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Non-biri/ml-agents-Individuality-Experiment | ['unity'] | ['Unity: A General Platform for Intelligent Agents'] | ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_input_pb2.py ml-agents/mlagents/trainers/components/reward_signals/curiosity/model.py ml-agents-envs/mlagents/envs/communicator_objects/unity_to_external_pb2.py ml-agents/mlagents/trainers/components/reward_signals/reward_signal.py gym-unity/gym_unity/envs/__init__.py ml-agents/mlagents/trainers/learn.py ml-agents-envs/mlagents/envs/communicator_objects/custom_observation_pb2.py ml-agents/mlagents/trainers/meta_curriculum.py ml-agents/mlagents/trainers/tests/test_barracuda_converter.py ml-agents/mlagents/trainers/ppo/models.py gym-unity/gym_unity/__init__.py utils/validate_meta_files.py ml-agents/mlagents/trainers/trainer_controller.py ml-agents/mlagents/trainers/components/bc/model.py ml-agents/mlagents/trainers/tests/test_curriculum.py ml-agents-envs/mlagents/envs/communicator.py ml-agents-envs/mlagents/envs/communicator_objects/custom_reset_parameters_pb2.py ml-agents/mlagents/trainers/tests/test_ppo.py ml-agents-envs/mlagents/envs/tests/test_rpc_communicator.py ml-agents/mlagents/trainers/components/reward_signals/__init__.py ml-agents-envs/setup.py ml-agents/mlagents/trainers/tests/mock_brain.py ml-agents-envs/mlagents/envs/action_info.py ml-agents-envs/mlagents/envs/rpc_communicator.py ml-agents/mlagents/trainers/tests/test_bcmodule.py ml-agents/mlagents/trainers/tests/test_trainer_controller.py ml-agents/mlagents/trainers/components/reward_signals/reward_signal_factory.py ml-agents/setup.py ml-agents/mlagents/trainers/barracuda.py ml-agents-envs/mlagents/envs/tests/test_envs.py ml-agents-envs/mlagents/envs/env_manager.py ml-agents/mlagents/trainers/ppo/trainer.py ml-agents-envs/mlagents/envs/tests/test_timers.py ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_output_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_initialization_output_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/unity_input_pb2.py ml-agents/mlagents/trainers/tests/test_meta_curriculum.py ml-agents/mlagents/trainers/components/reward_signals/curiosity/signal.py ml-agents-envs/mlagents/envs/subprocess_env_manager.py ml-agents/mlagents/trainers/bc/trainer.py ml-agents/mlagents/trainers/components/reward_signals/curiosity/__init__.py ml-agents/mlagents/trainers/curriculum.py ml-agents-envs/mlagents/envs/communicator_objects/agent_action_proto_pb2.py ml-agents/mlagents/trainers/tests/test_policy.py ml-agents/mlagents/trainers/ppo/policy.py ml-agents-envs/mlagents/envs/communicator_objects/space_type_proto_pb2.py ml-agents/mlagents/trainers/tests/test_learn.py ml-agents-envs/mlagents/envs/communicator_objects/brain_parameters_proto_pb2.py ml-agents/mlagents/trainers/tests/test_demo_loader.py ml-agents/mlagents/trainers/components/bc/__init__.py ml-agents/mlagents/trainers/models.py ml-agents/mlagents/trainers/__init__.py ml-agents-envs/mlagents/envs/communicator_objects/agent_info_proto_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/environment_parameters_proto_pb2.py ml-agents/mlagents/trainers/tests/test_simple_rl.py ml-agents-envs/mlagents/envs/policy.py ml-agents/mlagents/trainers/exception.py gym-unity/gym_unity/tests/test_gym.py ml-agents/mlagents/trainers/components/reward_signals/extrinsic/__init__.py ml-agents/mlagents/trainers/buffer.py ml-agents/mlagents/trainers/bc/online_trainer.py ml-agents-envs/mlagents/envs/communicator_objects/engine_configuration_proto_pb2.py ml-agents/mlagents/trainers/ppo/__init__.py ml-agents/mlagents/trainers/tensorflow_to_barracuda.py ml-agents-envs/mlagents/envs/communicator_objects/unity_to_external_pb2_grpc.py ml-agents-envs/mlagents/envs/mock_communicator.py ml-agents/mlagents/trainers/tests/test_rl_trainer.py ml-agents-envs/mlagents/envs/timers.py gym-unity/setup.py ml-agents-envs/mlagents/envs/communicator_objects/unity_message_pb2.py ml-agents-envs/mlagents/envs/environment.py ml-agents-envs/mlagents/envs/communicator_objects/custom_action_pb2.py ml-agents/mlagents/trainers/bc/policy.py ml-agents-envs/mlagents/envs/simple_env_manager.py ml-agents-envs/mlagents/envs/base_unity_environment.py ml-agents/mlagents/trainers/bc/__init__.py ml-agents/mlagents/trainers/trainer_util.py ml-agents/mlagents/trainers/tests/test_trainer_util.py ml-agents-envs/mlagents/envs/communicator_objects/unity_output_pb2.py ml-agents/mlagents/trainers/components/reward_signals/extrinsic/signal.py ml-agents/mlagents/trainers/components/reward_signals/gail/__init__.py ml-agents-envs/mlagents/envs/sampler_class.py ml-agents-envs/mlagents/envs/exception.py gym-unity/gym_unity/envs/unity_env.py ml-agents/mlagents/trainers/components/reward_signals/gail/model.py ml-agents-envs/mlagents/envs/communicator_objects/header_pb2.py ml-agents/mlagents/trainers/rl_trainer.py ml-agents/mlagents/trainers/tests/test_reward_signals.py ml-agents-envs/mlagents/envs/brain.py ml-agents/mlagents/trainers/components/reward_signals/gail/signal.py ml-agents/mlagents/trainers/ppo/multi_gpu_policy.py ml-agents/mlagents/trainers/tests/test_multigpu.py ml-agents-envs/mlagents/envs/communicator_objects/demonstration_meta_proto_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/resolution_proto_pb2.py ml-agents-envs/mlagents/envs/communicator_objects/__init__.py ml-agents/mlagents/trainers/demo_loader.py ml-agents-envs/mlagents/envs/__init__.py ml-agents/mlagents/trainers/components/bc/module.py ml-agents/mlagents/trainers/tests/test_trainer_metrics.py ml-agents-envs/mlagents/envs/tests/test_sampler_class.py ml-agents/mlagents/trainers/tests/test_buffer.py ml-agents-envs/mlagents/envs/communicator_objects/command_proto_pb2.py ml-agents/mlagents/trainers/trainer.py ml-agents-envs/mlagents/envs/socket_communicator.py ml-agents-envs/mlagents/envs/tests/test_subprocess_env_manager.py ml-agents/mlagents/trainers/bc/models.py ml-agents/mlagents/trainers/bc/offline_trainer.py ml-agents/mlagents/trainers/tf_policy.py ml-agents/mlagents/trainers/tests/test_bc.py ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_initialization_input_pb2.py ml-agents/mlagents/trainers/trainer_metrics.py UnityGymException ActionFlattener UnityEnv create_mock_vector_braininfo test_gym_wrapper test_multi_agent test_branched_flatten setup_mock_unityenvironment create_mock_brainparams BarracudaWriter fuse print_known_operations compress Build sort lstm write fuse_batchnorm_weights trim mean gru Model summary Struct parse_args to_json rnn BufferException Buffer Curriculum make_demo_buffer load_demonstration demo_to_buffer CurriculumError MetaCurriculumError TrainerError create_environment_factory create_sampler_manager run_training prepare_for_docker_run try_create_meta_curriculum main load_config MetaCurriculum EncoderType LearningModel AllRewardsOutput RLTrainer get_layer_shape pool_to_HW flatten sqr_diff process_layer process_model get_layer_rank slow_but_stable_topological_sort get_attr basic_lstm ModelBuilderContext order_by get_epsilon get_tensor_dtype replace_strings_in_list debug embody by_op get_tensor_dims strided_slice remove_duplicates_from_list axis_to_barracuda by_name locate_actual_output_node