apricot/core/sampling/factorial.py has_flag cpp_flag BuildExt get_pybind_include defined_on_index format_input assign_internal_gp Emulator ShapeError MissingParameterError raise_NotParsed raise_NotImplemented fit_cv fit_mle fit_map fit_hmc fit get_lreg_model LinearModel get_logger mad integrate_mixture _check_grid _check_bounds optimise grid_search _check_grid_shape_1d _check_grid_shape_nd_internal _check_grid_shape_nd _get_grid clear_model_cache force_f_array show_keys set_seed inspect_cache maybe is_string join_strings join_lines flatten to_list random_seed _atleast2d_fview plot_divergences get_param assign_param_name plot_parameter parse_param_str make_objective_fixed_noise run_optimiser run_cv cv_glue make_objective_infer_noise map_glue match hmc_glue slice_in_order param_to_2dfarray assign_init _hmc_post_internal check_divergent check_rhat run_hmc check_tree_saturation same_lengths check_ebfmi permute_aligned calc_ebfmi init_from_data init_from_str get_init make_pystan_dict Interface map_internal run_map _assign_new_seed make_objective_fixed_noise run_mle mle_glue run_optimiser make_objective_infer_noise fixed_sigma_output_helper load compile_model memo get_filename load_from_pickle prompt_cache get_bounds_grid get_theta_grid theta_grid_search scale_vector exp_grid parse_mean_other parse_mean parse_noise parse_noise_internal parse_kernel parse_warping_string parse_noise_float parse_noise_str parse_warping parse_mean_str assmeble_model_code fuse fuse_code_blocks none_to_list StanModelNoise StanModelMeanFunction StanModelKernel StanModelPart get_core mean_part_filename noise_part_filename find_kernel warp_sig apply_warping apply _default_sig make_kernel make_mean make_noise inv_gamma_cdf parse_option format_options inv_gamma_pdf min_spacing ls_inv_gamma_prior inv_gamma_tail create_objective solve_inv_gamma max_entropy _power_d_less_n cartesian factorial urandom sample_hypercube optimised_lhs _maximin lhs mdurs eval_criteria init_v prime_ge sobol i4_bit_hi1 sobol_scatter i4_sobol2 is_prime i4_bit_lo0 Branin Friedman WingWeight Piston Franke _f_circuit _f_piston Sphere Cosines _f_borehole _f_camel3 _f_friedman Camel3 _f_sphere Ishigami _f_branin _f_wing_weight RobotArm Camel6 _cosd _f_cosines Circuit _f_arm Borehole Higdon _make_ishigami _f_higdon _f_gramacy_lee GramacyLee _f_franke TestFunction _f_camel6 surface TestEmulator f_1d TestKernels TestSampling has_flag atleast_1d force_f_array squeeze reshape list format keys format Interface Emulator hmc hmc_glue Emulator map map_glue Emulator run_mle Emulator run_cv setFormatter getLogger addHandler StreamHandler Formatter setLevel INFO mean abs mean grid_search minimize func _get_grid atleast_1d _check_grid_shape_nd _check_grid_shape_1d any squeeze format format seed randint debug max seed debug random_seed isinstance isinstance isinstance endswith walk append remove format endswith print walk format search sub parse_param_str subplot int assign_param_name plot set_xlabel set_xlim tight_layout GridSpec set_ylabel figure legend range max log distplot set_yticklabels GridSpec max show subplot list set_xlabel range plot set_xticklabels set_xlim T set_yticks min set_xticks set_ylabel figure set_ylim len make_objective_fixed_noise get_theta_grid noise_type debug run_optimiser kernel_type cast make_objective_infer_noise theta_grid_search minimize debug CVEqKernel debug CVEqKernel noise_type any sub theta beta full slice_in_order sigma param_to_2dfarray beta full theta sigma assign_init _hmc_post_internal check_rhat check_divergent check_tree_saturation get_init check_ebfmi sampling make_pystan_dict random_seed extract tolist calc_ebfmi index get_sampler_params permute_aligned empty range len isinstance seed permutation sum any sum ones ls_inv_gamma_prior warping shape full zeros std format isinstance debug Integral type ones warping full zeros std random_seed map_internal get_init make_pystan_dict random_seed join format debug optimizing _assign_new_seed copy range make_objective_fixed_noise get_theta_grid noise_type debug run_optimiser kernel_type cast make_objective_infer_noise theta_grid_search NLMLEqKernel NLMLEqKernel noise_type get_filename isfile join debug StanModel assmeble_model_code prompt_cache print lower get_bounds_grid std sample_hypercube empty_like scale_vector exp_grid enumerate likelihood_func argmin empty range format AVAILABLE isinstance format AVAILABLE float Integral isinstance isinstance AVAILABLE format warning format Integral AVAILABLE isinstance type format isinstance get_core append list replace zip format format find_kernel warp_sig copy _default_sig copy parse_option format_options min_spacing solve_inv_gamma empty enumerate min outer shape empty masked_array abs range inv_gamma_cdf sum set_seed obj random sqrt create_objective root empty array range isinstance max entropy sample_hypercube index_dimension _power_d_less_n warning info len set_seed lower sort sum range list permutation set_seed random linspace empty range set_seed cdist argmin random delete mean empty range permutation set_seed arange random copy linspace empty eval_criteria range format hasattr tril_indices isinstance cdist LhsCriteria cast set_seed empty any i4_sobol2 next range set_seed empty i4_sobol2 next range floor floor zeros array ceil max ceil int sqrt int init_v floor i4_bit_lo0 zeros range bitwise_xor _cosd reshape cos pi sum cos pi log T sum sqrt pi sin ravel griddata subplot update set_label append_axes plot make_axes_locatable set_xlabel min GridSpec colorbar set_ylabel contourf linspace figure max | ## What is apricot? **apricot** is a probabilistic modelling toolbox, built for analysing the outputs of computer codes using Gaussian Process (GP) regression. The focus of the package is on providing easy-to-build, robust models of predictive uncertainty. Parameter inference for apricot's GP models is performed using [Stan](https://mc-stan.org/), interfaced with python using [pyStan](https://github.com/stan-dev/pystan). | 2,565 |
jspenmar/DejaVu_Features | ['image retrieval'] | ['Same Features, Different Day: Weakly Supervised Feature Learning for Seasonal Invariance'] | losses/contextual.py models/dejavu.py utils/ops.py losses/contextual_triplet.py models/building_blocks.py main.py load_image ContextualLoss ContextualTripletLoss ResidualBlock ConvBlock DejaVu np2torch upsample_like img2torch random_sample get_map_location downsample reshape_as_vectors get_device extract_kpt_vectors imread isinstance device long arange view shape stack extract_kpt_vectors permute | # Deja-Vu Features This repository contains the network architecture, losses and pretrained feature models from [Deja-Vu](https://www.researchgate.net/publication/340273695_Same_Features_Different_Day_Weakly_Supervised_Feature_Learning_for_Seasonal_Invariance). ## Introduction "Like night and day" is a commonly used expression to imply that two things are completely different. Unfortunately, this tends to be the case for current visual feature representations of the same scene across varying seasons or times of day. The aim of this paper is to provide a dense feature representation that can be used to perform localization, sparse matching or image retrieval, regardless of the current seasonal or temporal appearance. Recently, there have been several proposed methodologies for deep learning dense feature representations. These methods make use of ground truth pixel-wise correspondences between pairs of images and focus on the spatial properties of the features. As such, they don't address temporal or seasonal variation. | 2,566 |
jsun1/CS230Project | ['time series'] | ['Deep Learning for Predicting Asset Returns'] | conv_model.py data_extract.py conv_all_model.py softmax_model.py model.py main Net run_model main Net run_model main extract_all_data extract_data main Net run_model main Net reformat_y run_model list criterion backward print size extract_all_data zero_grad Adam range MSELoss Net parameters float step net len print time format run_model view extract_data get print insert tolist shuffle zfill array append max range len get print insert tolist zfill array append max range len extract_all_data append range set_printoptions reformat_y | [Paper](https://github.com/jsun1/CS221Project/files/4341533/CS230_Final_Report.pdf) # Generating Probability Distributions for Future Stock Prices Our goal is to build a deep learning model that reads historical data and outputs detailed probability distributions of future stock prices, rather than simply the predicted price. The input to our algorithm is of the shape (n, f, d), where n is the number of stocks, f is the number of features per stock per day, and d is the number of days of history. The inputs progress through convolutional layers followed by fully-connected layers and a softmax output. The resulting model predicts the future percent change in price on the next day for each stock, and assign probabilities to each range of percentage change (the boundaries between each range being -4%, -3%,-2%,- 1%,0%,+1%,+2%,+3%, and +4%). ## Dataset & features The daily stock price dataset from the Hong Kong exchange was gathered from [Quandl](https://www.quandl.com/data/HKEX-Hong-Kong-Exchange). This time-series data is discretized per day, and preprocessing included cleaning the data to fill in missing values with the previous day’s value. Below is an example of the values for one stock indicator: Date | Price| Bid| Ask| High| Low |Previous Close | Share Volume | Turnover --- | --- | --- | --- | --- | --- | --- | ---|--- 2020-01-02 |1.840 |1.830| 1.850 |1.850| 1.830| 1.830| 2246.0 | 4155.0 2020-01-03| 1.830| 1.830 |1.850|1.840 |1.810| 1.840| 26.0 | 48.0 2020-01-04 |1.900| 1.900| 1.910| 1.920| 1.820| 1.830 | 5395.0 | 10350.0 | 2,567 |
jtiger958/style-transfer-pytorch | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | model/transfer_net.py utils/utils.py dataloader/dataloader.py make_sample.py model/vgg.py src/test.py src/train.py main.py utils/download_dataset.py config/config.py main get_config Dataset get_loader ResidualNetwork TransferNet VGG16 Tester Trainer download_dataset download_url gram_matrix unzip_tar_file load_image DownloadProgressBar unzip_zip_file join checkpoint_dir sample_batch_size style_image_name sample_dir print batch_size Tester test Trainer data_path get_loader train download_dataset image_size makedirs int random_split DataLoader Dataset len join move print makedirs download_url rmtree listdir unzip_zip_file resize open bmm size transpose view extractall close ZipFile extractall close open print | ## neural-style-transfer-pytorch This repository is a deep-running part of a program that changes the style of a photo. And I refer to the following paper. [A Neural Algorithm of Artistic Style](https://arxiv.org/abs/1508.06576) ## How to train Put images that you want to copy style in directory 'style' ``` style/ vangogh.jpg monet.jpg ``` | 2,568 |
juangamella/abcd | ['active learning'] | ['Active Invariant Causal Prediction: Experiment Selection through Stability'] | new/analysis/check_samples.py new/test.py new/strategies/simulator.py new/strategies/budgeted_experiment_design.py new/strategies/real_data_learning.py new/run_dag_collection_experiments.py new/run_fig3.py new/utils/sys_utils.py new/get_parent_probs.py new/utils/graph_utils.py new/strategies/learn_target_parents.py new/run_gies.py new/config.py new/run_fig5.py new/analysis/rates_helper.py new/run_fig6.py new/make_dataset.py new/utils/test.py new/run_fig2.py new/strategies/random_nodes.py new/utils/performance_metrics.py new/strategies/var_score.py new/strategies/edge_prob.py new/run_experiments.py new/analysis/check_gies.py new/main.py new/analysis/check_folder.py new/logger.py new/run_fig4.py new/strategies/information_gain.py new/strategies/collect_dags.py new/run_genenet.py new/entropy_reduction_budgeted_exp.py get_strategy simulate_ parent_functionals descendant_functionals simulate_ parent_functionals get_mec_functional_k get_strategy get_k_entropy_fxn get_mec_functionals descendant_functionals parent_functionals get_data get_mec_functional_k get_strategy get_k_entropy_fxn get_mec_functionals check_folder get_final_dags get_true_dags get_parent_probs_by_dag get_l1_score get_dag_folders get_rates_data_array count_samples RatesHelper compute_bed_score create_bed_strategy collect_dags create_edge_prob_strategy IterationData create_info_gain_strategy get_mec_functional_k binary_entropy get_k_entropy_fxn create_learn_target_parents _non_isolated_nodes create_random_smart_strategy random_strategy IterationData simulate IterationData SimulationConfig get_component_dag IterationData get_component_dag simulate compute_parents_posterior GenerationConfig SimulationConfig get_orient_mask create_variance_strategy greedy_iv node_iv_var_mat create_var_score_fn var_score_mat _write_data probability_shrinkage entropy_shrinkage dag_posterior bernoulli get_covariance_interventional generate_DAG get_precision_interventional cross_entropy_interventional run_gies_boot _load_dags RAND_RANGE cov2dag plot_roc parent_probs ensure_dir make_data inv_perm prec2adj permute covariance diag simulate get_strategy strategy pop list descendant_functionals arcs print reversible_arcs shuffle copy reverse_arc get_mec_functional_k extend nodes get_k_entropy_fxn append DAG len print SimulationConfig DAG all_dags len genfromtxt mean range list range list range join isdir set append listdir join append loadtxt join from_amat nodes set join dump get_final_dags isdir print zip append listdir enumerate open int list items print nodes set to_dict zip zeros keys enumerate split load join int basename listdir isdir endswith DataArray range filter to_netcdf split zeros sum exists open join _write_data _load_dags save run_gies_boot enumerate copy list n_samples n_batches intervention_set range len add_arc map zip binomial DAG sum chain permutation vstack save _load_dags list IterationData savetxt sum range _write_data strategy enumerate join time T items print inv makedirs n_batches run_gies_boot len append sum array dag_posterior target rmdir exists nodes compute_parents_posterior append sample_interventional sample remove starting_samples var multiply sample BinaryIntervention from_amat append zeros sample_interventional enumerate interventional_cpdag cpdag shape from_amat zeros range enumerate get_orient_mask multiply node_iv_var_mat zeros array len var_score_mat range add choice set join format print system rmtree mkdir TOP_FOLDER items list close extend to_csv len int print Graph barabasi_albert_graph configuration_model add_arc edges ceil DAG watts_strogatz_graph identity copy inv identity copy get_covariance_interventional get_precision_interventional variances nodes pi e sum log len join remove append listdir read_csv values list T inv nodes dot zeros array len items list index zeros enumerate len defaultdict nodes zeros adj2inc roc_curve shape dirname makedirs str list loadtxt vstack append array range copy ldl inv_perm inv permute eye | # ABCD vs A-ICP comparison This repository contains the code to reproduce the results comparing [ABCD](https://arxiv.org/abs/1902.10347) to [A-ICP](https://github.com/juangamella/aicp) in the paper *Active Invariant Causal Prediction: Experiment Selection through Stability*, by Juan L Gamella and Christina Heinze-Deml. The repository is forked from [agrawalraj/active_learning](https://github.com/agrawalraj/active_learning), which contains the original implementation of [ABCD](https://arxiv.org/abs/1902.10347). It contains minor changes to the code to get it to run and retrieve results for the experiments comparing ABCD to A-ICP. Changes to the original code are marked with a comment: `#A-ICP paper: *`. ## Dependencies You will need at least Python 3.6 R 3.6 The original installation procedure didn't work for us, and some python dependencies were missing from the venv. This is what we did to get the code to run. ### Installing the R dependencies: In an R terminal, run | 2,569 |