convert strides_to_HW get_tensor_data very_slow_but_stable_topological_sort gru TFPolicy UnityPolicyException UnityTrainerException Trainer TrainerController TrainerMetrics initialize_trainers BehavioralCloningModel OfflineBCTrainer OnlineBCTrainer BCPolicy BCTrainer BCModel BCModule RewardSignal create_reward_signal CuriosityModel CuriosityRewardSignal ExtrinsicRewardSignal GAILModel GAILRewardSignal PPOModel get_devices MultiGpuPPOPolicy PPOPolicy PPOTrainer get_gae discount_rewards create_buffer simulate_rollout create_mock_3dball_brain create_mock_banana_brain setup_mock_unityenvironment create_mock_braininfo create_mock_brainparams setup_mock_env_and_brains test_barracuda_converter test_bc_trainer_step test_bc_trainer_add_proc_experiences test_cc_bc_model test_dc_bc_model test_visual_cc_bc_model test_bc_trainer_end_episode test_bc_policy_evaluate dummy_config test_visual_dc_bc_model create_bc_trainer test_bcmodule_rnn_update test_bcmodule_update test_bcmodule_dc_visual_update dummy_config create_ppo_policy_with_bc_mock test_bcmodule_defaults test_bcmodule_rnn_dc_update test_buffer_sample construct_fake_buffer assert_array fakerandint test_buffer test_buffer_truncate location default_reset_parameters test_init_curriculum_bad_curriculum_raises_error test_init_curriculum_happy_path test_increment_lesson test_get_config test_load_demo test_load_demo_dir basic_options test_docker_target_path test_run_training test_init_meta_curriculum_happy_path test_increment_lessons_with_reward_buff_sizes default_reset_parameters MetaCurriculumTest test_increment_lessons measure_vals reward_buff_sizes test_set_all_curriculums_to_lesson_num test_get_config test_set_lesson_nums test_init_meta_curriculum_bad_curriculum_folder_raises_error more_reset_parameters test_create_model dummy_config test_average_gradients test_update basic_mock_brain test_take_action_returns_action_info_when_available basic_params test_take_action_returns_nones_on_missing_values test_take_action_returns_empty_with_no_agents test_trainer_increment_step test_rl_functions test_ppo_model_dc_vector_rnn test_ppo_model_cc_vector_rnn test_add_rewards_output test_ppo_policy_evaluate test_ppo_model_cc_visual dummy_config test_ppo_model_dc_vector test_ppo_model_dc_visual test_ppo_get_value_estimates test_ppo_model_cc_vector test_gail_dc_visual reward_signal_update reward_signal_eval test_extrinsic test_curiosity_cc test_gail_rnn test_gail_cc create_ppo_policy_mock test_curiosity_dc curiosity_dummy_config dummy_config test_curiosity_visual test_curiosity_rnn gail_dummy_config create_mock_all_brain_info create_rl_trainer dummy_config test_rl_trainer create_mock_brain create_mock_policy clamp test_simple_rl Simple1DEnvironment _check_environment_trains test_initialization_seed test_start_learning_trains_until_max_steps_then_saves basic_trainer_controller test_take_step_adds_experiences_to_trainer_and_trains dummy_config trainer_controller_with_take_step_mocks trainer_controller_with_start_learning_mocks test_start_learning_trains_forever_if_no_train_model TestTrainerMetrics test_initialize_online_bc_trainer test_initialize_ppo_trainer test_initialize_trainer_parameters_override_defaults dummy_offline_bc_config test_initialize_invalid_trainer_raises_exception dummy_bad_config dummy_config dummy_offline_bc_config_with_override dummy_online_bc_config ActionInfo BaseUnityEnvironment safe_concat_np_ndarray BrainInfo BrainParameters safe_concat_lists Communicator UnityEnvironment EnvManager StepInfo SamplerException UnityWorkerInUseException UnityException UnityCommunicationException UnityTimeOutException UnityEnvironmentException UnityActionException MockCommunicator Policy RpcCommunicator UnityToExternalServicerImplementation MultiRangeUniformSampler UniformSampler SamplerFactory SamplerManager GaussianSampler Sampler SimpleEnvManager SocketCommunicator worker EnvironmentResponse UnityEnvWorker StepResponse SubprocessEnvManager EnvironmentCommand TimerNode hierarchical_timer get_timer_root get_timer_tree reset_timers set_gauge timed GaugeNode TimerStack UnityToExternalServicer UnityToExternalStub add_UnityToExternalServicer_to_server test_initialization test_reset test_close test_step test_handles_bad_filename test_rpc_communicator_checks_port_on_create test_rpc_communicator_create_multiple_workers test_rpc_communicator_close test_empty_samplers sampler_config_1 check_value_in_intervals incorrect_uniform_sampler test_incorrect_sampler test_sampler_config_1 sampler_config_2 incorrect_sampler_config test_incorrect_uniform_sampler test_sampler_config_2 mock_env_factory SubprocessEnvManagerTest MockEnvWorker test_timers decorated_func main create_mock_vector_braininfo sample UnityEnv setup_mock_unityenvironment step create_mock_brainparams create_mock_vector_braininfo UnityEnv setup_mock_unityenvironment step create_mock_brainparams setup_mock_unityenvironment create_mock_vector_braininfo create_mock_brainparams UnityEnv Mock list Mock array range join isdir print replaceFilenameExtension add_argument exit verbose source_file ArgumentParser target_file sqrt topologicalSort list hasattr layers addEdge Graph print inputs set len list hasattr layers print filter match trim_model compile data layers print tensors float16 replace layers dumps layers isinstance print tensors inputs zip to_json globals Build array_equal pool reduce Build tanh mad tanh mul Build concat add sigmoid sub mad _ tanh mul Build concat add sigmoid mad print sorted keys Buffer reset_local_buffers number_visual_observations append_update_buffer append range enumerate make_demo_buffer load_demonstration join read suffix isdir endswith BrainParametersProto from_agent_proto DemonstrationMetaProto ParseFromString AgentInfoProto isfile append from_proto listdir _DecodeVarint32 start_learning int str format create_environment_factory create_sampler_manager initialize_trainers external_brains TrainerController put try_create_meta_curriculum reset_parameters load_config SubprocessEnvManager pop SamplerManager load_config list set_all_curriculums_to_lesson_num MetaCurriculum reset_parameters keys chmod format basename isdir glob copyfile copytree prepare_for_docker_run replace int Process join docopt getLogger print run_training start Queue info append randint setLevel range endswith len print HasField hasattr get_attr isinstance get_attr tensor_shape ndarray isinstance shape int_val bool_val float_val ListFields name ndarray isinstance str tensor_content ndarray product isinstance get_tensor_dtype print get_tensor_dims unpack int_val bool_val array float_val enter append add set Build mul sub insert Build tolist append range len locate_actual_output_node name find_tensor_by_name split locate_actual_output_node name lstm find_tensor_by_name find_forget_bias split get_layer_shape id Struct tensor get_layer_rank layer_ranks hasattr name patch_data rank input_shapes out_shapes input get_attr append replace_strings_in_list tensors embody astype op inputs zip enumerate print float32 patch_data_fn model_tensors map_ignored_layer_to_its_input co_argcount len items list hasattr get_tensors name print process_layer eval slow_but_stable_topological_sort ModelBuilderContext sort assign_ids pop range insert len layers verbose Struct process_model open print_known_operations fuse compress node GraphDef Model dims_to_barracuda_shape insert get_tensor_dims inputs MessageToJson ParseFromString cleanup_layers read memories print sort write trim summary print_supported_ops update format OfflineBCTrainer copy OnlineBCTrainer PPOTrainer get check_config rcls list_local_devices list zeros_like size reversed range append discount_rewards Mock list ones array range brain_name create_buffer brain sequence_length append range vector_action_space_size Buffer ones number_visual_observations append_update_buffer shape append sum range enumerate setup_mock_unityenvironment mock_env create_mock_braininfo create_mock_brainparams create_mock_brainparams create_mock_brainparams join remove _get_candidate_names convert _get_default_tempdir dirname abspath isfile next Mock BCTrainer simulate_rollout mock_env dirname abspath setup_mock_unityenvironment policy create_mock_braininfo create_mock_3dball_brain update_policy create_bc_trainer increment_step agents process_experiences step create_bc_trainer add_experiences end_episode agents process_experiences step create_bc_trainer add_experiences BCPolicy evaluate close reset MockCommunicator reset_default_graph UnityEnvironment reset_default_graph reset_default_graph reset_default_graph reset_default_graph mock_env dirname abspath PPOPolicy setup_mock_unityenvironment create_mock_braininfo create_ppo_policy_with_bc_mock close create_mock_3dball_brain update items list close create_ppo_policy_with_bc_mock create_mock_3dball_brain update items list close create_ppo_policy_with_bc_mock create_mock_3dball_brain update items list close create_mock_banana_brain create_ppo_policy_with_bc_mock update items list close create_mock_banana_brain create_ppo_policy_with_bc_mock flatten list range len append range Buffer get_batch construct_fake_buffer assert_array append_update_buffer make_mini_batch reset_agent array sample_mini_batch construct_fake_buffer append_update_buffer construct_fake_buffer truncate_update_buffer append_update_buffer Curriculum Curriculum Curriculum make_demo_buffer load_demonstration dirname abspath make_demo_buffer load_demonstration dirname abspath MagicMock basic_options MagicMock MetaCurriculum assert_has_calls MetaCurriculumTest increment_lessons assert_called_with MetaCurriculumTest increment_lessons assert_called_with assert_not_called MetaCurriculumTest set_all_curriculums_to_lesson_num MetaCurriculumTest dict update MetaCurriculumTest reset_default_graph MultiGpuPPOPolicy create_mock_brainparams reset_default_graph create_mock_brainparams update Mock reset_default_graph MultiGpuPPOPolicy create_mock_brainparams MagicMock TFPolicy basic_mock_brain basic_params BrainInfo get_action MagicMock TFPolicy basic_mock_brain basic_params BrainInfo get_action MagicMock TFPolicy basic_mock_brain ActionInfo basic_params BrainInfo get_action evaluate close reset MockCommunicator PPOPolicy reset_default_graph UnityEnvironment get_value_estimates items list close reset MockCommunicator PPOPolicy reset_default_graph UnityEnvironment reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph assert_array_almost_equal array discount_rewards Mock increment_step BrainParameters assert_called_with PPOTrainer AllRewardsOutput BrainParameters PPOTrainer add_rewards_outputs update PPOPolicy setup_mock_env_and_brains reset evaluate model simulate_rollout _execute_model prepare_update update_dict make_mini_batch create_ppo_policy_mock reward_signal_update reward_signal_eval reward_signal_update reward_signal_eval create_ppo_policy_mock dirname abspath create_ppo_policy_mock reward_signal_update reward_signal_eval create_ppo_policy_mock reward_signal_update reward_signal_eval create_ppo_policy_mock reward_signal_update reward_signal_eval create_ppo_policy_mock reward_signal_update reward_signal_eval create_ppo_policy_mock reward_signal_update reward_signal_eval create_ppo_policy_mock reward_signal_update reward_signal_eval create_mock_brainparams RLTrainer dummy_config create_mock_brain Mock list create_mock_all_brain_info create_rl_trainer values end_episode construct_curr_info episode_steps create_mock_braininfo create_mock_policy add_experiences Simple1DEnvironment _check_environment_trains TrainerController assert_called_with MagicMock basic_trainer_controller start_learning assert_called_once MagicMock assert_not_called trainer_controller_with_start_learning_mocks trainer_controller_with_start_learning_mocks start_learning MagicMock assert_called_once MagicMock basic_trainer_controller assert_called_once Mock MagicMock StepInfo current_all_brain_info advance outputs assert_not_called trainer_controller_with_take_step_mocks assert_called_once_with previous_all_brain_info dummy_offline_bc_config dummy_offline_bc_config_with_override BrainParametersMock BrainParametersMock dummy_online_bc_config dummy_config BrainParametersMock dummy_bad_config extend copy global_done get_timer_root reset_timers put _send_response reset_parameters StepResponse env_factory memory list action value external_brains payload items EnvironmentResponse text reset step perf_counter push reset method_handlers_generic_handler add_generic_rpc_handlers UnityEnvironment close MockCommunicator UnityEnvironment close MockCommunicator reset str local_done print agents step close reset MockCommunicator UnityEnvironment len UnityEnvironment close MockCommunicator close RpcCommunicator close RpcCommunicator close RpcCommunicator SamplerManager sample_all sampler_config_1 sampler_config_2 SamplerManager SamplerManager sample_all incorrect_uniform_sampler incorrect_sampler_config set_gauge replace endswith add set walk | Non-biri/ml-agents-Individuality-Experiment | 800 |
OATML/bdl-benchmarks | ['out of distribution detection'] | ['A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks'] | baselines/diabetic_retinopathy_diagnosis/mc_dropout/model.py baselines/diabetic_retinopathy_diagnosis/deterministic/__init__.py bdlb/core/transforms.py baselines/diabetic_retinopathy_diagnosis/mc_dropout/__init__.py baselines/diabetic_retinopathy_diagnosis/deep_ensembles/main.py baselines/__init__.py bdlb/diabetic_retinopathy_diagnosis/tfds_adapter.py bdlb/core/benchmark.py bdlb/diabetic_retinopathy_diagnosis/benchmark.py baselines/diabetic_retinopathy_diagnosis/ensemble_mc_dropout/main.py bdlb/core/plotting.py bdlb/diabetic_retinopathy_diagnosis/__init__.py baselines/diabetic_retinopathy_diagnosis/deep_ensembles/model.py baselines/diabetic_retinopathy_diagnosis/ensemble_mc_dropout/model.py baselines/diabetic_retinopathy_diagnosis/ensemble_mc_dropout/__init__.py baselines/diabetic_retinopathy_diagnosis/deterministic/main.py bdlb/core/constants.py bdlb/core/__init__.py baselines/diabetic_retinopathy_diagnosis/mfvi/__init__.py baselines/diabetic_retinopathy_diagnosis/deterministic/model.py baselines/diabetic_retinopathy_diagnosis/mfvi/main.py baselines/diabetic_retinopathy_diagnosis/deep_ensembles/__init__.py baselines/diabetic_retinopathy_diagnosis/mc_dropout/main.py baselines/diabetic_retinopathy_diagnosis/mfvi/model.py bdlb/core/levels.py bdlb/__init__.py setup.py bdlb/core/registered.py main predict main predict main predict main VGGDrop predict main VGGFlipout predict Benchmark Level leaderboard tfk_history load BenchmarkNotFoundError Transform RandomAugment Compose Resize Normalize DiabeticRetinopathyDiagnosisBecnhmark DiabeticRetinopathyDiagnosis DiabeticRetinopathyDiagnosisConfig load list partial evaluate print dict VGGDrop load_weights model_checkpoints datasets summary append entropy bernoulli reshape mean shape std tfk_history fit model keras Sequential layers compile VGGFlipout keras Sequential layers compile get join show format subplots plot isinstance tight_layout set savefig legend makedirs join format subplots replace plot ValueError isinstance makedirs tight_layout set savefig legend fill_between read_csv enumerate | # Bayesian Deep Learning Benchmarks **This repository is no longer being updated.