juangamella/aicp | ['active learning'] | ['Active Invariant Causal Prediction: Experiment Selection through Stability'] | tests/tests_population_icp.py src/run_experiments.py tests/tests_utils.py src/main.py tests/context.py src/evaluation.py src/utils.py src/policy.py tests/tests_evaluation.py tests/tests_icp.py src/icp.py src/population_icp.py process_results run_policy TestCase EvaluationResult gen_cases ExperimentSettings Environments evaluate_policies intervention_targets icp Data t_test test_hypothesis f_test pool confidence_intervals regress Result mse ei regress split_residuals test diff MarkovR Random PopRandom PopMarkov markov_blanket R ER Markov Policy MarkovE intersection MarkovER PopMarkovR E markov_blanket reject_hypothesis population_icp stable_blanket mixture_mse pooled_regression parameter_string combinations eg4 eg5 graph_info all_but matrix_block descendants ancestors allclose eg3 eg6 eg1 sampling_matrix nonzero stable_blanket plot_graph ratios eg2 HelperTests DataTests ResultTests PopulationICPTests UtilsTests seed isinstance graph_info LGANM TestCase choice dag_avg_deg append randint range append range int time print len cpu_count floor ExperimentSettings append Pool range enumerate icp random_state target Environments policy_name to_list off_targets seed max_iter p ones add estimate append accepted next range all_but case set choice population_icp alpha sample vars policy first population int time print dict ratios intervention_targets len list min all_but choice Data remove test_hypothesis isinstance p print regress reduce set array_str colored intersection append range f_test t_test min n_env zeros range split pooled_targets zeros list p ttest cdf var len pooled_targets var ppf list T inv sqrt n diag len fit show arange plot choice scatter len remove list Result isinstance p print markov_blanket reject_hypothesis reduce hstack set array_str colored intersection append range pooled_regression covariance regress list regress p all_but covariance p matrix_block hstack inv len zeros sum mixture_mse enumerate list array map str list replace items flatten shape len atleast_1d range set update remove successors set predecessors from_numpy_matrix union update remove successors descendants set difference predecessors from_numpy_matrix union from_numpy_matrix from_numpy_matrix show list get_edge_attributes arange draw planar_layout dict set_facecolor figure zip from_numpy_matrix len zeros enumerate array array array array array array | # Active Invariant Causal Prediction: Experiment Selection through Stability This repository contains the code to run the experiments and plot the results. This README is not intended to be completely self-explanatory, and should be read alongside the manuscript (https://arxiv.org/abs/2006.05690). ## Installing dependencies You will need at least Python 3.6. All dependencies are specified in [`requirements.txt`](requirements.txt). To create a virtual environment and install them, run: ``` virtualenv --no-site-packages venv . venv/bin/activate pip install -r requirements.txt ``` To run the notebooks from the virtual environment, create a new local kernel (while the environment is active): | 2,570 |
juanluisrosaramos/CRNN_OCR | ['optical character recognition', 'scene text recognition'] | ['An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition'] | src/config/logging_config.py src/inspect_a_model.py src/eval/__init__.py src/train/crnn_trainer.py src/train.py src/logger/__init__.py src/utils/feature_io.py src/utils/image_utils.py src/config/__init__.py src/utils/text_feature_io.py src/eval/crnn_tester.py src/demo_batch.py src/data_provider/data_set.py src/train/__init__.py src/write_icdar_text_features.py src/config/training_config.py src/crnn_model/__init__.py src/utils/calculate_accuracy_test.py src/utils/text_feature_reader.py src/test.py src/utils/__init__.py src/crnn_model/cnn_basenet.py src/config/gpu_config.py src/data_provider/text_data_provider.py src/utils/calculate_accuracy.py src/logger/log_factory.py src/eval/recursive_crnn_tester.py src/config/config_provider.py src/freeze_model.py src/freeze_graph.py src/crnn_model/crnn_model.py src/demo_freeze.py src/data_provider/lexicon.py src/data_provider/tf_records_builder.py src/data_provider/icdar_tf_records_builder.py src/eval/basic_crnn_tester.py src/config/global_config.py src/data_provider/image_description.py src/utils/text_feature_writer.py src/write_text_features.py src/utils/levenshtein_distance.py src/utils/char_dict_builder.py src/demo.py src/data_provider/__init__.py src/inspect_checkpoint.py src/config/config_test.py src/config/test_config.py src/weights_to_pred_model.py src/data_provider/text_data_set.py src/utils/levenshtein_distance_test.py recognize parse_params load_images chunker recognize parse_params parse_params _parse_input_meta_graph_proto run_main _parse_input_graph_proto freeze_graph_with_def_protos freeze_graph main _has_no_variables _parse_input_saver_proto main print_tensors_in_checkpoint_file parse_numpy_printoption parse_params test_crnn parse_params save_model save_graph parse_params parse_params write_features parse_params write_features ConfigProvider ConfigTest GlobalConfig GpuConfig LoggingConfig TestConfig TrainingConfig CNNBaseModel CRNN DataSet IcdarTfRecordsBuilder ImageDescription Lexicon TextDataProvider TextDataSet TfRecordBuilder BasicCrnnTester CrnnTester RecursiveCrnnTester LogFactory CrnnTrainer get_batch_accuracy get_accuracy calculate_array_mean CrnnTesterTest CharDictBuilder FeatureIO load_and_resize_image load_and_resize_grayscale_image load_and_resize_color_image levenshtein_distance batch_levenshtein_distance LevenhsteinDistanceTest TextFeatureIO TextFeatureReader TextFeatureWriter add_argument ArgumentParser ctc_beam_search_decoder memory_fraction TextFeatureIO is_tf_growth_allowed load_and_resize_image close placeholder Saver ConfigProto get_logger Session CRNN reader ones cast reset_default_graph get_operations print node import_graph_def print GraphDef print MetaGraphDef print _parse_input_meta_graph_proto _parse_input_graph_proto graph_def freeze_graph_with_def_protos _parse_input_saver_proto split input_meta_graph input_binary variable_names_whitelist variable_names_blacklist output_graph V2 input_graph filename_tensor_name input_saver input_checkpoint checkpoint_version initializer_nodes input_saved_model_dir clear_devices V1 restore_op_name print output_node_names freeze_graph saved_model_tags add_argument parse_known_args ArgumentParser register run decode sorted NewCheckpointReader print get_tensor get_variable_to_shape_map get_printoptions set_printoptions type split file_name all_tensor_names all_tensors print_tensors_in_checkpoint_file exit tensor_name join format BasicCrnnTester info RecursiveCrnnTester is_recursive get_logger run write_graph basename convert_variables_to_constants dirname as_graph_def ctc_beam_search_decoder values to_int32 reshape concat indices placeholder Saver Session CRNN join print IcdarTfRecordsBuilder process makedirs TfRecordBuilder Lexicon Formatter append get_accuracy enumerate range len resize imread IMREAD_COLOR equalizeHist IMREAD_GRAYSCALE resize zeros imread append levenshtein_distance enumerate min range len | ## Running the prediction program Download the code from [https://github.com/juanluisrosaramos/CRNN_OCR.git](https://github.com/juanluisrosaramos/CRNN_OCR.git) There is an available Docker image (with a model) [here]( gcr.io/juanluis-personal/crnn_ocr:cpu ) where we can avoid the steps of download the code and build the image. The trained model is available [here](https://drive.google.com/open?id=1L9Vo_idQrYtqMT5CAb0kGKsWB53pIjIh) ### Using Dockers In the code folder we have a Dockerfile for building an image. The image base is an image with openCV stored in hub.docker.com (hubertlegec/opencv-python:1.0) and then runs the requirements.txt where the tensorflow library for CPU is required. In this case we use Tensorflow version 1.12 There is an already builded image for CPU available with: docker pull gcr.io/juanluis-personal/crnn_ocr:cpu #### CPU #### 1 Building the image for CPU | 2,571 |
juglab/DecoNoising | ['denoising'] | ['Improving Blind Spot Denoising for Microscopy'] | deconoising/prediction.py script_training.py deconoising/utils.py unet/model.py deconoising/training.py artificial_psf tiledPredict_reflect tiledPredict_pad predict tiledPredict trainingPred getStratifiedCoords2D randomCropFRI lossFunction randomCrop trainNetwork denormalize imgToTensor PSNR normalize printNow getDevice upconv2x2 conv1x1 UNet UpConv conv3x3 DownConv astype float32 zeros tensor sum imgToTensor permute zeros to numpy net reshape tiledPredict_reflect astype float32 from_numpy pad tiledPredict_pad cpu numpy zeros pad predict shape zeros min predict shape int astype ceil randint range append randint shuffle randomCrop getStratifiedCoords2D ones min copy choice shape flip array randint rot90 max zeros randomCropFRI imgToTensor permute zeros to net range reshape conv2d pad sqrt float sum zero_grad ReduceLROnPlateau save str std Adam append to range trainingPred concatenate size mean sqrt item printNow int join lossFunction backward parameters train step array print flush to_tensor float32 astype mean print device | # DecoNoising: Improving Blind Spot Denoising for Microscopy Anna S. Goncharova, Alf Honigmann, Florian Jug, and Alexander Krull</br> Max Planck Institute of Molecular Cell Biology and Genetics (**[MPI-CBG](https://www.mpi-cbg.de/home/)**) <br> Center for Systems Biology (**[CSBD](https://www.csbdresden.de/)**) in Dresden, Germany You can find the paper [here](https://arxiv.org/abs/2008.08414). ### Abstract Many microscopy applications are limited by the total amount of usable light and are consequently challenged by the resulting levels of noise in the acquired images. This problem is often addressed via (supervised) deep learning based denoising. Recently, by making assumptions about the noise statistics, self-supervised methods have emerged. Such methods are trained directly on the images that are to be denoised and do not require additional paired training data. | 2,572 |
juglab/DenoiSeg | ['few shot learning', 'denoising'] | ['DenoiSeg: Joint Denoising and Segmentation'] | denoiseg/internals/DenoiSeg_DataWrapper.py scripts/reproducibility/figures/plot_utils.py denoiseg/utils/misc_utils.py scripts/reproducibility/cluster_execution/noise2seg.py denoiseg/utils/seg_utils.py setup.py scripts/reproducibility/cluster_execution/compute_validationscores.py denoiseg/utils/compute_precision_threshold.py scripts/reproducibility/cluster_execution/run_noise2seg.py denoiseg/internals/losses.py denoiseg/__init__.py scripts/reproducibility/cluster_execution/validation_evaluation.py denoiseg/version.py denoiseg/models/denoiseg_config.py denoiseg/models/denoiseg_standard.py denoiseg/models/__init__.py DenoiSeg_DataWrapper denoiseg_denoise_loss loss_seg denoiseg_seg_loss loss_denoiseg DenoiSegConfig DenoiSeg measure_seg measure_precision matching_iou isnotebook intersection_over_union pixel_sharing_bipartite compute_labels matching_overlap combine_train_test_data shuffle_train_data create_patches augment_data denormalize zero_out_train_data onehot_encoding convert_to_oneHot normalize add_boundary_label create_slurm_script data_path TrainFracValidator start_score_evaluation_on_validation_experiment main create_configs main ValExpName create_slurm_script data_path TrainFracValidator main start_n2s_experiment create_configs main read_Noise2Seg_bestAlpha_results cm2inch read_Noise2Seg_results fraction_to_abs get_measure read_voidseg_results constant denoiseg_denoise_loss denoiseg_seg_loss constant n2v_loss zeros size range sum intersection_over_union sum astype where __name__ label exp extract_patches_2d range create_patches concatenate seed permutation print copy concatenate astype onehot_encoding int32 zeros add_boundary_label range astype find_boundaries zeros range int round glob join str join prompt start_score_evaluation_on_validation_experiment range create_configs join format cp create_slurm_script system copytree makedirs convert_to_oneHot ArgumentParser max predict_label_masks DenoiSegConfig parse_args imsave int16 astype zero_out_train_data mkdir vars load int augment_data print shuffle_train_data add_argument min float32 DenoiSeg zfill optimize_thresholds train len start_n2s_experiment join format cp create_slurm_script system copytree makedirs append get_measure array append str get_measure array isinstance |  # DenoiSeg: Joint Denoising and Segmentation Tim-Oliver Buchholz<sup>\*,1,2</sup>, Mangal Prakash<sup>\*,1,2,</sup>, Alexander Krull<sup>1,2,3</sup>, and Florian Jug<sup>1,2,^</sup> <sup>1</sup> Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany <br /> <sup>2</sup> Center for Systems Biology, Dresden, Germany <br /> <sup>3</sup> Max Planck Institute for Physics of Complex Systems, Dresden, Germany <br /> <sup>^</sup> <code>[email protected]</code> <br /> <sup>*</sup> Equal contribution (alphabetical order). Microscopy image analysis often requires the segmentation of objects, | 2,573 |
juglab/EmbedSeg | ['instance segmentation', 'semantic segmentation', 'autonomous driving'] | ['Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth', 'Embedding-based Instance Segmentation in Microscopy'] | EmbedSeg/utils/preprocess_data.py EmbedSeg/datasets/TwoDimensionalDataset.py EmbedSeg/criterions/lovasz_losses.py EmbedSeg/utils/glasbey.py EmbedSeg/models/__init__.py EmbedSeg/utils/view_palette.py EmbedSeg/criterions/my_loss.py EmbedSeg/utils/create_dicts.py EmbedSeg/utils/generate_crops.py EmbedSeg/utils/utils2.py EmbedSeg/datasets/ThreeDimensionalDataset.py EmbedSeg/models/BranchedERFNet_3d.py EmbedSeg/models/erfnet.py EmbedSeg/criterions/__init__.py EmbedSeg/train.py EmbedSeg/utils/transforms.py EmbedSeg/utils/test_time_augmentation.py EmbedSeg/datasets/__init__.py EmbedSeg/models/BranchedERFNet.py EmbedSeg/criterions/my_loss_3d.py EmbedSeg/utils/utils.py EmbedSeg/models/erfnet_3d.py EmbedSeg/test.py setup.py EmbedSeg/_tests/test_center_crops.py EmbedSeg/utils/visualize.py begin_evaluating test_3d test val val_3d val_vanilla_3d train_vanilla_3d val_vanilla train_vanilla save_checkpoint begin_training train invert_one_hot train_3d lovasz_grad flatten_binary_scores iou binary_xloss xloss lovasz_hinge_flat StableBCELoss lovasz_hinge lovasz_softmax_flat mean flatten_probas lovasz_softmax iou_binary calculate_iou SpatialEmbLoss SpatialEmbLoss_3d calculate_iou get_loss ThreeDimensionalDataset TwoDimensionalDataset get_dataset BranchedERFNet BranchedERFNet_3d Decoder DownsamplerBlock Encoder Net UpsamplerBlock non_bottleneck_1d Decoder DownsamplerBlock Encoder Net UpsamplerBlock non_bottleneck_1d get_model create_loss_dict create_test_configs_dict create_dataset_dict create_model_dict create_configs sparsify _fill_label_holes generate_center_image fill_label_holes pairwise_python process_one_hot generate_center_image_3d normalize process normalize_mi_ma process_3d Glasbey get_data_properties calculate_max_eval_image_size split_train_test calculate_foreground_weight calculate_avg_background_intensity extract_data make_dirs split_train_crops calculate_object_size split_train_val to_cuda to_cuda_3d apply_tta_2d process_flips apply_tta_3d to_numpy ToTensorFromNumpy get_transform RandomRotationsAndFlips RandomRotationsAndFlips_3d prepare_embedding_for_train_image add_samples Visualizer degrid Cluster AverageMeter Logger prepare_embedding_for_test_image Cluster_3d matching_dataset relabel_sequential _raise accuracy _check_label_array precision label_overlap intersection_over_union is_array_of_integers f1 recall _label_overlap matching_dataset_lazy obtain_AP_one_hot matching palette_to_image visualize_many_crops create_color_map visualize test_generate_center_image_1 test_generate_center_image_2 load get_model test_3d get_dataset test DataLoader load_state_dict device to exists eval Cluster eval Cluster_3d update format criterion model print param_groups squeeze AverageMeter zero_grad backward tqdm mean item step enumerate update format criterion model print param_groups squeeze AverageMeter zero_grad backward tqdm mean item train step enumerate update format criterion model print param_groups squeeze AverageMeter zero_grad backward tqdm mean item train step enumerate update format criterion model print param_groups squeeze AverageMeter zero_grad backward tqdm mean item train step enumerate eval eval eval eval zeros range where join str print copyfile save LambdaLR val_vanilla_3d train_vanilla DataLoader save_checkpoint Logger device ion max val_3d Adam get_dataset train_vanilla_3d add load_state_dict to train_3d range val format get_loss plot Cluster val_vanilla train ioff load init_output switch_backend Visualizer print parameters get_model step Cluster_3d makedirs cumsum float sum len mean zip append float sum list map zip append float sum range mean lovasz_hinge_flat data lovasz_grad relu Variable sort dot float view Variable float flatten_binary_scores mean lovasz_softmax_flat data lovasz_grad Variable sort size dot append float abs range size view filterfalse isnan iter next enumerate sum item BranchedERFNet_3d BranchedERFNet print join get_transform format dict format print print format print format dict format print binary_fill_holes unique zeros_like set find_objects enumerate zeros_like shrink percentile dtype astype clip evaluate sqrt empty range transpose argmin where shape pairwise_python zeros sum enumerate transpose argmin where shape pairwise_python zeros sum enumerate append range uint16 where fill_label_holes clip shape dirname normalize imread imsave format astype unique enumerate join int print generate_center_image array makedirs uint16 where fill_label_holes clip shape dirname normalize imread imsave format astype unique enumerate join int print generate_center_image_3d array makedirs sparsify arange uint16 where fill_label_holes clip shape dirname normalize imread imsave format astype enumerate join int generate_center_image array makedirs join format urlretrieve move print exists makedirs join format print dirname makedirs seed join sorted int arange format move glob print makedirs shuffle dirname len seed join sorted int arange format glob print shuffle copy make_dirs len seed join sorted int arange format move glob print makedirs shuffle len join list format print where tqdm mean append imread range len join list format std print min where tqdm mean unique append imread max range len join list format clip print maximum tqdm append imread max range len join list format print tqdm average mean append imread range len calculate_foreground_weight calculate_max_eval_image_size calculate_avg_background_intensity calculate_object_size to_cuda zeros_like model concatenate ascontiguousarray process_flips mean numpy to_numpy rot90 flip zeros_like model to_cuda_3d ascontiguousarray numpy to_numpy rot90 flip append append degrid range exp add_samples view contiguous size degrid expand mean randperm sqrt eq color_palette cpu cat exp add_samples byte view contiguous size degrid expand mean randperm sqrt eq unique color_palette cpu cat format ValueError int arange all concatenate astype unique min_scalar_type zeros max len _check_label_array zeros ravel range len _check_label_array sum update int list items setdefault tuple n_true set isscalar zip bool enumerate relabel_sequential min _check_label_array shape label_overlap zeros sum arange range load ndarray isinstance tuple new range enumerate hstack generate_palette Glasbey where show subplot xlabel text axis tight_layout imshow figure relabel_sequential set_yticklabels add_subplot GridSpec set_visible invert_one_hot show sorted imshow imread glob tight_layout enumerate join set_yticks set_ylabel figure randint len zeros generate_center_image unique where zeros generate_center_image unique where | <p align="center"> <img src="https://user-images.githubusercontent.com/34229641/117163211-bb727300-adc3-11eb-8fe4-ebd7d0e5fceb.png" width=300 /> </p> <h2 align="center">Embedding-based Instance Segmentation in Microscopy</h2> ## Table of Contents - **[Introduction](#introduction)** - **[Dependencies](#dependencies)** - **[Getting Started](#getting-started)** - **[Datasets](#datasets)** - **[Training & Inference on your data](#training-and-inference-on-your-data)** | 2,574 |