** Please refer to the [Diabetic Retinopathy Detection implementation in Google's 'uncertainty-baselines' repo](https://github.com/google/uncertainty-baselines/tree/master/baselines/diabetic_retinopathy_detection) for up-to-date baseline implementations. ## Overview In order to make real-world difference with **Bayesian Deep Learning** (BDL) tools, the tools must scale to real-world settings. And for that we, the research community, must be able to evaluate our inference tools (and iterate quickly) with real-world benchmark tasks. We should be able to do this without necessarily worrying about application-specific domain knowledge, like the expertise often required in medical applications for example. We require benchmarks to test for inference robustness, performance, and accuracy, in addition to cost and effort of development. These benchmarks should be at a variety of scales, ranging from toy `MNIST`-scale benchmarks for fast development cycles, to large data benchmarks which are truthful to real-world applications, capturing their constraints. Our BDL benchmarks should * provide a transparent, modular and consistent interface for the evaluation of deep probabilistic models on a variety of _downstream tasks_; * rely on expert-driven metrics of uncertainty quality (actual applications making use of BDL uncertainty in the real-world), but abstract away the expert-knowledge and eliminate the boilerplate steps necessary for running experiments on real-world datasets; * make it easy to compare the performance of new models against _well tuned baselines_, models that have been well-adopted by the machine learning community, under a fair and realistic setting (e.g., computational resources, model sizes, datasets); * provide reference implementations of baseline models (e.g., Monte Carlo Dropout Inference, Mean Field Variational Inference, Deep Ensembles), enabling rapid prototyping and easy development of new tools; | 801 |
OGHinde/Cool_MTGP | ['gaussian processes'] | ['A conditional one-output likelihood formulation for multitask Gaussian processes'] | lib/gpr.py lib/coolmt_gptorch.py lib/kernels.py lib/Cool_MTGP.py lib/Cool_MTGP2.py lib/COOLMTgpr_old.py lib/H_Cool_MTGP.py run_experiments_real_cooltorch.py COOLTorch.py lib/A_Cool_MTGP.py lib/COOLMTgpr.py MT_Matrix RMSE_NORM ExactGPModel RMSE predict_efficient MultitaskGPModel optimizeMTGP MultitaskGP GaussianProcessRegressor MultitaskGP ExactGPModel Cool_MTGP ExactGPModel MT_Matrix optimizeMTGP predict_efficient ExactGPModel MT_Matrix optimizeMTGP predict_efficient MultitaskGP GaussianProcessRegressor Product ExpSineSquared _check_length_scale Kernel NormalizedKernelMixin ConstantKernel DotProduct KernelOperator Sum Hyperparameter StationaryKernelMixin PairwiseKernel Matern CompoundKernel Exponentiation RBF RationalQuadratic WhiteKernel _approx_fprime sqrt MSE mean sqrt MSE backward print X zero_grad Adam GammaPrior ExactMarginalLogLikelihood sample sqrt grad exit append train step range getNoise transpose mm squeeze getK_X getAmplitude getAlphas shape enumerate unsqueeze inverse eye zeros range cuda X cat len svd T divide ravel sqrt kron astype zeros f range len | # Cool_MTGP ## A CONDITIONAL ONE-OUTPUT LIKELIHOOD FORMULATION FOR MULTITASK GAUSSIAN PROCESSES Authors: Óscar García Hinde ([email protected]) Vanessa Gómez Verdejo ([email protected]) Manel Martínez Ramón ([email protected]) This repository provides libraries and code to use the model proposed in the original paper, as well as replicate most of the results. The repository is still very much a work in progress. The datasets in /datasets/real/ were downloaded from http://mulan.sourceforge.net/datasets-mtr.html WHAT WORKS: | 802 |
OIdiotLin/DeepLab-pytorch | ['semantic segmentation'] | ['DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs'] | src/networks/deeplabv3.py src/libs/dense.py src/utils/utils.py src/libs/functions.py src/libs/__init__.py src/libs/bn.py src/dataset/datasets.py src/libs/residual.py src/libs/_ext/__init__.py src/train.py src/utils/loss.py src/libs/misc.py src/utils/encoding.py src/evaluate.py src/utils/criterion.py src/libs/build.py predict_multiscale predict_sliding get_palette get_confusion_matrix predict_whole main pad_image get_arguments set_bn_momentum lr_poly adjust_learning_rate set_bn_eval main str2bool get_arguments VOCDataTestSet CSDataSet CSDataTestSet VOCDataSet InPlaceABNSyncWrapper ABN InPlaceABNSync InPlaceABNWrapper InPlaceABN _pair DenseModule _act_forward _count_samples _broadcast_shape InPlaceABNSync _check_contiguous InPlaceABN _reduce _check _act_backward GlobalAvgPool2d IdentityResidualBlock _import_symbols ASPPModule ResNet Bottleneck conv3x3 Res_Deeplab CriterionDSN CriterionOhemDSN CallbackContext allreduce AllReduce DataParallelModel _criterion_parallel_apply execute_replication_callbacks Reduce patch_replication_callback DataParallelCriterion OhemCrossEntropy2d inv_preprocess decode_labels decode_predictions add_argument ArgumentParser range pad int isinstance print transpose min shape Upsample cuda ceil zeros range max pad_image net isinstance transpose from_numpy Upsample cuda net data zoom print copy shape predict_whole zeros float bincount zeros astype range diag CSDataSet DataLoader save argmax cuda Res_Deeplab fromarray data_dir map load_state_dict restore_from putpalette whole sum get_arguments asarray mean eval enumerate load data_list print maximum Upsample get_palette split zeros numpy gpu makedirs num_steps power learning_rate lr_poly eval __name__ __name__ model DataParallelModel zero_grad SGD adjust_learning_rate DataParallelCriterion CriterionDSN str default_timer state_dict num_steps SummaryWriter format copy float join criterion backward Variable snapshot_dir CriterionOhemDSN add_scalar ohem train step isinstance fn append size enumerate size enumerate slope elu_cuda _check leaky_relu_cuda elu_inv_cuda leaky_relu_backward_cuda elu_backward_cuda slope _check leaky_relu_cuda dir _wrap_function getattr append callable ResNet join isinstance _worker len start is_grad_enabled append range Lock list hasattr __data_parallel_replicate__ modules enumerate len replicate load new shape zeros numpy array range enumerate load isinstance concatenate new shape numpy append zeros argmax array range enumerate uint8 astype shape zeros numpy range | # DeepLab-pytorch > 人工智能课程大作业 - 论文翻译及实现 ## 论文 选读论文 **DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs** - [英文原版（arXiv）](https://arxiv.org/pdf/1606.00915.pdf) - [英文原版（IEEE）](https://ieeexplore.ieee.org/document/7913730) - [中文翻译](https://github.com/OIdiotLin/DeepLab-pytorch/blob/master/paper/DeepLab-ZH/top.pdf) | 803 |
OSU-slatelab/BMASS | ['word embeddings'] | ['Insights into Analogy Completion from the Biomedical Domain'] | lib/logging.py lib/replacer.py lib/__init__.py BMASS/settings.py analogy_task/analogy_model.py lib/prm.py analogy_task/task.py lib/preprocessing.py analogy_task/experiments_for_paper.py lib/embeddings/common.py analogy_task/embedding_wrapper.py lib/embeddings/glove.py lib/ir_metrics.py demo/config.py lib/embeddings/__init__.py lib/embeddings/word2vec.py BMASS/parser.py config.py lib/util.py Mode AnalogyModel EmbeddingWrapper _cli evaluate saveResults completeAnalogySet analogyTask convertAnalogyToMatrices read _parseLine _readMultipleEntries _AP_RR ReciprocalRank AP_RR MeanReciprocalRank _testmetrics AveragePrecision ProgressTracker log Timer _normalizeWrapper normalizePhone normalizeURLs normalizeNumeric normalizeEmail _tests tokenize PersistentResultsMatrix replacer matchesRegex parallelExecute rollDie flatten transformListToDict expectKey toCSV coinflip reverseDict transformDict dump writeList replace bitflag readlines XMLValue writeCSV prepareForParallel XMLAttribute laxIncrement readList readCSV Mode Format getFileSize read GloveMode read write _readTxt _readBin _getFileSize load unitNorm read listVocab splitVocabAndEmbeddings NearestNeighbors write closestNeighbor analogyQuery readVocab float range save stopTimer items read norm sort keys set mean EmbeddingWrapper reverse array append clean readList analogyTask startTimer split ALL_INFO add_option OptionParser exit print_help SINGLE_ANSWER MULTI_ANSWER parse_args convertAnalogyToMatrices track tick eval flushTracker append max range len index append writeln range len items read format write close completeAnalogySet stopTimer asArray AnalogyModel startTimer Session open append join cui_str _AP_RR _AP_RR append ReciprocalRank len set print AveragePrecision header set stdout remove strip extend lower encode suball append split normalizePhone _normalizeWrapper sub method split header test close write open close open readlines dump toCSV extend join dump readCSV tfrm tfrm keys append items prepare append int range len start join match append extend findall index findall tell seek int len close copy getFileSize append float array range open _readTxt _readBin append split close open tell seek decode read seek close index append array open get list tofile close write_str keys flush open read argsort array items norm array append tuple keys | # BMASS Source code used for ACL BioNLP 2017 workshop paper: Denis Newman-Griffis, Albert M Lai, Eric Fosler-Lussier. [_Insights into Analogy Completion from the Biomedical Domain_](http://web.cse.ohio-state.edu/~newman-griffis.1/papers/2017-BioNLP.pdf) ## Dataset [Download the dataset](http://slate.cse.ohio-state.edu/UTSAuthenticatedDownloader/index.html?dataset=BMASS) (requires a valid [UMLS Terminology Services](https://uts.nlm.nih.gov//license.html) login). ## Code + `analogy_task`: implementation of the analogy task (using TensorFlow v0.7) + `BMASS`: parser for BMASS data files + `lib`: various dependencies A demo virtual machine setup is also included in the `demo` directory, using [Vagrant](https://www.vagrantup.com/). This will run the analogy experiment for the full | 804 |
OSU-slatelab/geometric-embedding-properties | ['word embeddings'] | ['Characterizing the impact of geometric properties of word embeddings on task performance'] | affine/rand_linear.py affine/rref.py CDE/ae.py affine/transform.py NNE/generate_graph.py NNE/nearest_neighbors.py NNE/remap_embeddings_to_words.py affine/preprocessing.py NNE/nn_saver.py affine/config.py affine/affine_funcs.py affine/pose.py affine/extension_swap.py CDE/emb_modulus.py CDE/get_dist_vecs.py CDE/convert_embedding.py CDE/save_first_n.py CDE/rand_vecs.py CDE/next_batch.py CDE/logs/log_cleaner.py CDE/preprocessing.py uniform_scale rotate_2D shear translation homothetic reflect transflow parse_args get_config check_valid_file check_valid_dir subset_embedding loadGloveModel process_embedding parse_args genflow rand_linear parse_args genflow trainflow parse_args epoch mkproc main parse_args read_embedding check_valid_file runflow check_valid_dir compute_modulus parse_args check_valid_file process_embedding parse_args epoch genflow mkproc next_batch check_valid_file check_valid_dir subset_embedding get_embedding_dict loadGloveModel process_embedding parse_args epoch genflow mkproc parse_args saveflow readNeighbors writeGraph buildGraph _cli NearestNeighbors writeNodeMap _SIGNALS _cli readNodeMap _nn_writer _threadedNeighbors KNearestNeighbors _prepareForParallel _cli readVocab append enumerate flush tqdm append enumerate flush tqdm append enumerate flush tqdm tqdm dot append flush enumerate str print multiply transpose cos identity dot shape sin array flush len print len exit isfile check_valid_file process_embedding norm ones pi sqrt append zeros array print exit print exit print split open array len update Binary read items print exit Text array flush len read print exit write len print rand matmul abspath rand_linear check_valid_file str check_valid_dir process_embedding strftime shape sleep range update get_config flush join print write now tqdm len append func enumerate name get print flush start Process initializer qsize act_func put Saver abspath reset_default_graph abs check_valid_file str Process check_valid_dir transpose strftime placeholder matmul shape terminate cast sleep expand_dims range variance_scaling_initializer global_variables_initializer dropout relu shuffle copy Manager start Queue flush join norm constant minimize print Variable now float32 tqdm AdamOptimizer reduce_mean Word2Vec isfile zeros process_embedding print read exit len print read_embedding write reset_index Series tolist as_matrix DataFrame print norm average norm print shape compute_modulus expand_dims check_valid_file process_embedding initializer qsize act_func put Saver reset_default_graph abs Process transpose exit placeholder matmul cast expand_dims variance_scaling_initializer global_variables_initializer dropout relu copy Manager start Queue norm constant minimize Variable float32 AdamOptimizer reduce_mean Word2Vec isfile zeros name int print flush Binary read print exit Text len update norm rand write tqdm shape append range len int subset_embedding check_valid_file get items track readNeighbors float len flushTracker writeln tick add_option OptionParser exit print_help parse_args Process list format join parallelExecute HALT put start Queue append writeln _prepareForParallel range len append int range len get join track write tick flushTracker open nearestNeighbors NearestNeighbors Session put range len tuple sort list keys addCLIOption | # geometric-embedding-properties Source code and detailed results for - Whitaker et al, "[Characterizing the impact of geometric properties of word embeddings on task performance](https://arxiv.org/abs/1904.04866)." In Proceedings of RepEval 2019. This code is released under MIT License. If you use it in your own work, please cite the following paper: ``` @inproceedings{Whitaker2019RepEval, author = {Whitaker, Brendan and Newman-Griffis, Denis and Haldar, Aparajita and Ferhatosmanoglu, Hakan and Fosler-Lussier, Eric}, title = {Characterizing the impact of geometric properties of word embeddings on task performance}, booktitle = {Proceedings of the Third Workshop on Evaluating Vector Space Representations for NLP (RepEval)}, year = {2019} | 805 |