juglab/VoidSeg | ['denoising', 'instance segmentation', 'semantic segmentation'] | ['Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells', 'Leveraging Self-supervised Denoising for Image Segmentation'] | voidseg/utils/compute_precision_threshold.py voidseg/version.py voidseg/utils/seg_utils.py setup.py voidseg/models/seg_config.py voidseg/utils/misc_utils.py voidseg/models/__init__.py voidseg/internals/segmentation_loss.py voidseg/models/seg_standard.py voidseg/__init__.py loss_seg SegConfig Seg compute_threshold matching_iou isnotebook precision intersection_over_union pixel_sharing_bipartite combine_train_test_data shuffle_train_data create_patches augment_data fractionate_train_data denormalize onehot_encoding convert_to_oneHot normalize add_boundary_label constant zeros size range sum intersection_over_union sum matching_iou set unique pixel_sharing_bipartite len __name__ exp print progress_bar isnotebook precision linspace append label range predict_instances predict extract_patches_2d range create_patches concatenate seed permutation print copy concatenate astype onehot_encoding int32 zeros add_boundary_label range astype find_boundaries zeros range int round | # VoidSeg: Leveraging Self-Supervised Denoising for Image Segmentation Mangal Prakash, Tim-Oliver Buchholz, Manan Lalit, Pavel Tomancak, Florian Jug<sup>1</sup>, Alexander Krull<sup>1</sup> <sup>1</sup>Joint supervision   Deep learning (DL) has arguably emerged as the method of choice for the detection and segmentation of biological structures in microscopy images. However, DL typically needs copious amounts of annotated training data that is for biomedical projects typically not available and excessively expensive to generate. Additionally, tasks become | 2,575 |
juhongm999/dhpf | ['semantic correspondence'] | ['Learning to Compose Hypercolumns for Visual Correspondence'] | common/utils.py data/spair.py model/dhpf.py model/base/resnet.py train.py common/evaluation.py model/base/correlation.py model/rhm.py model/gating.py data/caltech.py common/supervision.py data/pfpascal.py model/base/geometry.py common/logger.py test.py data/pfwillow.py model/objective.py data/dataset.py model/base/norm.py data/download.py test train Evaluator AverageMeter Logger WeakSupStrategy StrongSupStrategy SupervisionStrategy mean fix_randseed where CaltechDataset CorrespondenceDataset find_knn save_response_content get_confirm_token load_dataset download_dataset download_from_google read_mat PFPascalDataset PFWillowDataset SPairDataset DynamicHPF GumbelFeatureSelection Objective HoughMatching Correlation Geometry Norm conv1x1 resnet50 Bottleneck conv3x3 Backbone resnet101 update visualize_selection transfer_kps evaluate model sel_buffer write_result AverageMeter len write_process benchmark enumerate detach model zero_grad benchmark compute_loss detach update write_process mean item loss_buffer enumerate transfer_kps get_image_pair evaluate backward write_result AverageMeter step get_correlation len seed manual_seed_all manual_seed squeeze nonzero t size repeat min get get join remove basename save_response_content print extractall get_confirm_token close rename rmdir Session open items list startswith join mkdir download_from_google loadmat clone load_url Backbone load_state_dict cat clone load_url Backbone load_state_dict cat | [](https://paperswithcode.com/sota/semantic-correspondence-on-spair-71k?p=learning-to-compose-hypercolumns-for-visual) [](https://paperswithcode.com/sota/semantic-correspondence-on-pf-pascal?p=learning-to-compose-hypercolumns-for-visual) [](https://paperswithcode.com/sota/semantic-correspondence-on-pf-willow?p=learning-to-compose-hypercolumns-for-visual) # Learning to Compose Hypercolumns for Visual Correspondence This is the implementation of the paper "Learning to Compose Hypercolumns for Visual Correspondence" by J. Min, J. Lee, J. Ponce and M. Cho. Implemented on Python 3.7 and PyTorch 1.0.1.  For more information, check out project [[website](http://cvlab.postech.ac.kr/research/DHPF/)] and the paper on [[arXiv](https://arxiv.org/abs/2007.10587)]. ## Requirements - Python 3.7 - PyTorch 1.0.1 | 2,576 |
juhongm999/hpf | ['semantic correspondence'] | ['Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features'] | model/evaluation.py beamsearch.py data/spair.py model/rhm.py model/util.py data/pfwillow.py data/dataset.py data/caltech.py evaluate.py data/download.py model/hpflow.py model/geometry.py data/pfpascal.py find_topk log_selected beamsearch_hp parse_layers log_evaluation run CaltechDataset CorrespondenceDataset UnNormalize Normalize save_response_content get_confirm_token load_dataset download_dataset download_from_google read_mat PFPascalDataset PFWillowDataset SPairDataset pts2mask Evaluator correct_kps intersection_over_union pts2ptstr ptstr2mask label_transfer_accuracy center prune_margin receptive_fields ImageTPS predict_kps PointTPS neighbours gaussian2d HyperpixelFlow appearance_similarity rhm build_hspace hspace_bin_ids visualize_prediction where log_args intersect1d parse_hyperpixel init_logger resize sort info info find_topk time sorted info log_selected HyperpixelFlow device DataLoader abspath load_dataset parse_layers log_evaluation download_dataset range append run DataLoader abspath resize device log_result visualize_prediction log_args init_logger load_dataset download_dataset __format__ Evaluator predict_kps mkdir enumerate join evaluate HyperpixelFlow parse_hyperpixel cpu len get get join remove basename save_response_content print extractall get_confirm_token close rename rmdir Session open items list startswith join mkdir download_from_google loadmat pow float sum le str list numpy polygon zeros float fromstring pts2mask float sum item zeros repeat view intersect1d where zeros_like float t index_select repeat nonzero neighbours sum max len le transpose t repeat ge sum len exp LongTensor pow float sum clamp matmul t pow unsqueeze center repeat device to long len sqrt int appearance_similarity view build_hspace add new_zeros expand_as hspace_bin_ids sum view_as setFormatter basicConfig addHandler StreamHandler Formatter setLevel INFO __dict__ info float max interpolate squeeze nonzero len cat img_tps new ImageTPS paste save cpu unnorm to_pil_image | [](https://paperswithcode.com/sota/semantic-correspondence-on-spair-71k?p=hyperpixel-flow-semantic-correspondence-with) [](https://paperswithcode.com/sota/semantic-correspondence-on-pf-pascal?p=hyperpixel-flow-semantic-correspondence-with) [](https://paperswithcode.com/sota/semantic-correspondence-on-pf-willow?p=hyperpixel-flow-semantic-correspondence-with) ## Hyperpixel Flow: <br/> Semantic Correspondence with Multi-layer Neural Features This is the implementation of the paper "Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features" by J. Min, J. Lee, J. Ponce and M. Cho. Implemented on Python 3.6 and Pytorch 1.0.1.  For more information, check out project [[website](http://cvlab.postech.ac.kr/research/HPF/)] and the paper on [[arXiv](http://arxiv.org/abs/1908.06537)]. ### Conda environment settings conda create -n hpf python=3.6 | 2,577 |
jukworks/swat-seq2seq | ['time series', 'anomaly detection'] | ['Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural Networks'] | db.py train.py network.py judge.py extract_anomaly_probs.py first_look.py measure_extractor.py parser.py test_dataset.py evaluation/Evaluation.py conf.py validate.py swat_dataset.py model.py index_in_process SrcType srcs_in_process DataType datetime_to_nanosec swat_time_to_nanosec InfluxDB main dig main Smooth main main AttentionDecoder Encoder Network Normalizer main attack_window_p data_generator SWaTDataset test_parsed_dataset TimeConvertor read_period_file read_timeseries_file Evaluation Single_Period main Evaluation_SWaT_Process str list print strptime DATETIME_BASIC_TIME_FORMAT INFLUX_RETURN_TIME_FORMAT timedelta keys range datetime print dig clear int read format ConfigParser Smooth add_argument check write ArgumentParser append parse_args process push datetime close open N_HIDDEN_CELLS Encoder AttentionDecoder Normalizer N_PROCESS csv_file data_generator out_file next range format N_PROCESS SWaTDataset DataLoader range enumerate readlines close open append split int str readlines close open append split getopt Evaluation_SWaT_Process exit read_period_file detected_attacks readlines read_timeseries_file float set_prediction extend_ground_truth set_ground_truth Evaluation split false_alarm len | # Anomaly Detection for SWaT Dataset using Sequence-to-Sequence Neural Networks This repo includes source codes and pre-trained models for the paper, "Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural Networks." (The paper will be public soon.) ## Requirements - Python 3 - PyTorch - InfluxDB (not mandatory, the codes give the output to InfluxDB) ## Workflow 1. parser.py for normalization 2. train.py for training models 3. validate.py for calculating distances | 2,578 |
julian-risch/KONVENS2019_and_LREC2020 | ['text classification'] | ['Bagging BERT Models for Robust Aggression Identification'] | test/test_lm_finetuning.py farm/eval.py farm/data_handler/processor.py farm/utils.py test/test_doc_regression.py farm/modeling/tokenization.py farm/data_handler/dataloader.py farm/infer.py docs/conf.py examples/embeddings_extraction.py farm/__init__.py farm/data_handler/data_silo.py farm/metrics.py farm/data_handler/samples.py test/create_testdata.py examples/lm_finetuning.py farm/data_handler/utils.py examples/question_answering.py farm/data_handler/input_features.py farm/modeling/language_model.py examples/doc_regression.py farm/modeling/optimization.py farm/modeling/adaptive_model.py farm/visual/ascii/text.py farm/experiment.py test/test_question_answering.py farm/convert_tf_checkpoint_to_pytorch.py farm/visual/ascii/images.py test/test_doc_classification.py farm/file_utils.py examples/doc_classification.py farm/modeling/prediction_head.py farm/train.py setup.py farm/data_handler/dataset.py examples/ner.py farm/inference_rest_api.py run_all_experiments.py test/test_ner.py main setup skip convert_tf_checkpoint_to_pytorch Evaluator validate_args save_model run_experiment load_model load_experiments get_adaptive_model cached_path s3_etag http_get s3_request s3_get read_set_from_file get_from_cache read_config filename_to_url url_to_filename split_s3_path unnestConfig get_file_extension FasttextInferencer Inferencer resp_json InferenceEndpoint NumpyEncoder ModelListEndpoint simple_accuracy squad squad_f1 squad_EM pearson_and_spearman f1_macro acc_and_f1 compute_metrics WrappedDataParallel WrappedDDP Trainer BaseMLLogger convert_iob_to_simple_tags MLFlowLogger log_ascii_workers initialize_device_settings flatten_list to_numpy format_log set_all_seeds TensorBoardLogger NamedDataLoader covert_dataset_to_dataloader convert_features_to_dataset DataSilo _SQUAD_check_is_max_context sample_to_features_squad samples_to_features_bert_lm samples_to_features_ner _SQUAD_improve_answer_span sample_to_features_text SquadProcessor NERProcessor Processor RegressionProcessor TextClassificationProcessor BertStyleLMProcessor InferenceProcessor Squad_cleartext create_samples_squad create_samples_sentence_pairs create_sample_ner Sample SampleBasket create_sample_one_label_one_text _download_extract_downstream_data is_json expand_labels _get_random_sentence get_sentence_pair read_ner_file _conll03get read_tsv read_squad_file pad mask_random_words add_cls_sep truncate_seq_pair read_docs_from_txt AdaptiveModel Bert LanguageModel _LRSchedule initialize_optimizer BertAdam WarmupCosineWithWarmupRestartsSchedule WarmupCosineSchedule WarmupCosineWithHardRestartsSchedule WarmupConstantSchedule WarmupLinearSchedule ConstantLR calculate_optimization_steps BertLMHead FeedForwardBlock NextSentenceHead TextClassificationHead PredictionHead TokenClassificationHead QuestionAnsweringHead RegressionHead BasicTokenizer _words_to_tokens tokenize_with_metadata BertTokenizer _is_punctuation germeval14_subsample squad_subsample germeval18_subsample test_doc_classification test_doc_regression test_lm_finetuning test_ner test_qa init_experiment run_experiment MLFlowLogger info load_experiments connect str format print BertForPreTraining save load_tf_weights_in_bert from_json_file state_dict unnestConfig read_config gradient_accumulation_steps validate_args from_pretrained batch_size model initialize_device_settings Trainer save seed list calculate_class_weights train keys set_all_seeds load int initialize_optimizer balance_classes bool get_adaptive_model DataSilo load create AdaptiveModel append split encode hexdigest sha256 str join str urlparse exists path netloc urlparse startswith resource split_s3_path Object resource split_s3_path download_fileobj get update write close tqdm iter_content len get str s3_etag decode join list url_to_filename filter startswith listdir head makedirs set str list items DotMap isinstance error dict getArgValue items list join isinstance error len copy info append meshgrid range enumerate extend make_response dumps flatten_list list array simple_accuracy f1_score range cat len set intersection append numpy range len squad_f1 squad_EM seed manual_seed_all manual_seed format init_process_group set_device device device_count info bool append replace zip pop deepcopy isinstance join list zip info split info split DataLoader sampler list TensorDataset append tensor keys items list count convert_tokens_to_ids index len items list convert_tokens_to_ids expand_labels pad add_cls_sep len vocab convert_tokens_to_ids mask_random_words append truncate_seq_pair len _DocSpan DotMap _SQUAD_improve_answer_span is_impossible is_training clear_text _SQUAD_check_is_max_context orig_answer_text length convert_tokens_to_ids append range doc_tokens question_text start tokenize enumerate namedtuple len join tokenize range length start min enumerate join Sample tqdm get_sentence_pair tokenize_with_metadata append range len join Sample extend whitespace_tokenize warning split append enumerate len items list _download_extract_downstream_data to_dict info keys read_csv drop _download_extract_downstream_data info split append open _download_extract_downstream_data info join format error realpath _conll03get dirname info makedirs _download_extract_downstream_data pop len append range len append _get_random_sentence randrange range len int min len shuffle random append round max enumerate dumps list log_params BertAdam named_parameters FusedAdam warning WarmupLinearSchedule FP16_Optimizer calculate_optimization_steps int get_world_size info append _words_to_tokens enumerate split append tokenize zip startswith category ord makedirs join makedirs join makedirs from_pretrained load initialize_optimizer Inferencer list run_inference initialize_device_settings Trainer TextClassificationHead pprint AdaptiveModel load_from_dir save zip TextClassificationProcessor train set_all_seeds DataSilo from_pretrained load initialize_optimizer print CRITICAL run_inference initialize_device_settings Trainer set_level AdaptiveModel RegressionProcessor save train set_all_seeds DataSilo RegressionHead from_pretrained load initialize_optimizer CRITICAL extract_vectors initialize_device_settings Trainer set_level AdaptiveModel save BertStyleLMProcessor train set_all_seeds DataSilo from_pretrained load initialize_optimizer NERProcessor CRITICAL run_inference initialize_device_settings Trainer set_level AdaptiveModel TokenClassificationHead save train set_all_seeds DataSilo from_pretrained load initialize_optimizer SquadProcessor QuestionAnsweringHead CRITICAL run_inference initialize_device_settings Trainer set_level AdaptiveModel save train set_all_seeds DataSilo | julian-risch/KONVENS2019_and_LREC2020 | 2,579 |