OSU-slatelab/mimic-enhance | ['speech enhancement'] | ['Phonetic Feedback for Speech Enhancement With and Without Parallel Speech Data'] | train.py embedding.py discriminator.py sdr.py aecnn.py test.py trainer.py data_io.py resnet.py generate_wav.py plot_wav.py AECNN wav_dataset mag Discriminator Embedding main make_filename run_test print_wav print_spec energy zero_crossings main run_test conv_block ResNet conv_layer permute_si_sdr pow_np_norm si_sdr remove_dc pow_norm main run_test main parse_args run_training normalize truncate_and_l1 Trainer get_gram_matrix truncate_and_ce stft sqrt squeeze load gcheckpoints eval load_state_dict wav_dataset phase range parse_args run_test T set_xlabel add_subplot axis close imshow flipud set_ylabel savefig figure range zeros_like len range zeros_like len arange plot add_subplot close energy zero_crossings savefig figure len print squeeze numpy cuda mean pow_norm pow_np_norm si_sdr mpretrain mcheckpoints Trainer save device wav_dataset gcheckpoints real_senone_file mfile load_state_dict to run_epoch range state_dict pretrain gfile is_available float load join time gpretrain print real_flist epochs items add_argument ArgumentParser run_training min view mean t mm std min min | # Speech Enhancement with Mimic Loss This project seeks to bring together the surprisingly separate worlds of speech enhancement and noise-robust ASR by applying phonetic knowledge to improve a front-end enhancement module. This can improve both intellegibility metrics (STOI and eSTOI) as well as ASR performed on the outputs of this enhancement system. The backbone of this project is work by Pandey et al. [1] which performs denoising in the time domain, but generates a loss in the spectral domain and backpropagates through the STFT to improve the denoising model. We apply mimic loss [2] in the spectral domain and backpropagate to the | 806 |
OSUPCVLab/VideoToTextDNN | ['video captioning'] | ['MTLE: A Multitask Learning Encoder of Visual Feature Representations for Video and Movie Description'] | create_movies.py model_attention.py data/util.py data/py3_process_features.py data/process_frames.py train_model.py metrics.py data/create_tacos.py data/create_mvad_mpii_lsmdc.py cocoeval.py data/create_msr_vtt.py data/subsect_videos.py common.py data/process_pca.py data/validate_feats.py data/create_trecvid.py data/create_skip_vectors.py data/create_y2t.py model_lstmdd.py config.py hyperband.py model_mtle.py download.py data/create_dataset.py data_engine.py score COCOScorer load_pkl test_cocoscorer flatten_list_of_list numpy_floatX sgd rmsprop get_rab_exp_path load_pkl adam grad_nan_report load_params adadelta dump_pkl zipp generate_minibatch_idx concatenate get_rab_dataset_base_path ortho_weight itemlist dropout_layer tanh norm_weight init_tparams get_two_rngs unzip linear create_dir_if_not_exist rectifier load_txt_file createVideo prepare_path_and_create_video _validate check_rvid_path copy_frames_and_draw_overlay main drawOverlay resizeImage prepare_data Movie2Caption test_data_engine video_vtt video_mvad download_mvad video_mpii download_video video_trecvid download_vine get_random_hyperparameter_configuration args_as_typed HYPERBAND run_then_return_val_loss save_test_samples_lsmdc save_blind_test_samples test_cocoeval_vtt gen_model test_cocoeval save_test_samples_acm_trecvid_y2t save_test_samples_youtube2text save_samples update_params compute_score generate_sample_gpu_single_process score_with_cocoeval save_test_samples_vtt build_sample_pairs train_from_scratch Attention validate_options _p train_from_scratch Attention validate_options _p train_from_scratch Attention validate_options _p main set_config train_from_scratch convert_from_string create_commands create_command_files validate remove_pickle_files load_annots_vtt get_annots_test_vtt _validate get_annots_train_val_vtt get_annots_vtt vtt get_features_from_dir get_annots_mvad lsmdc16 mvad mpii create_dictionary get_annots_lsmdc get_blind_lsmdc get_human_annotations tokenize_cap main tacos get_annots_tacos build_ground_truth_dict get_features_from_dir get_annots_trecvid trecvid build_ground_truth_dict y2t _validate get_annots_y2t fix_feature_file_names get_features_from_dir get_features_from_pkl main process_vid main extract_and_write_pca gather_feats process_batches init_model create_batches diff_feats extract_features general_case tacos do_command load_c3d_feat pad_frames mkdirs_safe create_line load_pkl create_dictionary get_sub_frames extract_frames_equally_spaced dump_pkl go load open print compute_score method zip score COCOScorer RandomState MRG_RandomStreams set_subtensor ndim zeros sum range items list set_value OrderedDict items list get_value binomial switch shape OrderedDict shared list items svd randn ortho_weight randn load items list items list set_printoptions OrderedDict mean isnan append sum function list function sqr get_value sqrt zip append shared numpy_floatX values function function dump open list asarray print range split print makedirs readlines close open print format system int truetype Draw print text rectangle save open len print format system load join format vidpath dict_path print isdir close map test rpath check_rvid_path mkdirs_safe append listdir Pool enumerate open mkdirs_safe join createVideo str str join format print system listdir drawOverlay resizeImage len max get_video_features clean_sequences asarray get_ctx_mask len astype get_z_seq append get_words enumerate split time print prepare_data kf_train Movie2Caption system int join format move print json_path end makedirs close map start filter dst_dir mkdir append listdir Pool len join int read chr print len write close urlopen mkdir info open print format dst_dir system join close map fill_info_list dst_dir mkdir Pool print system join close map fill_info_list dst_dir mkdir Pool join format replace print system args_as_typed isfile open makedirs int join format list logeta print close extend reversed floor ceil Pool range len get items RandomStreams asarray excluding build_sampler init_tparams set_value print list _gencap float32 _shared put init_params keys items OrderedDict zip load join video_feature sorted data_dir test_ids write close OrderedDict dict model_type mkdir append signature open video_feature sorted test_ids write close OrderedDict model_type mkdir append signature open video_feature sorted test_ids write close OrderedDict model_type mkdir append signature open join video_feature list sorted write close OrderedDict model_type load_pkl mkdir append signature keys open join video_feature list sorted OrderedDict model_type load_pkl mkdir append signature keys score test_ids OrderedDict valid_ids