julian-risch/PatentMatch | ['information retrieval', 'semantic correspondence'] | ['PatentMatch: A Dataset for Matching Patent Claims & Prior Art'] | pipeline/4_createFrameWithParagraphTexts_withoutEquivalents.py Examples/es_helpers.py pipeline/1_createDataFrameClaims_positiveSamples.py pipeline/3_mergeFramesWithExtractedParagraphes.py pipeline/3_optional_opsAPI.py pipeline/5_mergeFramesWithExtractedParagraphes.py pipeline/6_createDatasetsFromCSV.py parse.py pipeline/1_createDataFrameClaims_NegativeSamples.py pipeline/3_optional_parseEquivalentsLog.py pipeline/2_extractCitedPatentText.py pipeline/0_parse.py searchElasticsearch.py pipeline/7_createDatasets.py gendata normalize_claims extract_classifications createIndexCitations createIndexPatentApplications upload main extract_citation_entry extract_citationIDs query_citation_id process_hits create_index lazy_indexing delete_indices parse_arguments reindex gendata normalize_claims extract_classifications createIndexCitations createIndexPatentApplications upload main extract_citation_entry extract_citationIDs main query_citation_id query_exist_claim process_hits main query_citation_id query_exist_claim process_hits desirable text_is_range extract_paragraphs syntax_right query_patent_citation_country_docNumber getAccessToken getEquivalents getPatentCitationIds elasticSearch_process process_csv elasticsearch_request_getDnum elasticsearch_request_getParagraphText getPatentDetails getParagraphText dataframeToDict getParagraphFromText execute list keys values zip findall text fromstring append find split append int range split update get print text fromstring split findall find update get replace print readlines upload open print Elasticsearch create print Elasticsearch create gendata print bulk create_connection get search append add_argument add_parser ArgumentParser parse_args add_subparsers create format exists delete info len format bulk info int print range Elasticsearch set_option search process_hits to_csv scroll DataFrame len word_tokenize list replace set pos_tag b64encode post bytes getAccessToken print text fromstring post iter append get search print getEquivalents unique sleep append read_csv elasticSearch_process list rstrip fromkeys __contains__ append split print search append iterrows replace split int search find int find replace value_counts print to_csv rename nan sample dropna train_test_split read_csv | # PatentMatch: A Dataset for Matching Patent Claims & Prior Art This repository accompanies the paper "PatentMatch: A Dataset for Matching Patent Claims & Prior Art" by Julian Risch, Nicolas Alder, Christoph Hewel and Ralf Krestel. It contains source code describing the data collection process. The dataset can be downloaded [here](https://hpi.de/naumann/s/patentmatch). The paper is published on [arxiv.org](https://arxiv.org/abs/2012.13919). Example code that shows how to use the dataset to train a BERT model is also available on Github [here](https://github.com/julian-risch/PatentMatch-FARM). # Citation If you use our work, please cite our paper [**PatentMatch: A Dataset for Matching Patent Claims & Prior Art**](https://hpi.de/fileadmin/user_upload/fachgebiete/naumann/people/risch/risch2020patentmatch.pdf) as follows: @article{risch2020match, title={{PatentMatch}: A Dataset for Matching Patent Claims with Prior Art}, author={Risch, Julian and Alder, Nicolas and Hewel, Christoph and Krestel, Ralf}, year={2020}, journal = {ArXiv e-prints}, eprint={2012.13919}, | 2,580 |
julian-risch/TRAC-COLING2018 | ['data augmentation'] | ['Aggression Identification Using Deep Learning and Data Augmentation'] | coling-lgbm.py coling-oof.py engineer_feature has_embedding_freq read_data get_coefs nonalpha_freq read_test_data load_embeddings asterix_freq get_subs read_coling_data main has_embedding z_normalize engineer_features uppercase_freq text_to_vector df_to_data read_val_data read_coling_data_de logistic_regression read_data inv_convert data_generator str_normalize count_regexp_occ char_approach pos_tags model5 logistic_scale eval infer_spaces predict_for_test_set tfidf pos_tag_approach rnn logistic_preprocess get_session convert read_test_data read_coling_data text_pos_inv_convert get Series __name__ reshape apply engineer_feature DataFrame fit has_embedding split hstack read_csv where drop concat read_data read_csv asarray print concat hstack fit read_test_data to_csv LGBMClassifier KFold get_subs read_coling_data split cross_val_score argmax engineer_features values drop append best_match range len get GPUOptions lower sub replace join replace astype infer_spaces startswith str_normalize zeros enumerate split zeros values enumerate text_to_vector iterrows text_to_vector sample zeros values read_data read_csv drop Input concatenate asarray print classification_report confusion_matrix f1_score argmax print OneVsRestClassifier LogisticRegression multilabel_ TfidfVectorizer predict_proba transform argmax values fit values print predict fit_generator data_generator df_to_data round compile len RegexpTokenizer tokenize print predict OneVsRestClassifier LogisticRegression TfidfVectorizer transform values fit print OneVsRestClassifier LogisticRegression multilabel_ TfidfVectorizer predict_proba transform argmax values fit df_to_data reshape concatenate values apply transform normalize MaxAbsScaler logistic_preprocess print OneVsRestClassifier LogisticRegression logistic_scale multilabel_ predict_proba argmax values fit | # TRAC-COLING2018 The First Shared Task on Aggression Identification dealt with the classification of the aggression level of user posts at different social media platforms. It was part of the Workshop on Trolling, Aggression and Cyberbullying (TRAC 2018) at the International Conference of Computational Linguistics (COLING 2018). ### Citation If you use our work, please cite our paper [**Aggression Identification Using Deep Learning and Data Augmentation**](https://hpi.de/fileadmin/user_upload/fachgebiete/naumann/people/risch/risch2018aggression.pdf) as follows: @inproceedings{risch2018aggression, author = {Risch, Julian and Krestel, Ralf}, booktitle = {Proceedings of the Workshop on Trolling, Aggression and Cyberbullying (TRAC@COLING)}, title = {Aggression Identification Using Deep Learning and Data Augmentation}, pages = {150--158}, year = {2018} | 2,581 |
julmaxi/summary_lq_analysis | ['text summarization'] | ['How to Evaluate a Summarizer: Study Design and Statistical Analysis for Manual Linguistic Quality Evaluation'] | scripts/summaryanalysis/pseudopower.py scripts/summaryanalysis/power.py scripts/summaryanalysis/annotationutils.py scripts/summaryanalysis/art.py scripts/summaryanalysis/shr.py scripts/summaryanalysis/timereliability.py scripts/summaryanalysis/sample.py scripts/summaryanalysis/plot_power_curvey.py scripts/summaryanalysis/montecarlo.py scripts/summaryanalysis/ordinal.py scripts/summaryanalysis/design_power.py get_annotator_groups paired_approximate_randomization_test test_design_power old regress_on_sample add_grouping_column filter_wrong_rankings run_art_experiment_fixed_budget get_model_type1_error_rates get_art_pvals read_obspower_files run_art_experiment OrdinalModel create_design run_regression run_experiment regress_on_sample take run_regression generate_samples get_annotator_groups compute_annotator_shr get_annotator_groups compute_annotator_shr_raw compute_correlations_from_selection_mask compute_time_reliability_curve compute_grouped_subsample_variance items sorted defaultdict list tuple map set append values abs sum range choice seed run_regression sample from_dict from_tuples print concat to_csv mean test_design_power out_file append range create_design zero_coefficients list defaultdict combinations items Counter copy mean test tqdm unique sample to_numpy range append create_design list glob concat map groups append read_csv list copy coefficients dict set where zip systems join get_annotator_groups set_index from_records append enumerate combinations add_grouping_column mean unique sample to_numpy paired_approximate_randomization_test range append reset_index concat get_art_pvals append create_design append get_art_pvals create_design extend list range len create_design writer items list print close Counter open remove fdopen close to_csv set mkstemp add split float run enumerate combinations list get_annotator_groups mean to_numpy pearsonr combs_generator defaultdict get_annotator_groups tuple append chain isin compute_correlations_from_selection_mask var list defaultdict items compute_annotator_shr_raw mean array list get_annotator_groups sort_index len tqdm mean sample to_numpy pearsonr range append chain combinations list get_annotator_groups shuffle tqdm append to_numpy sum range len | Code for the EACL 2021 paper How to Evaluate a Summarizer: Study Design and Statistical Analysis for Manual Linguistic Quality Evaluation Most experiments can be reproduced by running the accompanying jupyter notebook. For significance analysis run scrips/r/analyse-ordinal.r anonymized_judgements/<data_file> <score for likert or rank for ranking> crossed Power analysis is not included in the steps, as it is computationally expensive. To reproduce one step, run ```bash python -m summaryanalysis.design_power -b <batch count> -d <docs per batch> -a <annotators per doc> <model_file> out.csv ``` | 2,582 |
junjieliu2910/DynamicSaprseTraining | ['network pruning'] | ['Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers'] | mnist/model/lenet.py cifar/models/binarization.py cifar/models/__init__.py mnist_lstm/model/binarization.py mnist_lstm/model/__init__.py cifar/utils.py cifar/models/masked_wideresnet.py mnist_lstm/model/lstm.py mnist/train.py mnist/trainer/trainer.py mnist_lstm/model/lstmcell.py mnist/utils/utils.py mnist_lstm/train.py cifar/train_argument.py cifar/models/masked_vgg.py cifar/trainer/trainer.py cifar/trainer/__init__.py mnist/train_argument.py cifar/models/vgg.py mnist/model/binarization.py cifar/models/wideresnet.py cifar/train.py mnist/utils/__init__.py mnist/trainer/__init__.py mnist/model/__init__.py main print_args parser list2cuda save_model one_hot evaluate tensor2cuda load_model print_layer_keep_ratio numpy2cuda create_logger makedirs BinaryStep MaskedMLP MaskedConv2d masked_vgg MaskedWideResNet BasicBlock NetworkBlock vgg BasicBlock NetworkBlock WideResNet Trainer main print_args parser BinaryStep MaskedMLP MaskedConv2d LeNet_5 LeNet_5_Masked LeNet_300_100 LeNet_300_100_Masked Trainer list2cuda get_tensor_sparsity save_model one_hot evaluate tensor2cuda load_model print_layer_keep_ratio numpy2cuda create_logger makedirs main parser BinaryStep LSTM_model LSTMCell MaskedLSTMCell LSTM WideResNet Trainer create_logger SGD DataLoader MultiStepLR device seed mask masked_vgg to CIFAR100 CrossEntropyLoss model_root manual_seed vgg is_available affix setattr CIFAR10 log_root join MaskedWideResNet print_args parameters train makedirs add_argument ArgumentParser items list format print info array from_numpy is_available cuda scatter_ zeros unsqueeze len astype float32 join getLogger addHandler StreamHandler DEBUG setLevel INFO FileHandler load load_state_dict save state_dict sum format view isinstance abs numel shape weight modules info step MNIST data_root sum abs view repackage_hidden batch_size getLogger model keep_ratio zero_grad basicConfig addHandler len max_epoch Adam range format StreamHandler alpha info item enumerate learning_rate criterion backward named_parameters step init_hidden hidden_size | # Installation Recommend to use python 3.7 and pytorch 1.2 # Mnist ## LeNet300_100 ``` cd mnist/ ``` **Dense baseline** ``` python train.py --model=Lenet300_100 --affix=Lenet300_100_baseline | 2,583 |
junyuchen245/Unsuprevised_Seg_via_CNN | ['medical image segmentation', 'semantic segmentation'] | ['Medical Image Segmentation via Unsupervised Convolutional Neural Network'] | Unsupervised_Seg/nets.py Unsupervised_Seg/img_flow.py Unsupervised_Seg/train_v2.py Unsupervised_Seg/losses.py Unsupervised_Seg/train.py Unsupervised_Seg/fine_tune.py Unsupervised_Seg/img_flow_test.py pre_proc img_read file_update img_flow get_line find_arrow img_read file_update img_flow get_line find_arrow ACWE_label ACWE recurrentNet_fine_tune recurrentNet rcnn_block test_step pre_proc show_figure load_imgs test_step pre_proc min max range load read rstrip split str sorted remove rstrip img_read int concatenate file_update len write close shape get_line find_arrow split listdir range open conv2 conv3 Conv getattr conv1 conv4 rcnn_block Model getattr Input range rcnn_block Model getattr Input range subplot str print reshape axis close mean imshow title savefig figure append randint contour sum std range predict load permutation subplot str axis close imshow title savefig figure randint contour | # Unsupervised Segmentation Using Convolutional Neural Network <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg"></a> [](https://arxiv.org/abs/2001.10155) This is a Tensorflow/Keras implementation of my paper: <a href="https://openreview.net/forum?id=XrbnSCv4LU">Chen, Junyu, et al. "Medical Image Segmentation via Unsupervised Convolutional Neural Network. " Medical Imaging with Deep Learning (MIDL), 2020.</a> :wave: Note that this method does ***binary*** segmentation. Please check out our new approach :point_right: (<a href="https://github.com/junyuchen245/Semi-supervised_FCM_Loss_for_Segmentation">***FCM loss***</a>) for unsupervised and semi-supervised loss functions for multi-class segmentation (PyTorch and TensorFlow).</span> ## Network Architecture:  ## Evaluation and Results: We evaluated four settings of the proposed algorithm on the task of bone segmentation in bone SPECT images: * Mode 1: Unsupervised (self-supervised) training with L_ACWE. | 2,584 |
jusonn/Neural-Process | ['gaussian processes'] | ['Conditional Neural Processes'] | np_mnist.py np_regression.py NP idx_to_y idx_to_x get_context_idx kl_div_gaussian test generate_grid np_loss train NP KLD_gaussian train random_split_c_t log_likelihood visualize sample tensor list range stack unsqueeze linspace index_select index_select expand sum exp kl_div_gaussian binary_cross_entropy format idx_to_y idx_to_x view get_context_idx model backward print zero_grad len dataset expand np_loss randint step enumerate eval Normal arange choice decoder data_to_r reparametrize plot size pause add_subplot draw r_to_z scatter figure fill_between numpy range visualize astype float32 KLD_gaussian from_numpy item to random_split_c_t range | # Neural Process - Conditional Neural Process https://arxiv.org/pdf/1807.01613.pdf - Neural Process https://arxiv.org/pdf/1807.01622.pdf Implementation of Neural Process in pytorch. Works on MNIST image completion and regression task. 1. Regression task   2. MNIST Image Completion. ### requirement | 2,585 |
justincosentino/robust-sparse-networks | ['network pruning'] | ['The Search for Sparse, Robust Neural Networks'] | experiments/path.py experiments/reinit_none.py data/digits_loader.py models/registry.py experiments/__init__.py data/__init__.py experiments/reinit_rand.py models/dense.py attacks/pgd.py analysis/visualize.py data/registry.py experiments/base_experiment.py models/__init__.py experiments/callbacks.py experiments/registry.py attacks/registry.py models/mask.py experiments/reinit_orig.py experiments/pruning.py experiments/utils.py attacks/__init__.py attacks/fgsm.py analysis/__init__.py attacks/utils.py data/loader_utils.py run_analysis.py run_experiments.py experiments/no_pruning.py data/fashion_loader.py __init__.py main init_flags parse_args main init_flags parse_args print_all_keys get_masks_stats plot_test_accuracies prune_iter_iterator load_all_exp_results generate_weight_distributions trial_iterator produce_tables plot_early_stoping get_early_stop_iteration run build_attack build_attack register get_builder get_adversarial_loss get_adversarial_acc_metric load_digits load_fashion load_from_keras _preprocess_labels _preprocess_images register get_loader train_once init_experiment EvalEvery run_trial run get_masks_path get_kernels_path get_log_path prune_by_percent register get_experiment_fn run_trial run run_trial run run_trial run restore_array apply_masks get_masks get_masked_kernels save_array build_model MaskedDense register get_builder join add_argument realpath dirname ArgumentParser join items format table print lower append rmtree init_flags run parse_args exists makedirs run_fn get_experiment_fn plot_test_accuracies load_all_exp_results produce_tables subplots flatten clf DataFrame sorted apply_masks set_title get_masked_kernels savefig format set model_builder distplot enumerate items join get_builder print len sum restore_array join items defaultdict format get_masks_stats prune_iter_iterator trial_iterator read_csv subplots concat logical_not clf DataFrame values sorted logical_and OrderedDict savefig legend append format tight_layout set contains lineplot unique zip keys items remove join print logical_or color_palette get_legend_handles_labels print format isinstance items sorted from_dict to_latex join sort_index insert tuple print len nested_dict astype from_product mean get_loc std append split idxmin subplots clf DataFrame values sorted savefig legend append format tight_layout set lineplot zip keys items remove join print dict get_early_stop_iteration get_legend_handles_labels get_adversarial_loss FastGradientMethod KerasModelWrapper get_adversarial_acc_metric ProjectedGradientDescent load_data reshape astype max ConfigProto set_session Session set_random_seed get_masks data_loader get_masked_kernels attack_builder save_array sum format init_experiment EvalEvery get_masks_path model_builder compile int get_builder evaluate print get_kernels_path get_loader fit train_once range run_trial join format MakeDirs items prune_by_percent_once deepcopy format print prune_by_percent range items MakeDirs ListDirectory layers get_weights layers multiply items | # robust-sparse-networks Empirically evaluating basic robustness properties of the winning "lottery tickets". This repository provides the code required to run experiments from [The Search for Sparse, Robust Neural Networks](https://arxiv.org/abs/1912.02386) by {Justin Cosentino, Federico Zaiter}, {Dan Pei and Jun Zhu}. We presented this work at the Safety and Robustness in Decision Making Workshop at the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), in Vancouver, Canada. If you find this useful, consider citing our paper: ``` @misc{cosentino2019search, title={The Search for Sparse, Robust Neural Networks}, author={Justin Cosentino and Federico Zaiter and Dan Pei and Jun Zhu}, year={2019}, eprint={1912.02386}, | 2,586 |
justinkay/repulsion-loss-detectron2 | ['pedestrian detection'] | ['Repulsion Loss: Detecting Pedestrians in a Crowd'] | repulsion_loss/roi_heads.py repulsion_loss/config.py repulsion_loss/fast_rcnn.py setup.py repulsion_loss/matcher.py add_reploss_config smooth_ln iog RepLossFastRCNNOutputs Top2Matcher RepLossROIHeads CN clamp min where zeros max | # repulsion-loss-detectron2 Implementation of ["Repulsion Loss: Detecting Pedestrians in a Crowd"](https://arxiv.org/abs/1711.07752) for Faster RCNN, on top of [Detectron2](https://github.com/facebookresearch/detectron2). Note: this was built on version [0.1.1](https://github.com/facebookresearch/detectron2/releases/tag/v0.1.1) of Detectron2 and has not been tested on later versions. ## Usage You should not need any additional libraries other than what is needed for Detectron2. Installation with pip: ``` pip install -e . ``` Then, simply: | 2,587 |
jvc2688/KeplerPixelModel | ['causal inference'] | ['Removing systematic errors for exoplanet search via latent causes'] | cpm/code/wavelet_ml.py cpm/code/leastSquareSolver.py cpm/code/magNeighorFit.py linear_least_squares find_mag_neighbor plot_lightcurve get_fake_data get_kfold_train_mask get_transit_boundary get_fit_matrix load_data_r fit_target predictor_filter_bin plot_ratio get_plot_info plot_fit predictor_filter get_pixel_mask get_epoch_mask load_data load_header get_transit_mask fit_target_pixel remove get_pixel_mask get_lightcurve_info variance get_cdpp_ml get_epoch_mask load_cdpp get_fake_data load_data signal_extension get_cdpp T dot isscalar zeros cho_factor int print sort target_pixel_files ceil load_header kic_kepmag array range append zeros shape sum zeros_like inf get_pixel_mask reshape isfinite get_epoch_mask shape interp range get_epoch_mask get_pixel_mask sample zeros list range digitize arange concatenate print min copy mean shape histogram zeros argmax array ones randint range sci_channel float64 roll flatten list ones array dirname append range ktc_kepler_id create_group concatenate sci_data_quarter close mean predictor_filter items print File load_data zeros polyvander std makedirs koi zeros_like koi_period float64 argmin koi_duration koi_time0bk round range append len append range load join remove concatenate print File close fit_epoch flatten mean start dirname append sum range makedirs load sum join remove concatenate print fit_epoch flatten mean start dirname save append argmax range makedirs subplots zeros_like flatten argmax round tick_right set_xlabel shape savefig setp legend sum range plot get_xticklabels set_xlim isfinite copy mean get_ylim get_xlim load koi read suptitle get_yticklabels print text divide star koi_duration subplots_adjust set_ylabel load_data median get_transit_mask std set_ylim argmax load koi zeros_like File close divide copy koi_duration flatten mean round load_data median sum std range get_transit_mask range plot plot concatenate axvline mean fill_between get_transit_boundary range set_ylim list argmax sum load File close divide flatten mean load_data kic_kepmag concatenate append zeros sum range len variance min mean sqrt swt Wavelet append zeros signal_extension range len variance min mean sqrt uwt Wavelet append zeros signal_extension range len | KeplerPixelModel ================ | 2,588 |
jwawon/unity-ml-agent-study | ['unity'] | ['Unity: A General Platform for Intelligent Agents'] | ml-agents/mlagents/envs/communicator_objects/environment_parameters_proto_pb2.py ml-agents/tests/trainers/test_trainer_controller.py ml-agents/mlagents/trainers/buffer.py ml-agents/mlagents/envs/communicator_objects/unity_rl_initialization_input_pb2.py ml-agents/mlagents/envs/communicator_objects/brain_parameters_proto_pb2.py ml-agents/tests/envs/test_envs.py ml-agents/mlagents/envs/communicator_objects/__init__.py ml-agents/mlagents/envs/rpc_communicator.py ml-agents/mlagents/trainers/ppo/__init__.py gym-unity/gym_unity/envs/__init__.py ml-agents/mlagents/envs/communicator_objects/agent_action_proto_pb2.py ml-agents/mlagents/trainers/learn.py gym-unity/gym_unity/envs/unity_env.py ml-agents/mlagents/trainers/bc/trainer.py ml-agents/mlagents/trainers/policy.py ml-agents/mlagents/envs/communicator_objects/unity_rl_initialization_output_pb2.py ml-agents/tests/trainers/test_curriculum.py ml-agents/mlagents/trainers/meta_curriculum.py ml-agents/mlagents/trainers/curriculum.py ml-agents/mlagents/trainers/ppo/models.py ml-agents/mlagents/envs/communicator_objects/space_type_proto_pb2.py ml-agents/mlagents/envs/communicator_objects/unity_output_pb2.py ml-agents/mlagents/envs/communicator_objects/unity_input_pb2.py gym-unity/gym_unity/__init__.py ml-agents/mlagents/trainers/ppo/policy.py ml-agents/mlagents/envs/communicator_objects/engine_configuration_proto_pb2.py ml-agents/mlagents/envs/communicator_objects/brain_type_proto_pb2.py ml-agents/mlagents/envs/socket_communicator.py gym-unity/setup.py ml-agents/mlagents/trainers/trainer_controller.py ml-agents/mlagents/envs/communicator_objects/agent_info_proto_pb2.py ml-agents/mlagents/envs/communicator_objects/unity_to_external_pb2_grpc.py ml-agents/tests/trainers/test_ppo.py ml-agents/mlagents/envs/brain.py ml-agents/mlagents/trainers/bc/policy.py ml-agents/tests/trainers/test_bc.py ml-agents/tests/mock_communicator.py ml-agents/mlagents/envs/communicator_objects/unity_message_pb2.py ml-agents/mlagents/trainers/models.py ml-agents/mlagents/trainers/__init__.py ml-agents/mlagents/envs/communicator_objects/resolution_proto_pb2.py ml-agents/mlagents/envs/communicator_objects/unity_to_external_pb2.py ml-agents/mlagents/envs/communicator_objects/unity_rl_input_pb2.py ml-agents/tests/trainers/test_buffer.py ml-agents/mlagents/trainers/trainer.py ml-agents/mlagents/envs/communicator.py ml-agents/setup.py ml-agents/mlagents/envs/communicator_objects/unity_rl_output_pb2.py ml-agents/mlagents/envs/__init__.py ml-agents/mlagents/trainers/bc/__init__.py gym-unity/tests/test_gym.py ml-agents/mlagents/envs/exception.py ml-agents/mlagents/envs/environment.py ml-agents/mlagents/trainers/bc/models.py ml-agents/mlagents/envs/communicator_objects/command_proto_pb2.py ml-agents/mlagents/trainers/exception.py ml-agents/tests/trainers/test_meta_curriculum.py ml-agents/mlagents/trainers/ppo/trainer.py ml-agents/mlagents/envs/communicator_objects/header_pb2.py UnityGymException UnityEnv test_gym_wrapper test_multi_agent BrainInfo BrainParameters Communicator UnityEnvironment UnityException UnityTimeOutException UnityEnvironmentException UnityActionException RpcCommunicator UnityToExternalServicerImplementation SocketCommunicator UnityToExternalServicer UnityToExternalStub add_UnityToExternalServicer_to_server BufferException Buffer Curriculum CurriculumError MetaCurriculumError TrainerError main run_training MetaCurriculum LearningModel Policy UnityPolicyException UnityTrainerException Trainer TrainerController BehavioralCloningModel BCPolicy BehavioralCloningTrainer PPOModel PPOPolicy PPOTrainer get_gae discount_rewards MockCommunicator test_initialization test_reset test_close test_step test_handles_bad_filename test_dc_bc_model test_cc_bc_model test_visual_cc_bc_model test_bc_policy_evaluate dummy_config test_visual_dc_bc_model assert_array test_buffer location default_reset_parameters test_init_curriculum_bad_curriculum_raises_error test_init_curriculum_happy_path test_increment_lesson test_get_config test_init_meta_curriculum_happy_path test_increment_lessons_with_reward_buff_sizes default_reset_parameters MetaCurriculumTest test_increment_lessons measure_vals reward_buff_sizes test_set_all_curriculums_to_lesson_num test_get_config test_set_lesson_nums test_init_meta_curriculum_bad_curriculum_folder_raises_error more_reset_parameters test_rl_functions test_ppo_model_dc_vector_curio test_ppo_model_dc_vector_rnn test_ppo_model_cc_vector_rnn test_ppo_policy_evaluate test_ppo_model_cc_visual dummy_config test_ppo_model_dc_vector test_ppo_model_dc_visual test_ppo_model_cc_visual_curio test_ppo_model_dc_visual_curio test_ppo_model_cc_vector_curio test_ppo_model_cc_vector test_initialization test_initialize_trainers dummy_bc_config dummy_bad_config dummy_config dummy_start test_load_config sample step MockCommunicator UnityEnv step MockCommunicator UnityEnv method_handlers_generic_handler add_generic_rpc_handlers start_learning int str TrainerController int Process getLogger print start info append randint docopt range list zeros_like size reversed range asarray tolist discount_rewards UnityEnvironment close MockCommunicator UnityEnvironment close MockCommunicator reset str local_done print agents step close reset MockCommunicator UnityEnvironment len UnityEnvironment close MockCommunicator reset_default_graph close reset_default_graph reset_default_graph reset_default_graph reset_default_graph flatten list range len get_batch Buffer assert_array append_update_buffer make_mini_batch append reset_agent array range Curriculum Curriculum Curriculum MetaCurriculum assert_has_calls MetaCurriculumTest increment_lessons assert_called_with MetaCurriculumTest increment_lessons assert_called_with assert_not_called MetaCurriculumTest set_all_curriculums_to_lesson_num MetaCurriculumTest dict update MetaCurriculumTest reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph assert_array_almost_equal array discount_rewards TrainerController | <img src="docs/images/unity-wide.png" align="middle" width="3000"/> <img src="docs/images/image-banner.png" align="middle" width="3000"/> # Unity ML-Agents Toolkit (Beta) **The Unity Machine Learning Agents Toolkit** (ML-Agents) is an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. We also provide implementations (based on TensorFlow) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be | 2,589 |
jwehrmann/lavse | ['word embeddings', 'cross modal retrieval'] | ['Language-Agnostic Visual-Semantic Embeddings'] | lavse/model/similarity/measure.py lavse/model/similarity/similarity.py lavse/model/data_parallel.py lavse/train/evaluation.py lavse/model/similarity/factory.py demo/test.py lavse/data/datasets.py lavse/model/imgenc/precomp.py lavse/train/__init__.py params.py lavse/data/preprocessing.py lavse/model/loss.py lavse/train/train.py lavse/model/layers/dynconv.py lavse/utils/layers.py lavse/model/imgenc/factory.py lavse/model/imgenc/fullencoder.py lavse/train/lr_scheduler.py lavse/data/__init__.py tools/print_result.py demo/test_redis.py run.py lavse/utils/options.py lavse/model/imgenc/common.py lavse/__init__.py lavse/data/adapters.py demo/settings.py lavse/model/txtenc/pooling.py demo/model.py lavse/train/optimizers.py vocab/align_vocab.py demo/fakemodel.py lavse/model/layers/attention.py lavse/utils/logger.py lavse/model/imgenc/__init__.py lavse/model/similarity/__init__.py lavse/data/tokenizer.py lavse/utils/helper.py lavse/utils/__init__.py lavse/model/txtenc/factory.py test.py lavse/model/layers/condbn.py lavse/model/layers/convblocks.py lavse/model/model.py demo/retrieve.py vocab/build_vocab.py lavse/model/txtenc/txtenc.py lavse/data/loaders.py demo/image_retrieval.py lavse/data/collate_fns.py lavse/utils/file_utils.py lavse/model/txtenc/__init__.py lavse/model/__init__.py lavse/model/txtenc/embedding.py lavse/model/layers/__init__.py lavse/model/imgenc/pooling.py demo/app.py get_test_params get_train_params init_distributed_mode setup_for_distributed retrieve tob64 predict_caption makecalc fake_retrieve tob64 retrieve predict_caption classify_process retrieve tob64 predict_caption retrieve predict_caption Coco Flickr repeat_pad split_array no_preprocess default_padding collate_lang_word Collate stack collate_lang_liwe to_numpy liwe_padding Birds ImageDataset CrossLanguageLoader PrecompDataset DummyDataset get_loaders get_dataset_class get_loader prepare_ml_data DataIterator get_transform Tokenizer Vocabulary gather DataParallel DistributedDataParallel cosine_sim ContrastiveLoss_ get_loss cosine_sim_numpy ContrastiveLossWithSoftmax ContrastiveLoss SoftmaxLoss adjust_k LAVSE lavse load_state_dict_with_replace get_img_pooling get_available_imgenc get_image_encoder BaseFeatures inceptionv3 FullImageEncoder InceptionEncoder HierarchicalFeatures Aggregate VSEPPEncoder FullHierImageEncoder ImageEncoder max_pooling mean_pooling VSEImageEncoder load_state_dict_with_replace MultiheadAttentionEncoder SCANImagePrecomp SimplePrecomp ImageProj GRUImgEncoder HierarchicalEncoder MultiHeadAttention SelfAttention ModuleSelfAttention AttentionL Attention CondBatchNorm1d GELU ParallelBlock ConvBlock DynamicConv1dTBC MixedConv1d ProjRNN KernelProjection1d ProjConv2d MixedConv2d Linear get_similarity_object get_available_similarities cosine_sim l2norm l1norm cosine_sim_numpy LogSumExp attn_softmax StackedAttention cosine_similarity ClippedL2Norm Attention Cosine GloveEmb PartialGRUProj PartialGRUs PartialConcat PartialConcatScale get_txt_pooling get_text_encoder get_available_txtenc max_pooling none mean_pooling last_hidden_state_pool LiweGRU RNNEncoder LiweGRUGlove GloveRNNEncoder i2t evaluate t2i predict_loader get_scheduler BanOptimizer get_optimizer freeze Trainer parse_loader_name load_yaml_opts merge_dictionaries read_txt save_json save_yaml_opts get_logdir load_json print_tensor_dict reset_pbar save_checkpoint get_data_path get_tb_writer get_device restore_checkpoint tensor_to_numpy default_initializer print_log_param_stats tb_log_dict AverageMeter log_grad_norm LogCollector create_logger log_param_histograms get_logger merge_dictionaries OptionsDict Options load_and_filter_file loadGloveModel add_argument ArgumentParser vars Dict parse_args add_argument ArgumentParser vars Dict parse_args setup_for_distributed init_process_group print decode BytesIO save getvalue retrieve predict_caption tob64 transform append enumerate print float liwe_padding to numpy transform tob64 append enumerate decode ltrim BATCH_SIZE print retrieve SERVER_SLEEP dumps predict_caption set lrange loads tob64 sleep transform QUEUE append enumerate long enumerate max len max array append split_array long enumerate default_padding list zip list liwe_padding zip long collate_lang_liwe debug DistributedSampler get_dataset_class collate_lang_word Collate DataLoader append dataset_class Tokenizer debug get_dataset_class get_loader zip append extend Compose Normalize load print LAVSE load_state_dict Dict OrderedDict items list fc MethodType inception_v3 mean stack bias xavier_uniform_ weight constant_ div sum sqrt div shape view norm sum model_class stack stack master forward_batch pbar_fn eval zeros dataset numpy enumerate len tensor_to_numpy i2t update print dt similarity eval multimodal_criterion t2i to sum compute_pairwise_similarity median mean shape floor zeros range len median T mean shape floor zeros range len parameters copy add_representer pop Dict split join exit strftime rmtree eval input exists update join hasattr copy save module load items list LAVSE load_state_dict SummaryWriter get_logdir format device update time items sorted join item append print_fn data_path uniform_ xavier_uniform_ weight fill_ eval basicConfig getLogger getLogger items list add_scalar add_histogram data named_parameters append named_parameters item add_scalar format print named_parameters item numpy update items list defaultdict join print load_json split print mean shape open append tensor array zeros split | # Language-Agnostic Visual-Semantic Embeddings This is LAVSE (pronounced læːvɪs), the official source code for the ICCV'19 paper Language-Agnostic Visual-Semantic Embeddings. This repository is inspired by [VSEPP](https://github.com/fartashf/vsepp), [SCAN](https://github.com/kuanghuei/SCAN), and [BootstrapPytorch](https://github.com/Cadene/bootstrap.pytorch). Project page with live demo, another details and figures: https://jwehrmann.github.io/projects.lavse/. ## This code features: * Training and validation of SOTA models in multiple datasets (COCO, Flickr30k, Multi30k, and YJCaptions). * Single language and multi-language support (English, German, Japanese). * Text encoders (GRU, Liwe, Glove embeddings) easy to add new options, for instance BERT. * Image encoders (Precomp, full ConvNet encoders). * Similarity computation (easy to extend, it supports attention layers). * Warm-up hinge-loss function. | 2,590 |