COCOScorer print test_ids valid_ids sample btest_ids build_sample_pairs score_with_cocoeval generate_sample_gpu_single_process save_blind_test_samples print save_test_samples_acm_trecvid_y2t save_test_samples_youtube2text generate_sample_gpu_single_process save_test_samples_vtt save_test_samples_lsmdc print test_ids valid_ids load_txt_file score_with_cocoeval Movie2Caption build_sample_pairs print test_ids valid_ids load_txt_file score_with_cocoeval Movie2Caption build_sample_pairs warn time print train Attention predict format timer lstmdd mtle isinstance convert_from_string model seed argv reload_ load_pkl dump_pkl save_model_dir erase_history random_seed setattr int join gethostname system create_dir_if_not_exist from_dir set_config set_config train_from_scratch seed join skip_thoughts format base_path test set add create_line warning create_command_files join format out info get_annots_train_val_vtt get_annots_test_vtt join word_tokenize format str int debug shuffle warn lower info encode append len join word_tokenize format str debug shuffle warn lower info encode append len load int join load_c3d_feat str info exists enumerate len format info set intersection len validate remove_pickle_files ArgsFaker feats_dir exists list feats_testing_dir sorted do_skip_thoughts with_sentences pkl_dir exit create_dictionary input get_features_from_dir protocol dump_pkl format json_dir test eval get_annots_vtt version mkdir info main type keys listdir join remove load_annots_vtt remove exists join format feats_testing_dir json_dir exit feats_dir info type exists critical OrderedDict decode word_tokenize join replace lower info encode enumerate OrderedDict split int unit_test join list data_dir pkl_dir load_pkl mkdir get_annots_lsmdc info create_dictionary get_blind_lsmdc keys dump_pkl decode feats_dir copy2 open word_tokenize data_dir pkl_dir load_pkl create_dictionary encode append dump_pkl unit_test local_dir replace readlines close shuffle lower mkdir info enumerate join makedirs split feats_dir copy2 list data_dir pkl_dir load_pkl create_dictionary dump_pkl unit_test local_dir readlines shuffle mkdir info keys enumerate int join get_annots_mvad makedirs join decode word_tokenize join lower encode list load_model dump_pkl captions_file load_pkl output_file encode keys len int decode word_tokenize join replace str print len strip shuffle lower encode float enumerate append split join split open join list gt_dir splits print pkl_dir test build_ground_truth_dict feats_dir mkdir create_dictionary listdir keys get_annots_tacos dump_pkl int decode word_tokenize join replace print len strip extend shuffle lower encode float enumerate append split print gt_dir ArgsFaker build_ground_truth_dict feats_dir list splits do_skip_thoughts pkl_dir create_dictionary get_features_from_dir dump_pkl protocol format test mkdir info main type keys join get_annots_trecvid print int list decode word_tokenize replace join print strip shuffle extend lower split encode float keys enumerate append len load open join format print write rename append listdir open remove_pickle_files ArgsFaker feats_dir exists values open list do_skip_thoughts pkl_dir exit fix_feature_file_names create_dictionary input get_features_from_dir protocol dump_pkl format json_dir from_pkl test set eval mkdir main listdir type keys get_features_from_pkl load join remove load_annots_vtt isdir print get_annots_y2t print int end prepend start dst_dir mkdir src_dir print format system mkdir str print write listdir flush len feats_testing_dir test_pkl extract_and_write_pca add_argument exit pca_test_dir print_help feats_dir train_pkl ArgumentParser parse_args type pca_dir join load_c3d_feat str isdir print len exit rmtree load_pkl open mkdir save transform listdir enumerate gather_feats fit eval LoadImage TransformImage is_available cuda init_model feats_dir warning create_batches frames_dir exists gpu_list autofill format process_batches debug choice mkdir diff_feats info listdir type int join work sort format AvgPool2d relu cpu view size info concatenate avg_pool ReLU is_available features cuda enumerate append len tf_img_fn list format Variable tuple min load_img_fn input_size warning info append zeros array range enumerate len listdir keys set system endswith loads dst_dir Pool open map append src_dir format close start mkdir float listdir join read int annots_path print end isfile endswith dst_dir Pool open map split append src_dir format close start float int frame_to_timeestamp annots_path end isfile makedirs print concatenate asarray len list array_split range len extract_frames_equally_spaced pad_frames join print sort exit range fromfile zeros listdir get_sub_frames exists len makedirs feats_dir warning frames_dir values st_size add append format stat set info listdir keys join remove print min rm_nil len | # MTLE This is the latest version of our code described in our [paper](https://arxiv.org/abs/1809.07257). An earlier version of our code was used at LSMDC17 where we won the movie description task. ## Dependencies These are the general, high-level dependencies: - CUDA-capable GPU(s) - Large storage medium for dataset videos (for re-creating results) - Python 2.7 + Python 3.5 (both required, more info below) For Python 2.7: - `Theano 0.8.1` - `cuDNN 5.4` | 807 |
OasisYang/HPM | ['person re identification'] | ['Horizontal Pyramid Matching for Person Re-identification'] | models/__init__.py models/HPM.py models/ResNet.py global_pcb weight_init HPM weights_init_classifier spp_vertical pcb_block weights_init_kaiming ResNet resnet50 Bottleneck resnet152 conv3x3 resnet34 resnet18 BasicBlock resnet101 init_model get_names data constant normal_ kaiming_normal_ __name__ constant_ data normal_ __name__ isinstance fill_ out_channels Conv2d normal_ sqrt zero_ BatchNorm2d Linear ModuleList weight_init append size range view pool view relu fc size conv append bn load_url ResNet load_state_dict load_url ResNet load_state_dict load_url ResNet load_state_dict load_url ResNet load_state_dict load_url ResNet load_state_dict | # Horizontal Pyramid Matching for Person Re-identification(HPM) ### Citing HPM This repository contains the the core source codes of proposed HPM, which may help you to reproduce the performance reported in the paper. If you find this repository or the HPM approach useful in your research, please consider citing: @article{fu2018horizontal, title={Horizontal Pyramid Matching for Person Re-identification}, author={Fu, Yang and Wei, Yunchao and Zhou, Yuqian and Shi, Honghui and Huang, Gao and Wang, Xinchao and Yao, Zhiqiang and Huang, Thomas}, journal={AAAI}, year={2019} } | 808 |