jweyn/DLWP-CS | ['weather forecasting'] | ['Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere'] | DLWP/model/models.py DLWP/model/__init__.py DLWP/plot/__init__.py DLWP/data/cfsr.py DLWP/remap/__init__.py DLWP/custom.py Azure/train_cs.py DLWP/remap/base.py Azure/train_func.py DLWP/plot/util.py DLWP/data/__init__.py DLWP/model/generators.py Azure/train_tf.py DLWP/data/era5.py DLWP/__init__.py DLWP/util.py DLWP/verify.py DLWP/barotropic/__init__.py DLWP/barotropic/pyspharm_transforms.py DLWP/model/preprocessing.py DLWP/barotropic/model.py DLWP/remap/cubesphere.py DLWP/model/models_torch.py DLWP/model/extensions.py DLWP/plot/plot_functions.py unet2 basic unet3 unet complete_model unet4 skip_model basic_model anomaly_correlation_loss anomaly_correlation CubeSphereConv2D PeriodicPadding2D latitude_weighted_loss RowConnected2D SaveWeightsOnEpoch RNNResetStates AdamLearningRateTracker AsymptoteReLU FillPadding3D RunHistory FillPadding2D BatchHistory CubeSpherePadding2D slice_layer TorchReshape EarlyStoppingMin GeneratorEpochEnd PeriodicPadding3D TFPadding3D row_conv2d SGDLearningRateTracker TFPadding2D get_from_class get_methods get_classes save_model load_model day_of_year remove_chars insolation is_channels_last load_torch_model to_chunked_dataset delete_nan_samples to_bool get_object make_keras_picklable save_torch_model train_test_split_ind predictors_to_time_series add_metadata_to_forecast verification_from_series climo_error verification_from_samples daily_climatology forecast_error persistence_error add_metadata_to_forecast_cs daily_climo_time_series monthly_climo_error BarotropicModel BarotropicModelPsi TransformsEngine call_process_month call_fetch CFSReanalysis CFSReforecast _check_exists get_short_name ERA5Reanalysis _check_exists call_fetch SeriesDataGeneratorWithInference TimeSeriesEstimator ArrayDataGeneratorWithInference tf_data_generator SeriesDataGenerator ArrayDataGenerator DataGenerator DLWPNeuralNet DLWPFunctional DLWPTorchNN std_by_batch prepare_data_array mean_by_batch Preprocessor get_constants plot_movie zonal_mean_plot plot_basemap forecast_example_plot history_plot slp_contour radar_colormap blue_red_colormap rotate_vector_r shifted_color_map rgb_colormap _BaseRemap CubeSphereRemap to_chunked_dataset conv_2d_2 conv_2d_7_2 conv_2d_7 conv_2d_8 relu cube_padding_1 pooling_2 conv_2d_6 conv_2d_3 conv_2d_1 up_sampling_2 conv_2d_2 conv_2d_7_2 conv_2d_7 conv_2d_8 relu concatenate cube_padding_1 pooling_2 conv_2d_6 conv_2d_3 conv_2d_1 up_sampling_2 conv_2d_2 conv_2d_7_2 conv_2d_2_2 conv_2d_7 conv_2d_8 relu concatenate cube_padding_1 conv_2d_5 pooling_2 conv_2d_6 conv_2d_6_2 conv_2d_1 conv_2d_5_2 up_sampling_2 conv_2d_1_2 conv_2d_8 conv_2d_6_3 conv_2d_7_3 up_sampling_2 conv_2d_1_3 conv_2d_2 conv_2d_7 cube_padding_1 conv_2d_5 conv_2d_2_3 conv_2d_1 conv_2d_1_2 conv_2d_7_2 conv_2d_2_2 relu concatenate conv_2d_5_2 conv_2d_5_3 pooling_2 conv_2d_6 conv_2d_6_2 conv_2d_8 up_sampling_2 conv_2d_4 conv_2d_4_2 conv_2d_2 conv_2d_7 cube_padding_1 conv_2d_5 conv_2d_3_2 conv_2d_3 conv_2d_1 conv_2d_1_2 conv_2d_7_2 conv_2d_2_2 relu concatenate conv_2d_5_2 pooling_2 conv_2d_6 conv_2d_6_2 append range isinstance model_function conv_2d_2 periodic_padding_3d_2 reshape_2 conv_2d_5 conv_lstm_2d_1 periodic_padding_2 zero_padding_1 conv_2d_6 conv_2d_3 zero_padding_3d_2 conv_2d_1 max_pooling_2 periodic_padding_1 up_sampling_2 conv_2d_4 reshape_1 zero_padding_2 conv_2d_2 periodic_padding_3d_2 reshape_2 concatenate conv_2d_5 conv_lstm_2d_1 periodic_padding_2 zero_padding_1 conv_2d_6 conv_2d_3 zero_padding_3d_2 conv_2d_1 max_pooling_2 periodic_padding_1 up_sampling_2 conv_2d_4 reshape_1 zero_padding_2 concatenate slice normalize_data_format conv2d append range repeat_elements ones cast_to_floatx cos pi shape assign pow sin zeros expand_dims square mean_absolute_error mean sqrt mean_squared_error abs variable Model join getattr __import__ split getattr __import__ get_from_class isinstance dir import_module getattr get_from_class dir import_module getattr callable hasattr copy save update get_methods get_classes multi_gpu_model base_model copy save model load eval list reshape delete set shape nan list remove sort choice append range year Timestamp Series astype float32 pi apply cos shape sqrt sin meshgrid arcsin expand_dims round range len update tuple chunk dims dict shape data_vars hasattr layers tuple cos warn deg2rad nanmean sqrt shape lat append abs range len cos warn deg2rad nanmean sqrt lat append abs range cos deg2rad nanmean sqrt lat append abs range groupby cos deg2rad mean sqrt lat float array values reshape DataArray reshape DataArray astype len reshape DataArray astype len int arange DataArray astype isel nan full values enumerate int predictors arange DataArray astype nan full values enumerate append assign_coords exists _process_month _fetch update deepcopy isinstance from_generator isinstance generate list range mean_by_batch list range append stack open_dataset values predictors isinstance slice insolation isel warn to_bool sel values len pop set_label drawcountries arange set_size_inches drawparallels tight_layout colorbar drawstates title clf getattr figure savefig drawcoastlines plot_function drawmeridians int arange ymin append levels text ymax repr extrema zip m contour clabel clear subplot set_size_inches arange set_title total_seconds drawparallels pcolormesh savefig figure m drawcoastlines drawmeridians enumerate set_size_inches plot xlabel grid ylabel title savefig figure legend subplot set_size_inches arange set_title drawparallels Basemap m pcolormesh drawcoastlines figure savefig lat meshgrid lon contour drawmeridians show fill_betweenx set_size_inches plot xlabel grid ylabel ylim savefig figure legend lat radians exp angle arctan2 cos basemap any real meshgrid abs imag append range from_list append range from_list cmap hstack LinearSegmentedColormap linspace zip append | # DLWP-CS: Deep Learning Weather Prediction #### DLWP-CS is a Python project containing data-processing and model-building tools for predicting the gridded atmosphere using deep convolutional neural networks applied to a cubed sphere. ## Reference If you use this code or find it useful please cite [our publication](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020MS002109) (also on [ArXiv](https://arxiv.org/abs/2003.11927)). ## Getting started For now, DLWP is not a package that can be installed using `pip` or a `setup.py` file, so it works like most research code: download (or clone) and run. #### Required dependencies It is assumed that the following are installed using Anaconda Python 3 (Python 2.7 should be supported, but not recommended). I highly recommend creating a new environment, e.g., `conda create --name dlwp python=3.7 ipython`, and changing to that new environment. - [TensorFlow](https://www.tensorflow.org) >= 2.0 (GPU capable version highly recommended). | 2,591 |
jwyang/graph-rcnn.pytorch | ['graph generation', 'scene graph generation'] | ['Graph R-CNN for Scene Graph Generation'] | lib/scene_parser/rcnn/utils/metric_logger.py lib/scene_parser/rcnn/modeling/relation_heads/auxilary/multi_head_att.py lib/scene_parser/rcnn/structures/bounding_box_pair.py lib/scene_parser/rcnn/modeling/relation_heads/relation_heads.py lib/scene_parser/rcnn/data/datasets/evaluation/voc/voc_eval.py lib/scene_parser/rcnn/modeling/backbone/fbnet.py lib/scene_parser/rcnn/modeling/balanced_positive_negative_pair_sampler.py lib/scene_parser/rcnn/data/datasets/voc.py lib/data/evaluation/sg/sg_eval.py lib/scene_parser/rcnn/layers/batch_norm.py lib/data/factory.py lib/scene_parser/rcnn/modeling/utils.py lib/scene_parser/rcnn/layers/dcn/deform_pool_module.py lib/scene_parser/rcnn/utils/registry.py lib/scene_parser/rcnn/structures/keypoint.py lib/scene_parser/rcnn/modeling/make_layers.py lib/scene_parser/rcnn/utils/collect_env.py lib/scene_parser/rcnn/modeling/backbone/fpn.py lib/scene_parser/rcnn/modeling/rpn/retinanet/inference.py lib/scene_parser/rcnn/modeling/backbone/resnet.py lib/scene_parser/rcnn/modeling/roi_heads/box_head/inference.py lib/scene_parser/rcnn/data/datasets/list_dataset.py lib/data/evaluation/gqa_voc/gqa_voc_eval.py lib/scene_parser/rcnn/layers/smooth_l1_loss.py lib/scene_parser/rcnn/modeling/roi_heads/roi_heads.py lib/scene_parser/rcnn/utils/logger.py lib/utils/box.py lib/scene_parser/rcnn/modeling/rpn/__init__.py lib/config/defaults.py lib/data/voc_eval.py lib/scene_parser/rcnn/modeling/relation_heads/roi_relation_box_feature_extractors.py lib/scene_parser/rcnn/modeling/backbone/fbnet_modeldef.py lib/scene_parser/rcnn/modeling/relation_heads/msdn/msdn.py lib/data/samplers/grouped_batch_sampler.py lib/data/evaluation/gqa_voc/__init__.py lib/scene_parser/rcnn/layers/dcn/deform_conv_module.py lib/scene_parser/rcnn/modeling/rpn/rpn.py lib/scene_parser/rcnn/modeling/registry.py lib/scene_parser/rcnn/layers/_utils.py lib/scene_parser/rcnn/structures/image_list.py lib/scene_parser/rcnn/utils/cv2_util.py lib/scene_parser/rcnn/engine/bbox_aug.py lib/data/evaluation/voc/voc_eval.py lib/utils/miscellaneous.py lib/scene_parser/rcnn/modeling/rpn/loss.py lib/data/transforms/__init__.py lib/scene_parser/rcnn/utils/c2_model_loading.py lib/scene_parser/rcnn/modeling/relation_heads/reldn/reldn.py lib/scene_parser/rcnn/modeling/roi_heads/box_head/roi_box_predictors.py lib/scene_parser/rcnn/layers/sigmoid_focal_loss.py lib/scene_parser/rcnn/layers/__init__.py main.py lib/scene_parser/rcnn/modeling/relation_heads/loss.py lib/scene_parser/rcnn/structures/boxlist_ops.py lib/config/__init__.py lib/scene_parser/rcnn/utils/boxes.py lib/utils/logger.py lib/scene_parser/rcnn/utils/timer.py lib/scene_parser/rcnn/modeling/backbone/backbone.py lib/scene_parser/rcnn/layers/roi_align.py lib/scene_parser/rcnn/config/__init__.py lib/scene_parser/rcnn/modeling/roi_heads/box_head/loss.py lib/scene_parser/rcnn/utils/miscellaneous.py lib/scene_parser/rcnn/modeling/relation_heads/relpn/relationshipness.py lib/scene_parser/rcnn/modeling/rpn/retinanet/loss.py lib/scene_parser/rcnn/structures/segmentation_mask.py lib/scene_parser/rcnn/utils/model_zoo.py lib/scene_parser/rcnn/structures/bounding_box.py lib/scene_parser/rcnn/modeling/relation_heads/roi_relation_predictors.py lib/data/evaluation/__init__.py lib/config/paths_catalog.py lib/scene_parser/rcnn/modeling/relation_heads/grcnn/agcn/agcn.py lib/data/build.py lib/scene_parser/rcnn/modeling/relation_heads/inference.py lib/scene_parser/rcnn/modeling/roi_heads/box_head/roi_box_feature_extractors.py lib/data/samplers/distributed.py lib/scene_parser/rcnn/data/datasets/evaluation/voc/__init__.py lib/scene_parser/rcnn/data/datasets/concat_dataset.py lib/scene_parser/rcnn/modeling/backbone/__init__.py lib/scene_parser/rcnn/modeling/relation_heads/reldn/spatial.py lib/scene_parser/rcnn/modeling/rpn/anchor_generator.py lib/scene_parser/rcnn/modeling/roi_heads/box_head/box_head.py lib/scene_parser/rcnn/engine/__init__.py lib/scene_parser/rcnn/utils/env.py lib/scene_parser/rcnn/layers/dcn/__init__.py lib/data/transforms/transforms.py lib/data/vg_eval.py lib/scene_parser/rcnn/setup.py lib/scene_parser/rcnn/modeling/balanced_positive_negative_sampler.py lib/scene_parser/rcnn/modeling/pair_matcher.py lib/scene_parser/rcnn/modeling/relation_heads/baseline/baseline.py lib/scene_parser/rcnn/layers/roi_pool.py lib/scene_parser/rcnn/modeling/rpn/utils.py lib/scene_parser/rcnn/layers/misc.py lib/data/evaluation/voc/__init__.py lib/scene_parser/rcnn/engine/inference.py lib/scene_parser/rcnn/__init__.py lib/scene_parser/rcnn/utils/checkpoint.py lib/scene_parser/rcnn/modeling/relation_heads/relpn/relpn.py lib/scene_parser/rcnn/modeling/relation_heads/roi_relation_box_predictors.py lib/scene_parser/rcnn/solver/build.py lib/data/evaluation/gqa_coco/__init__.py lib/scene_parser/rcnn/modeling/relation_heads/msdn/msdn_base.py lib/data/samplers/iteration_based_batch_sampler.py lib/scene_parser/rcnn/layers/dcn/deform_conv_func.py lib/scene_parser/rcnn/layers/nms.py lib/scene_parser/rcnn/config/defaults.py lib/scene_parser/rcnn/solver/lr_scheduler.py lib/scene_parser/rcnn/utils/comm.py lib/scene_parser/rcnn/modeling/box_coder.py lib/scene_parser/rcnn/modeling/relation_heads/imp/imp.py lib/scene_parser/rcnn/solver/__init__.py lib/utils/pytorch_misc.py lib/scene_parser/rcnn/modeling/detector/generalized_rcnn.py lib/scene_parser/rcnn/layers/dcn/deform_pool_func.py lib/scene_parser/rcnn/utils/visualize.py lib/scene_parser/rcnn/modeling/relation_heads/relpn/utils.py lib/scene_parser/rcnn/modeling/relation_heads/sparse_targets.py lib/data/evaluation/gqa_coco/gqa_coco_eval.py lib/scene_parser/rcnn/modeling/poolers.py lib/scene_parser/rcnn/data/datasets/evaluation/coco/coco_eval.py lib/scene_parser/rcnn/data/datasets/coco.py lib/data/samplers/__init__.py lib/scene_parser/rcnn/modeling/rpn/retinanet/retinanet.py lib/scene_parser/rcnn/engine/trainer.py lib/scene_parser/rcnn/modeling/backbone/fbnet_builder.py lib/model.py lib/scene_parser/rcnn/utils/imports.py lib/scene_parser/rcnn/modeling/rpn/inference.py lib/scene_parser/parser.py lib/scene_parser/rcnn/data/datasets/evaluation/__init__.py lib/data/evaluation/sg/evaluator.py lib/data/collate_batch.py lib/data/evaluation/coco/__init__.py lib/scene_parser/rcnn/config/paths_catalog.py lib/scene_parser/rcnn/data/datasets/__init__.py lib/data/evaluation/sg/__init__.py lib/data/transforms/build.py lib/scene_parser/rcnn/modeling/matcher.py lib/scene_parser/rcnn/modeling/relation_heads/grcnn/grcnn.py lib/scene_parser/rcnn/modeling/relation_heads/roi_relation_feature_extractors.py lib/scene_parser/rcnn/data/datasets/evaluation/coco/__init__.py lib/data/evaluation/coco/coco_eval.py lib/data/vg_hdf5.py lib/scene_parser/rcnn/modeling/relation_heads/reldn/visual.py lib/scene_parser/rcnn/utils/model_serialization.py main train test build_model SceneGraphGeneration DatasetCatalog ModelCatalog make_data_sampler build_data_loader make_batch_data_sampler _quantize _compute_aspect_ratios BatchCollator BBoxAugCollator build_data_loader vg_eval load_graphs vg_hdf5 parse_rec voc_eval voc_ap evaluate_sg evaluate COCOResults check_expected_results evaluate_predictions_on_coco do_coco_evaluation evaluate_box_proposals prepare_for_coco_detection coco_evaluation check_expected_results do_gqa_coco_evaluation evaluate_box_proposals prepare_for_gqa_coco_detection evaluate_predictions_on_gqa_coco GQACOCOResults gqa_coco_evaluation calc_detection_gqa_voc_ap do_gqa_voc_evaluation calc_detection_gqa_voc_prec_rec eval_detection_gqa_voc gqa_voc_evaluation _triplet evaluate_from_dict BasicSceneGraphEvaluator evaluate_recall _compute_pred_matches _object_recall _triplet iou evaluate do_sg_evaluation _predicate_recall _relation_recall _relation_recall_triplet sg_evaluation calc_detection_voc_ap do_voc_evaluation calc_detection_voc_prec_rec eval_detection_voc voc_evaluation DistributedSampler GroupedBatchSampler IterationBasedBatchSampler build_transforms Compose ToTensor RandomVerticalFlip Resize Normalize RandomHorizontalFlip ColorJitter get_save_dir SceneParser build_scene_parser build_scene_parser_optimizer get_extensions DatasetCatalog ModelCatalog COCODataset _has_only_empty_bbox has_valid_annotation _count_visible_keypoints ConcatDataset ListDataset PascalVOCDataset evaluate COCOResults check_expected_results prepare_for_coco_segmentation evaluate_predictions_on_coco evaluate_box_proposals do_coco_evaluation prepare_for_coco_keypoint prepare_for_coco_detection coco_evaluation calc_detection_voc_ap do_voc_evaluation calc_detection_voc_prec_rec eval_detection_voc voc_evaluation im_detect_bbox_aug im_detect_bbox_scale im_detect_bbox_hflip im_detect_bbox compute_on_dataset inference _accumulate_predictions_from_multiple_gpus do_train reduce_loss_dict FrozenBatchNorm2d _NewEmptyTensorOp Conv2d DFConv2d interpolate BatchNorm2d ConvTranspose2d ROIAlign _ROIAlign _ROIPool ROIPool SigmoidFocalLoss _SigmoidFocalLoss sigmoid_focal_loss_cpu smooth_l1_loss _load_C_extensions DeformConvFunction ModulatedDeformConvFunction ModulatedDeformConv DeformConv ModulatedDeformConvPack DeformRoIPoolingFunction DeformRoIPoolingPack ModulatedDeformRoIPoolingPack DeformRoIPooling BalancedPositiveNegativePairSampler BalancedPositiveNegativeSampler BoxCoder conv_with_kaiming_uniform make_conv3x3 get_group_gn make_fc group_norm Matcher PairMatcher make_pooler LevelMapper Pooler cat build_resnet_fpn_p3p7_backbone build_backbone build_resnet_fpn_backbone build_resnet_backbone add_rpn_head add_roi_head_mask FBNetROIHead _get_rpn_stage FBNetRPNHead FBNetTrunk add_roi_head _get_head_stage _get_trunk_cfg create_builder add_conv_body add_roi_head_keypoints _get_divisible_by ConvBNRelu _expand_block_cfg FBNetBuilder CascadeConv3x3 get_blocks SEModule _add_to_arch IRFBlock Shift expand_stages_cfg expand_stage_cfg ShiftBlock5x5 _py2_round get_num_stages unify_arch_def _get_upsample_op Upsample Identity _block_cfgs_to_list ChannelShuffle add_archs LastLevelMaxPool FPN LastLevelP6P7 StemWithGN ResNetHead _make_stage ResNet BottleneckWithGN Bottleneck StemWithFixedBatchNorm BottleneckWithFixedBatchNorm BaseStem GeneralizedRCNN PostProcessor make_roi_relation_post_processor make_roi_relation_loss_evaluator FastRCNNLossComputation ROIRelationHead build_roi_relation_head make_roi_relation_box_feature_extractor FPNXconv1fcFeatureExtractor FPN2MLPFeatureExtractor ResNet50Conv5ROIFeatureExtractor FPNPredictor make_roi_relation_box_predictor FastRCNNPredictor FPNXconv1fcFeatureExtractor FPN2MLPFeatureExtractor ResNet50Conv5ROIFeatureExtractor make_roi_relation_feature_extractor FPNPredictor make_roi_relation_predictor FastRCNNPredictor _get_tensor_from_boxlist FrequencyBias _get_rel_inds MultiHeadAttention attention build_baseline_model Baseline build_grcnn_model GRCNN _Collection_Unit _Update_Unit _GraphConvolutionLayer_Collect _GraphConvolutionLayer_Update normal_init IMP build_imp_model build_msdn_model MSDN Message_Passing_Unit_v2 Gated_Recurrent_Unit MSDN_BASE Message_Passing_Unit_v1 build_reldn_model RelDN SpatialFeature build_spatial_feature VisualFeature build_visual_feature Relationshipness Relationshipnessv2 make_relation_proposal_network RelPN box_pos_encoder 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 FPNXconv1fcFeatureExtractor FPN2MLPFeatureExtractor ResNet50Conv5ROIFeatureExtractor FPNPredictor make_roi_box_predictor FastRCNNPredictor AnchorGenerator generate_anchors _scale_enum _whctrs make_anchor_generator _ratio_enum make_anchor_generator_retinanet _generate_anchors BufferList _mkanchors make_rpn_postprocessor RPNPostProcessor RPNLossComputation generate_rpn_labels make_rpn_loss_evaluator build_rpn RPNHeadFeatureSingleConv RPNModule RPNHead RPNHeadConvRegressor concat_box_prediction_layers permute_and_flatten make_retinanet_postprocessor RetinaNetPostProcessor make_retinanet_loss_evaluator generate_retinanet_labels RetinaNetLossComputation build_retinanet RetinaNetHead RetinaNetModule make_optimizer make_lr_scheduler WarmupMultiStepLR BoxList BoxPairList cat_boxlist boxlist_iou boxlist_nms remove_small_boxes _cat ImageList to_image_list PersonKeypoints kp_connections _create_flip_indices Keypoints keypoints_to_heat_map SegmentationMask PolygonList PolygonInstance BinaryMaskList clip_xyxy_to_image bbox_transform unique_boxes clip_tiled_boxes bbox_transform_inv boxes_area xywh_to_xyxy clip_boxes_to_image xyxy_to_xywh filter_small_boxes boxes_union _rename_basic_resnet_weights _rename_conv_weights_for_deformable_conv_layers load_resnet_c2_format load_c2_format _rename_weights_for_resnet _load_c2_pickled_weights _rename_fpn_weights SceneParserCheckpointer DetectronCheckpointer Checkpointer collect_env_info get_pil_version synchronize get_world_size reduce_dict all_gather get_rank is_main_process findContours setup_environment setup_custom_environment import_file setup_logger SmoothedValue MetricLogger save_config mkdir save_labels get_timestamp strip_prefix_if_present load_state_dict align_and_update_state_dicts cache_url _register_generic Registry get_time_str Timer compute_colors_for_labels overlay_class_names select_top_predictions overlay_boxes overlay_question_answers bbox_overlaps setup_logger mkdir save_labels save_config transpose_packed_sequence_inds arange Flattener intersect_2d get_ranking enumerate_imsize const_row to_variable pairwise batch_map diagonal_inds right_shift_packed_sequence_inds to_onehot gather_nd argsort_desc optimistic_restore cache np_to_variable random_choose clip_grad_norm update_lr save_net batch_index_iterator de_chunkize load_net print_para accuracy nonintersecting_2d_inds enumerate_by_image unravel_index distributed local_rank build_model distributed local_rank build_model batchsize ArgumentParser str session set_device algorithm get_rank inference parse_args instance merge_from_file format init_process_group save_config synchronize config_file setup_logger test distributed resume mkdir info join add_argument use_freq_prior train local_rank SequentialSampler RandomSampler list sorted copy get_img_info append float range len BatchSampler IterationBasedBatchSampler GroupedBatchSampler _quantize _compute_aspect_ratios format make_data_sampler print get_world_size BatchCollator vg_hdf5 DataLoader make_batch_data_sampler SIZE_DIVISIBILITY build_transforms minimum max argmax eps cumsum float astype maximum voc_ap argsort zip zeros bool sum array range len File astype len stack append zeros numpy array range column_stack int parse findall text append find arange concatenate size maximum sum max range parse_rec cumsum argmax max sum range eps format astype mkdir float enumerate minimum join print sort maximum voc_ap argsort zeros bool array len dict __name__ isinstance dict __name__ isinstance items list format join COCOResults check_expected_results getLogger item info save evaluate_box_proposals prepare_for_coco_detection get_img_info convert tolist extend resize enumerate arange zeros_like resize max boxlist_iou append loadAnns sum range cat getAnnIds mean float enumerate get_img_info reshape sort convert min zeros as_tensor len list evaluate COCOeval summarize set accumulate error format info getLogger items list format join check_expected_results getLogger prepare_for_gqa_coco_keypoint prepare_for_gqa_coco_segmentation evaluate_box_proposals prepare_for_gqa_coco_detection item info save GQACOCOResults get_img_info convert tolist extend resize enumerate accumulate summarize evaluate COCOeval get_img_info format info get_groundtruth eval_detection_gqa_voc resize append enumerate calc_detection_gqa_voc_ap calc_detection_gqa_voc_prec_rec list defaultdict cumsum astype extend copy keys numpy array unique zip append zeros argmax max arange concatenate empty nan sum max range len warning info getLogger sum evaluate_recall intersect_2d ones len astype reduce union1d prod argsort_desc append float argmax max column_stack column_stack _triplet prod _compute_pred_matches column_stack int concatenate intersect_2d reshape any zip append get_img_info format evaluate zip info all_modes get_groundtruth argsort mean array print_stats resize get_field evaluate_scene_graph_entry numpy prod bbox enumerate int _triplet view ones squeeze min ravel cpu numpy _relation_recall append contiguous astype float32 int32 bbox_overlaps range iou astype intersect1d zip enumerate iou astype intersect1d zip append enumerate iou astype zip enumerate iou astype intersect1d zip enumerate minimum maximum info getLogger get_img_info format info get_groundtruth eval_detection_voc resize append enumerate calc_detection_voc_ap calc_detection_voc_prec_rec list defaultdict cumsum astype extend copy keys numpy array unique zip append zeros argmax max arange concatenate empty nan sum max range len warning info getLogger BRIGHTNESS TO_BGR255 VERTICAL_FLIP_PROB_TRAIN CONTRAST MIN_SIZE_TEST HUE Compose MIN_SIZE_TRAIN Normalize MAX_SIZE_TRAIN MAX_SIZE_TEST SATURATION ColorJitter join max MODE CONV_BODY format TRAIN_BATCH_SIZE NAME SESSION BASE_LR LOADER makedirs make_optimizer load SceneParserCheckpointer USE_GT_BOXES get_save_dir DistributedDataParallel load_state_dict make_lr_scheduler state_dict glob join dirname abspath _has_only_empty_bbox PascalVOCDataset COCODataset prepare_for_coco_segmentation prepare_for_coco_keypoint get_img_info decode Masker tolist extend masker expand tqdm resize get_field enumerate convert tolist extend resize get_field enumerate add_preds_t MIN_SIZE_TEST SCALE_H_FLIP filter_results MAX_SIZE_TEST len im_detect_bbox_scale append range cat add_field MAX_SIZE size im_detect_bbox_hflip im_detect_bbox make_roi_box_post_processor enumerate H_FLIP NUM_CLASSES BoxList SCALES mode SIZE_DIVISIBILITY Compose to_image_list model Compose to_image_list SIZE_DIVISIBILITY to im_detect_bbox im_detect_bbox_hflip update tqdm eval device enumerate update list sorted getLogger warning all_gather keys Timer toc join format getLogger synchronize get_time_str total_time get_world_size device _accumulate_predictions_from_multiple_gpus tic dict save info compute_on_dataset dataset len get_world_size getLogger model zero_grad save str MetricLogger to sum update format timedelta info reduce_loss_dict enumerate time error any global_avg train step len _output_size tuple dtype sigmoid unsqueeze device log abs where join glob extend dirname abspath EPSILON DIM_PER_GP NUM_GROUPS group_norm Conv2d bias normal_ kaiming_normal_ ReLU append weight constant_ kaiming_uniform_ bias weight constant_ Linear POOLER_RESOLUTION POOLER_SCALES POOLER_SAMPLING_RATIO Pooler OrderedDict ResNet Sequential BACKBONE_OUT_CHANNELS FPN ResNet Sequential OrderedDict RES2_OUT_CHANNELS BACKBONE_OUT_CHANNELS FPN ResNet Sequential OrderedDict RES2_OUT_CHANNELS BACKBONE_OUT_CHANNELS get format FBNetBuilder SCALE_FACTOR WIDTH_DIVISOR DW_CONV_SKIP_BN unify_arch_def DW_CONV_SKIP_RELU loads ARCH_DEF BN_TYPE info ARCH get_num_stages get list get_blocks range FBNetTrunk Sequential OrderedDict create_builder last_depth get list format warn get_blocks range len create_builder FBNetRPNHead RPNHeadConvRegressor out_channels get get_blocks create_builder create_builder create_builder int Upsample append deepcopy range append expand_stage_cfg append expand_stage_cfg enumerate enumerate update deepcopy _block_cfgs_to_list _add_to_arch max append deepcopy append transformation_module range CLS_AGNOSTIC_BBOX_REG BoxCoder DETECTIONS_PER_IMG ENABLED PostProcessor BBOX_REG_WEIGHTS USE_FPN NMS SCORE_THRESH POSITIVE_FRACTION FG_IOU_THRESHOLD CLS_AGNOSTIC_BBOX_REG PairMatcher BATCH_SIZE_PER_IMAGE BoxCoder BalancedPositiveNegativePairSampler BBOX_REG_WEIGHTS BG_IOU_THRESHOLD FastRCNNLossComputation ones size bbox cuda cat enumerate cumsum squeeze unique dropout transpose matmul sqrt unsqueeze masked_fill softmax normal_ add_ zero_ POSITIVE_FRACTION FG_IOU_THRESHOLD CLS_AGNOSTIC_BBOX_REG PairMatcher BATCH_SIZE_PER_IMAGE BoxCoder RelPN BalancedPositiveNegativePairSampler BBOX_REG_WEIGHTS BG_IOU_THRESHOLD clone RETINANET_ON CombinedROIHeads append CLS_AGNOSTIC_BBOX_REG MIN_DETECTIONS_PER_IMG BoxCoder DETECTIONS_PER_IMG ENABLED PostProcessor BBOX_REG_WEIGHTS USE_FPN NMS SCORE_THRESH POSITIVE_FRACTION FG_IOU_THRESHOLD CLS_AGNOSTIC_BBOX_REG BATCH_SIZE_PER_IMAGE BoxCoder BalancedPositiveNegativeSampler BBOX_REG_WEIGHTS BG_IOU_THRESHOLD Matcher FastRCNNLossComputation AnchorGenerator STRADDLE_THRESH ANCHOR_SIZES ANCHOR_STRIDE USE_FPN ASPECT_RATIOS AnchorGenerator STRADDLE_THRESH OCTAVE ANCHOR_SIZES tuple SCALES_PER_OCTAVE append float ASPECT_RATIOS ANCHOR_STRIDES range 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 FPN_POST_NMS_PER_BATCH POST_NMS_TOP_N_TEST MIN_SIZE PRE_NMS_TOP_N_TRAIN FPN_POST_NMS_TOP_N_TEST PRE_NMS_TOP_N_TEST get_field POSITIVE_FRACTION FG_IOU_THRESHOLD RPNLossComputation BATCH_SIZE_PER_IMAGE BalancedPositiveNegativeSampler BG_IOU_THRESHOLD Matcher RETINANET_ON reshape permute view permute_and_flatten reshape shape zip append NMS_TH DETECTIONS_PER_IMG INFERENCE_TH PRE_NMS_TOP_N RetinaNetPostProcessor get_field FG_IOU_THRESHOLD RetinaNetLossComputation SigmoidFocalLoss LOSS_GAMMA BG_IOU_THRESHOLD Matcher LOSS_ALPHA WEIGHT_DECAY_BIAS SGD named_parameters BASE_LR BIAS_LR_FACTOR WEIGHT_DECAY convert _box_nms get_field bbox mode squeeze unbind bbox clamp min convert area max len add_field size set BoxList _cat fields mode int list isinstance tuple copy_ zero_ zip ceil Tensor update copy long minimum maximum size warn dot array unique ndarray maximum isinstance ndarray isinstance minimum maximum minimum maximum minimum maximum minimum dtype exp BBOX_XFORM_CLIP astype shape zeros transpose log enumerate max _rename_basic_resnet_weights sorted format getLogger OrderedDict from_numpy info keys _rename_fpn_weights sorted format replace getLogger match STAGE_WITH_DCN info keys enumerate CONV_BODY _rename_conv_weights_for_deformable_conv_layers replace _rename_weights_for_resnet _load_c2_pickled_weights get_pretty_env_info barrier get_world_size from_buffer dumps get_world_size loads zip append to max cat get_world_size startswith 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 timestamp now strftime makedirs update join categories format hasattr getLogger warning info __name__ is_main_process is_main_process max list sorted format view getLogger tolist info append keys enumerate len items sorted list OrderedDict 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 str timedelta get_field sort squeeze astype tuple tolist int64 rectangle zip get_field to bbox format putText tolist FONT_HERSHEY_SIMPLEX zip bbox putText format FONT_HERSHEY_SIMPLEX enumerate view size min expand max items join format print size set copy_ keys state_dict next tee size topk squeeze long size long arange fill_ items list File create_dataset items list asarray format print size File from_numpy copy_ range format print size f append batch_index_iterator Variable cuda LongTensor is_available join sorted format items named_parameters append topk size t eq mul_ expand_as append sum max ones diag where column_stack all Variable type cuda size dim range clone int numpy enumerate size long arange int enumerate append clone size min contiguous choice get_device cuda concatenate cumsum copy append range len append range zip items norm format sorted print size mul_ float print param_groups format | # graph-rcnn.pytorch Pytorch code for our ECCV 2018 paper ["Graph R-CNN for Scene Graph Generation"](https://arxiv.org/pdf/1808.00191.pdf) <div style="color:#0000FF" align="center"> <img src="figures/teaser_fig.png" width="850"/> </div> <!-- :balloon: 2019-06-04: Okaaay, time to reimplement Graph R-CNN on pytorch 1.0 and release a new benchmark for scene graph generation. It will also integrate other models like IMP, MSDN and Neural Motif Network. Stay tuned! :balloon: 2019-06-16: Plan is a bit delayed by ICCV rebuttal, but still on track. Stay tuned! --> ## Introduction This project is a set of reimplemented representative scene graph generation models based on Pytorch 1.0, including: * [Graph R-CNN for Scene Graph Generation](https://arxiv.org/pdf/1808.00191.pdf), our own. ECCV 2018. | 2,592 |
jxhe/vae-lagging-encoder | ['text generation'] | ['Lagging Inference Networks and Posterior Collapse in Variational Autoencoders'] | modules/utils.py plot_scripts/plot_single.py logger.py config/config_synthetic.py modules/encoders/enc_mix.py config/config_yahoo.py modules/vae.py modules/encoders/enc_resnet_v2.py modules/decoders/decoder_helper.py data/__init__.py toy.py modules/encoders/enc_resnet.py modules/decoders/__init__.py modules/encoders/encoder.py modules/decoders/dec_lstm.py config/config_omniglot.py plot_scripts/plot_multiple.py image.py modules/plotter.py modules/decoders/dec_pixelcnn_v2.py text.py modules/decoders/dec_pixelcnn.py modules/encoders/enc_lstm.py modules/__init__.py prepare_data.py config/config_yelp.py modules/lm/__init__.py modules/encoders/__init__.py modules/decoders/decoder.py data/text_data.py modules/lm/lm_lstm.py calc_au init_config test calc_iwnll main calc_mi Logger download_file_from_google_drive save_response_content get_confirm_token calc_au reconstruct init_config test calc_iwnll sample_from_prior main calc_mi init_config plot_multiple test calc_iwnll main calc_mi plot_single MonoTextData VocabEntry VisPlotter log_sum_exp generate_grid VAE DecoderBase BeamSearchNode LSTMDecoder VarLSTMDecoder PixelCNNDecoder StackedGatedMaskedConv2d GatedMaskedConv2d he_init PixelCNN MaskedConv2d MaskABlock PixelCNNBlock PixelCNNDecoderV2 GaussianEncoderBase LSTMEncoder VarLSTMEncoder MixLSTMEncoder CNNClassifier ResidualBlock ResNetEncoder MaskedConv2d he_init ResNetBlock deconv3x3 ResNet conv3x3 ResNetEncoderV2 LSTM_LM plot_multiple load_data plot_line seed join Namespace print add_argument params ArgumentParser manual_seed is_available parse_args dataset cuda makedirs flush print size sum loss calc_mi size calc_mi_q encode_stats size mean append sum cat nll_iw print size round enumerate flush len kl_start batch_size data_file clip_grad_norm_ zero_grad DataLoader __version__ save device dataset save_image cuda seed len Adam epochs TensorDataset load_state_dict to sum sample_from range state_dict bernoulli save_path size new_zeros PixelCNNDecoderV2 mean eval choice load_path flush calc_mi load join time backward make_savepath print warm_up min parameters cpu train step loss ResNetEncoderV2 makedirs get get_confirm_token save_response_content Session items list startswith permutation exp len permutation exp permutation uniform_initializer SGD dec_nh train_data LSTMDecoder decoding_strategy vocab MonoTextData decode_from format val_data dropped create_data_batch random_integers LSTMEncoder test_data plot_mode join dump batch_size chunk num_plot plot_dir calc_infer_mean append calc_model_posterior_mean round cat open join dump aggressive plot_dir cat open generate_grid dz zmax append zmin calc_model_posterior_mean data_sample calc_infer_mean exp squeeze sum max size repeat arange view sqrt normal_ in_features load open arange set_yticklabels add_subplot tick_params show set_xlabel set_linewidth scatter savefig set_color plot set_xticklabels set_xlim set_ticks_position set_yticks dict set_xticks set_ylabel figure zeros set_ylim len arange add_subplot tick_params show set_xlabel set_linewidth savefig set_color range plot set_xlim annotate set_yticks dict set_ylabel set_xticks figure zeros set_ylim len | # Aggressive Training of Inference Network This is PyTorch implementation of the [paper](http://arxiv.org/abs/1901.05534): ``` Lagging Inference Networks and Posterior Collapse in Variational Autoencoders Junxian He, Daniel Spokoyny, Graham Neubig, Taylor Berg-Kirkpatrick ICLR 2019 ``` The code seperates optimization of encoder and decoder in VAE, and performs more steps of encoder update in each iteration. This new training procedure mitigates the issue of posterior collapse in VAE and leads to a better VAE model, without changing model components and training objective. **This repo is able to reproduce quantitative experimental results and qualitative visualizations of posterior mean space presented in the paper.** Please contact [email protected] if you have any questions. | 2,593 |
jzlianglu/pykaldi2 | ['speech recognition'] | ['PyKaldi2: Yet another speech toolkit based on Kaldi and PyTorch'] | bin/train_transformer_ce.py simulation/_mixer.py utils/utils.py reader/reader.py simulation/config.py simulation/freq_analysis.py ops/ops.py reader/__init__.py simulation/mask.py decode.py reader/zip_io.py data/__init__.py ops/__init__.py models/__init__.py bin/latgen.py bin/train_se2.py reader/preprocess.py bin/train_chain.py utils/__init__.py bin/train_ce.py simulation/_sampling.py simulation/_geometry.py simulation/_rirgen.py bin/dump_loglikes.py simulation/overlap.py simulation/_distorter.py simulation/simulation.py models/transformer.py data/dataloader.py models/lstm.py reader/stream.py simulation/__init__.py data/sr_dataset.py models/lstm_libcss.py simulation/_iso_noise_simulator.py example/OpenCSS/local/utt_wer.py bin/train_se.py bin/train_transformer_se.py main main main main run_train_epoch main run_train_epoch main run_train_epoch main run_train_epoch main run_train_epoch main run_train_epoch ChunkDataloader SeqDataloader DataBuffer _utt2seg SpeechDataset DataGeneratorSequenceConfig DataGeneratorTrain LSTMAM NnetAM LSTMStack TransformerEncoderLayerWithConv1d PositionalEncoding TransformerAM sMBRFunction TeacherStudentMMI MMIFunction MWEFunction ChainObjtiveFunction GlobalMeanVarianceNormalization cmn apply_cmn feature_normalization ZipNpyIO convert_data_precision HDF5IO ZipPickleIO ZipIO BinaryIO ZipWaveIO WaveIO RIRStream gen_stream_from_zip remove_from_list_by_index TimitDataStream DataStream gen_speech_stream_from_list SpeechDataStream get_relative_path my_cat wavlist2uttlist WSJDataStream LibriDataStream zip_io write_wav zip_or_dir multi_channel_multi_source_config _dump_config2file _gen_stft_config single_channel_single_source_config single_channel_multi_source_config _gen_default_simu_config multi_channel_single_source_config MaskEstimator _PlacedUtterance _test OverlapSimulator _test_speed _fftconvolve1d _NoiseSampler _comp_noise_scale_given_snr Distorter RoomConfig SoundSourceConfig ArrayPositionConfig Rectangle _illustrate_dist2rectangle generate_isotropic_noise _sample_circle _sample_sphere ISONoiseConfig _get_hoth_mag _test_iso_noise MixerConfig Mixer xp_rirgen t60_to_alpha xp_rirgen2 min_t60_of_room get_sample _Sampler sample_array_position UniformIntSampler UniformSampler sample_source_position_by_random_coordinate sample_room sample_source_position DiscreteSampler BinarySampler GaussianSampler get_distribution_template AverageMeter noam_decay ProgressMeter ArgumentParser device prior_path tensor cuda log list set_device SeqDataloader OrderedDict load_state_dict append parse_args sum format eval model_path load items sweep_size print SpeechDataset add_argument NnetAM dumps LSTMStack dict data_path numpy len graph_dir exit LatticeFasterDecoderOptions trans_model from_files TransitionModel write isfile learn_mean_and_variance_from_train_loader DistributedOptimizer save global_mvn str Adam resume_from_model broadcast_parameters CrossEntropyLoss range state_dict param_groups ChunkDataloader size init is_available stream_idx_for_transform num_epochs GlobalMeanVarianceNormalization LSTMAM run_train_epoch dataPath hvd exp_dir parameters train local_rank broadcast_optimizer_state makedirs model clip_grad_norm_ zero_grad max_grad_norm cuda view to update size item is_available long enumerate time criterion backward print AverageMeter float32 parameters ProgressMeter step len __version__ xent_regularize ali_dir SupervisionOptions DenominatorGraph ChainTrainingOptions seed_model read lang_dir chain_dir ContextDependency roll proto_supervision_to_supervision save str list squeeze tolist alignment_to_proto_supervision apply append sum range state_dict frame_subsampling_factor to_phone_alignment param_groups lr noam_decay warmup_steps exp_dir SGD den_dir CrossEntropyLoss ce_ratio ce_criterion align Matrix numpy nlayers dropout nheads TransformerAM dim_model ff_size ones transpose masked_fill warmup_step float tril contiguous filter int reshape shape floor append range mean list cmn dict type append is_tensor keys apply_cmn apply list astype type append abs max sorted tolist append len range split append len range relpath DataStream set_data_len SpeechDataStream wavlist2uttlist ZipWaveIO set_data_len namelist my_cat ZipWaveIO sorted list get_label WSJDataStream append range RIRStream TimitDataStream DataStream set SpeechDataStream wavlist2uttlist zip ZipFile LibriDataStream load read BytesIO sort dict len sum int16 format T print min astype write copy float abs max clip config _gen_stft_config get_distribution_template _gen_default_simu_config single_channel_single_source_config _gen_default_simu_config multi_channel_single_source_config asarray multi_channel_multi_source_config single_channel_single_source_config single_channel_multi_source_config multi_channel_single_source_config simulate list range OverlapSimulator int rfft next_fast_len shape irfft mean sqrt toc _fftconvolve1d plot randn size tic range dist2point exp imagesc Rectangle linspace meshgrid zeros max range mod arccos cos pi sqrt sin zeros range int asarray arange f interp1d pi RuntimeError power arange cos pi sin zeros range int normal exp arange _sample_circle _sample_sphere pi RuntimeError sqrt log2 real ceil zeros _get_hoth_mag sum range irfft T asarray plot generate_isotropic_noise prod log prod log lfilter arange cos pi where flatten floor log exp ones logical_and shape sin sum prod range scatter_add get_delays_and_gains astype sqrt minimum int get_reflection_candidates print reshape min float32 at int32 zeros array len arange cos pi where flatten floor log exp ones shape sin uint32 sum prod range scatter_add get_delays_and_gains astype sqrt minimum int get_reflection_candidates print reshape min float32 at zeros len minimum normal int ones maximum choice uniform warning randint float zeros asarray reshape zeros GaussianSampler range len get_sample sample_source_position_by_random_coordinate zeros asarray range get_distribution_template | # pykaldi2 PyKaldi2 is a speech toolkit that is built based on [Kaldi](http://kaldi-asr.org/) and [PyTorch](https://pytorch.org/). It relies on [PyKaldi](https://github.com/pykaldi/pykaldi) - the Python wrapper of Kaldi, to access Kaldi functionalities. The key features of PyKaldi2 are one-the-fly lattice generation for lattice-based sequence training, on-the-fly data simulation and on-the-fly alignment gereation. A beta version lattice-free MMI (LFMMI) training script is also provided. ## How to install PyKaldi2 runs on top of the [Horovod](https://github.com/horovod/horovod) and PyKaldi libraries. The dockerfile is provided to customarize the envriorment. To use the repo, do the following three steps. 1. Clone the repo by ``` git clone https://github.com/jzlianglu/pykaldi2.git ``` 2. Build the docker image, simply run ``` | 2,594 |
jzwerling/SemanticKnowledgeGraph | ['anomaly detection'] | ['The Semantic Knowledge Graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domain'] | skg/es_queries.py skg/ingest_xml.py skg/skg.py get_entities populate_index get_edge_for_two_nodes index_settings getElementsByTagName create list items get_entities index refresh sub getAttribute nlp int format find_edge print search | # Semantic Knowledge Graph Implementation of Semantic Knowledge graph with Elasticsearch 6.6 and Python 3.7 Based on Trey Grainger's presentation here: https://www.youtube.com/watch?v=JvuQX92zyi0&t=2124s and white paper here: https://arxiv.org/pdf/1609.00464.pdf To recreate the Jean Grey example from the presentation, downloaded scifi.stackexchange.com.7z from https://archive.org/download/stackexchange, ingest_xml.py is used to populate the Elasticsearch indexes with the archive data. As written, the code is looking for Posts.xml and Comments.xml in the same directory as ingest_xml.py. skg.py takes the provided nodes and displays the terms that show how the nodes are related, effectively describing the edge that connects the nodes. This is done through a query to Elasticsearch with two match_phrase conditions, one for each node supplied, results come from significant terms aggregation. There is also an optional 'levels' parameter to indicate recursion depth. If this is supplied, skg will iterate through the returned significant terms, and sequentially pair these with the orignal nodes to find more related terms. *** ingest_xml.py now takes an optional parameter, index_entities. When set to true, spaCy is used to identify the entities in each text, and these are indexed in the 'entities' field. So now it is possible to run skg.py and get a significant terms aggregation based on the entities identified in each document rather than just the terms in the text. | 2,595 |
jzwerling/semantic-knowledge-graph | ['anomaly detection'] | ['The Semantic Knowledge Graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domain'] | skg/es_queries.py skg/ingest_xml.py skg/skg.py get_entities populate_index get_edge_for_two_nodes index_settings getElementsByTagName create list items get_entities index refresh sub getAttribute nlp int format find_edge print search | # Semantic Knowledge Graph Implementation of Semantic Knowledge graph with Elasticsearch 6.6 and Python 3.7 Based on Trey Grainger's presentation here: https://www.youtube.com/watch?v=JvuQX92zyi0&t=2124s and white paper here: https://arxiv.org/pdf/1609.00464.pdf To recreate the Jean Grey example from the presentation, downloaded scifi.stackexchange.com.7z from https://archive.org/download/stackexchange, ingest_xml.py is used to populate the Elasticsearch indexes with the archive data. As written, the code is looking for Posts.xml and Comments.xml in the same directory as ingest_xml.py. skg.py takes the provided nodes and displays the terms that show how the nodes are related, effectively describing the edge that connects the nodes. This is done through a query to Elasticsearch with two match_phrase conditions, one for each node supplied, results come from significant terms aggregation. There is also an optional 'levels' parameter to indicate recursion depth. If this is supplied, skg will iterate through the returned significant terms, and sequentially pair these with the orignal nodes to find more related terms. *** ingest_xml.py now takes an optional parameter, index_entities. When set to true, spaCy is used to identify the entities in each text, and these are indexed in the 'entities' field. So now it is possible to run skg.py and get a significant terms aggregation based on the entities identified in each document rather than just the terms in the text. | 2,596 |
k920049/brunch-hgru | ['session based recommendations'] | ['Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks'] | torchmodel.py model/legacy/model.py model/torch/ListModule.py model/tensorflow/Helper.py pipeline/torch/Vocabulary.py model/tensorflow/Wrapper.py model/tensorflow/Loss.py pipeline/tensorflow/Reader.py model/torch/Loss.py model/torch/Cell.py test.py pipeline/torch/Dataset.py model/legacy/torchmodel.py src/utils.py model.py model/torch/Wrapper.py src/train_hier_gru.py src/hgru4rec.py pipeline/torch/Vectorizer.py pipeline/tensorflow/History.py pipeline/tensorflow/Datasets.py src/evaluation.py custom_acc custom_loss HierarchicalRecommender HierarchicalRecommender Recommender _generate_zero_filled_state _is_multiple_state _generate_zero_filled_state_for_cell RankingLoss HierarchicalRNNCell HierarchicalRNNCell ListModule Word2VecEncoder word_rank_dictionary word_count Rand_Idxed_Corpus LinearDecoder LogUniformSampler SampledSoftmax word_freq_ordered HierarchicalRNN Datasets History Reader generate_batches BrunchDataset Vectorizer Vocabulary evaluate_sessions_batch evaluate_sessions evaluate_sessions_batch_hier_bootstrap print_norm Sampler inspect HGRU4Rec sort_by_order sort_by_length get_parameter_norm get_grad_norm squeeze divide reduce_sum dtype is_sequence idx2word valid test train len argsort word_count zeros len enumerate word_freq_ordered to items DataLoader list nunique cumsum from_records vstack max fillna predict_next_batch seed tolist append sum sort_values range format hstack astype unique info T print min int32 zeros array len from_records concat vstack max drop_duplicates predict_next_batch fillna seed tolist logical_and preprocess_data append sum range format hstack astype copy unique info remove T cumcount min any int32 zeros array len sum from_records predict_next hstack vstack in1d unique append sort_values range len norm format inspect info list norm norm sorted size stack zip sorted size stack zip | # HGRU4Rec Code for our ACM RecSys 2017 paper "Personalizing Session-based Recommendation with Hierarchical Recurrent Neural Networks". See the paper: [https://arxiv.org/abs/1706.04148](https://arxiv.org/abs/1706.04148) ## Setup This code is based of GRU4Rec ([https://github.com/hidasib/GRU4Rec](https://github.com/hidasib/GRU4Rec)). As the original code, it is written in Python 3.4 and requires Theano 0.8.0+ to run efficiently on GPU. In addition, this code uses H5Py and PyTables for efficient I/O operations. We suggest to use `virtualenv` or `conda` (preferred) together with `requirements.txt` to set up a virtual environment before running the code. ## Experiments on the XING dataset This repository comes with the code necessary to reproduce the experiments on the XING dataset. | 2,597 |
k9k2/qSGD | ['stochastic optimization'] | ['Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization'] | cifar10_WideResNet/utils/progress/progress/helpers.py cifar10_WideResNet/cifar.py cifar10_WideResNet/models/cifar/resnet.py cifar10_WideResNet/models/cifar/__init__.py cifar10_WideResNet/utils/transforms.py cifar10_WideResNet/models/cifar/wrn.py cifar10_WideResNet/utils/progress/progress/counter.py cifar10_WideResNet/utils/eval.py cifar10_WideResNet/utils/logger.py cifar10_WideResNet/utils/visualize.py cifar10_WideResNet/transforms.py plot.py cifar10_WideResNet/utils/progress/progress/spinner.py models/models.py cifar10_WideResNet/utils/__init__.py main.py cifar10_WideResNet/utils/progress/test_progress.py models/preact_resnet.py cifar10_WideResNet/utils/progress/setup.py cifar10_WideResNet/utils/misc.py cifar10_WideResNet/utils/progress/progress/__init__.py cifar10_WideResNet/utils/progress/progress/bar.py lr_scheduler multiClassHingeLoss lr_decay_func output train RandomErasing ResNet Bottleneck conv3x3 resnet BasicBlock wrn BasicBlock NetworkBlock WideResNet accuracy plot_overlap savefig Logger LoggerMonitor init_params AverageMeter mkdir_p get_mean_and_std RandomErasing make_image show_mask_single show_mask gauss colorize show_batch sleep FillingSquaresBar FillingCirclesBar IncrementalBar ChargingBar ShadyBar PixelBar Bar Countdown Stack Counter Pie SigIntMixin WriteMixin WritelnMixin PieSpinner MoonSpinner Spinner PixelSpinner LineSpinner Progress Infinite LeNet Linear PreActBlock PreActResNet50 PreActResNet PreActResNet18 test PreActResNet152 PreActBottleneck PreActResNet101 PreActResNet34 param_groups lr_decay_func lr_scheduler model Variable print lr_decay_func size exit zero_grad backward mean hinge_loss step cuda cross_entropy enumerate format model print train write eval float enumerate WideResNet topk size t eq mul_ expand_as append sum max asarray arange plot numbers enumerate len print DataLoader div_ zeros range len normal constant isinstance kaiming_normal Conv2d bias modules BatchNorm2d weight Linear makedirs numpy range zeros unsqueeze gauss show make_image imshow make_grid make_image subplot make_grid size clone axis upsampling imshow expand_as range make_image subplot make_grid size clone axis upsampling imshow expand_as cpu range len randn print size PreActResNet18 net | # Ordered SGD: a simple modification of SGD to accelerate training and improve test accuracy This repo consists Pytorch code for the AISTATS 2020 paper "[Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization](http://proceedings.mlr.press/v108/kawaguchi20a.html)". The proposed algorithm, Ordered SGD, **is fast (computationally efficient), is easy to be implemented, and comes with theoretical gurantees in both optimization and generalization**. Implementing Ordered SGD only requires modifications of one line or few lines in any code that uses SGD. The Ordered SGD algorithm accelerates training and improves test accuracy by focusing on the important data samples. The following figure illustrates the advantage of Ordered SGD in that Ordered SGD learns a different type of models than those learned by the standard SGD, which is sometimes beneficial. <p align="center"> <img src="fig/fig1_1.png" height="400" width= "800"> </p> As a result of the above mechanism (and other theoretical facts), Ordered SGD is fast and can improve test errors as shown in the following figure and tables: CIFAR-10 with WideResNet28_10 | Method | test error | | ------------- | ---------- | | 2,598 |
kabrau/FaceDetection | ['face detection'] | ['WIDER FACE: A Face Detection Benchmark'] | detectors.py WebCamTest.py test.py BlurImages.py label_map_util.py run SSDDetector getPositions printBlur CascadeDetector ImageTools 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 join SSDDetector imwrite print PrintBoxes Detect waitKey copy imshow Open CascadeDetector ImageTools resize BlurBoxes imread listdir shape uint8 getPositions fillPoly astype bitwise_and BORDER_DEFAULT shape copyto zeros GaussianBlur array item name id display_name item info append range _validate_label_map item id load_labelmap max convert_label_map_to_categories load_labelmap | # FaceDetection Face detection Haar Cascade vs SSD **Video:** https://youtu.be/UOHbKHGze2U **Post:** https://medium.com/@kabrau/detec%C3%A7%C3%A3o-de-faces-com-ssd-e-haar-cascade-para-desfocar-as-faces-em-imagens-5532184bcd89 **WebCanmTest.py:** Predict faces from WebCam **test.py:** Predict faces from samples folder **BlurImages.py:** Blur faces from a folder ## Cascade | 2,599 |
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