Ohraincu/JDNet | ['rain removal', 'single image deraining'] | ['Joint Self-Attention and Scale-Aggregation for Self-Calibrated Deraining Network'] | code/base/rain100L/config/function/functions/utils.py code/diff_loss/mae/config/function/functions/subtraction_refpad.py code/base/rain100H/config/model.py code/base/rain100L/config/function/functions/subtraction_refpad.py code/diff_loss/mse/config/function/functions/subtraction2_zeropad.py code/diff_loss/mse/config/function/functions/utils.py code/base/rain100L/config/dataset.py code/ablation/r1/config/compile.py code/ablation/r1/config/show.py code/base/rain100L/config/function/functions/aggregation_zeropad.py code/base/rain100L/config/function/functions/subtraction2_refpad.py code/diff_loss/mae/config/function/functional.py code/diff_loss/mse/config/function/functions/aggregation_zeropad.py code/diff_loss/mse/config/show.py code/ablation/r1/config/train.py code/base/rain100H/config/dataset.py code/diff_loss/mse/config/dataset.py code/base/rain100L/config/model.py code/base/rain100L/config/function/modules/subtraction.py code/base/rain100H/config/function/functions/aggregation_zeropad.py code/diff_loss/mae/config/train.py code/base/rain100L/config/function/functional.py code/diff_loss/mse/config/cal_ssim.py code/base/rain100H/config/function/functional.py code/base/rain100L/config/show.py code/base/rain100H/config/function/functions/subtraction2_zeropad.py code/base/rain100L/config/function/functions/subtraction2_zeropad.py code/diff_loss/mae/config/function/modules/subtraction2.py code/diff_loss/mse/config/model.py code/ablation/r2/config/compile.py code/base/rain100H/config/function/modules/aggregation.py code/base/rain100L/config/function/functions/__init__.py code/base/rain100L/config/function/functions/aggregation_refpad.py code/ablation/r1/config/eval.py code/ablation/r2/config/model.py code/base/rain100H/config/compile.py code/base/rain100L/config/compile.py code/diff_loss/mae/config/function/functions/aggregation_zeropad.py code/base/rain100H/config/function/functions/subtraction2_refpad.py code/diff_loss/mae/config/function/functions/__init__.py code/diff_loss/mse/config/function/functions/subtraction_refpad.py code/diff_loss/mae/config/compile.py code/base/rain100H/config/function/functions/subtraction_refpad.py code/diff_loss/mae/config/cal_ssim.py code/diff_loss/mse/config/train.py code/base/rain100H/config/function/modules/subtraction2.py code/diff_loss/mae/config/function/modules/subtraction.py code/diff_loss/mse/config/settings.py code/ablation/r2/config/dataset.py code/diff_loss/mae/config/settings.py code/ablation/r2/config/eval.py code/base/rain100H/config/eval.py code/ablation/r1/config/cal_ssim.py code/base/rain100L/config/function/modules/subtraction2.py code/base/rain100H/config/function/functions/subtraction_zeropad.py code/base/rain100L/config/train.py code/base/rain100L/config/function/modules/aggregation.py code/base/rain100H/config/function/functions/utils.py code/base/rain100H/config/cal_ssim.py code/ablation/r1/config/settings.py code/diff_loss/mse/config/function/modules/aggregation.py code/diff_loss/mae/config/function/functions/subtraction2_zeropad.py code/base/rain100H/config/train.py code/base/rain100H/config/function/modules/subtraction.py code/diff_loss/mse/config/eval.py code/diff_loss/mae/config/function/functions/subtraction2_refpad.py code/diff_loss/mae/config/function/modules/__init__.py code/base/rain100L/config/function/modules/__init__.py code/diff_loss/mse/config/function/modules/subtraction.py code/base/rain100H/config/function/modules/__init__.py code/diff_loss/mse/config/function/modules/__init__.py code/ablation/r2/config/cal_ssim.py code/diff_loss/mae/config/function/functions/aggregation_refpad.py code/diff_loss/mae/config/function/functions/subtraction_zeropad.py code/base/rain100L/config/eval.py code/diff_loss/mae/config/model.py code/diff_loss/mse/config/function/functions/aggregation_refpad.py code/diff_loss/mse/config/function/functional.py code/diff_loss/mse/config/function/functions/__init__.py code/base/rain100H/config/show.py code/base/rain100H/config/function/functions/aggregation_refpad.py code/diff_loss/mae/config/eval.py code/diff_loss/mae/config/show.py code/diff_loss/mse/config/compile.py code/base/rain100H/config/function/functions/__init__.py code/ablation/r1/config/model.py code/base/rain100L/config/cal_ssim.py code/ablation/r2/config/show.py code/diff_loss/mse/config/function/functions/subtraction_zeropad.py code/ablation/r2/config/train.py code/diff_loss/mse/config/function/functions/subtraction2_refpad.py code/base/rain100H/config/settings.py code/diff_loss/mse/config/function/modules/subtraction2.py code/ablation/r2/config/settings.py code/base/rain100L/config/settings.py code/diff_loss/mae/config/function/modules/aggregation.py code/base/rain100L/config/function/functions/subtraction_zeropad.py code/ablation/r1/config/dataset.py code/diff_loss/mae/config/function/functions/utils.py code/diff_loss/mae/config/dataset.py create_window gaussian _ssim SSIM ssim ShowDataset TrainValDataset TestDataset ensure_dir PSNR Session run_test ODE_DerainNet Scale_attention DenseConnection Residual_Block run_show ensure_dir PSNR Session run_train_val ensure_dir PSNR Session create_window gaussian _ssim SSIM ssim ShowDataset TrainValDataset TestDataset ensure_dir PSNR Session run_test DenseConnection SCConv Scale_attention ODE_DerainNet Residual_Block SCBottleneck run_show ensure_dir PSNR Session run_train_val ensure_dir PSNR Session create_window gaussian _ssim SSIM ssim ShowDataset TrainValDataset TestDataset ensure_dir PSNR Session run_test DenseConnection SCConv Bottleneck Scale_attention ODE_DerainNet position Residual_Block SAM SCBottleneck run_show ensure_dir PSNR Session run_train_val ensure_dir PSNR Session subtraction2 aggregation subtraction GET_BLOCKS test_aggregation_refpad aggregation_refpad AggregationRefpad GET_BLOCKS aggregation_zeropad AggregationZeropad test_aggregation_zeropad test_subtraction2_refpad GET_BLOCKS Subtraction2Refpad subtraction2_refpad GET_BLOCKS test_subtraction2_zeropad Subtraction2Zeropad subtraction2_zeropad SubtractionRefpad GET_BLOCKS subtraction_refpad test_subtraction_refpad SubtractionZeropad GET_BLOCKS test_subtraction_zeropad subtraction_zeropad load_kernel Dtype Aggregation Subtraction Subtraction2 create_window gaussian _ssim SSIM ssim ShowDataset TrainValDataset TestDataset ensure_dir PSNR Session run_test DenseConnection SCConv Bottleneck Scale_attention ODE_DerainNet position Residual_Block SAM SCBottleneck run_show ensure_dir PSNR Session run_train_val ensure_dir PSNR Session subtraction2 aggregation subtraction GET_BLOCKS test_aggregation_refpad aggregation_refpad AggregationRefpad GET_BLOCKS aggregation_zeropad AggregationZeropad test_aggregation_zeropad test_subtraction2_refpad GET_BLOCKS Subtraction2Refpad subtraction2_refpad GET_BLOCKS test_subtraction2_zeropad Subtraction2Zeropad subtraction2_zeropad SubtractionRefpad GET_BLOCKS subtraction_refpad test_subtraction_refpad SubtractionZeropad GET_BLOCKS test_subtraction_zeropad subtraction_zeropad load_kernel Dtype Aggregation Subtraction Subtraction2 create_window gaussian _ssim SSIM ssim ShowDataset TrainValDataset TestDataset ensure_dir PSNR Session run_test DenseConnection SCConv Bottleneck Scale_attention ODE_DerainNet position Residual_Block SAM SCBottleneck run_show ensure_dir PSNR Session run_train_val ensure_dir PSNR Session subtraction2 aggregation subtraction GET_BLOCKS test_aggregation_refpad aggregation_refpad AggregationRefpad GET_BLOCKS aggregation_zeropad AggregationZeropad test_aggregation_zeropad test_subtraction2_refpad GET_BLOCKS Subtraction2Refpad subtraction2_refpad GET_BLOCKS test_subtraction2_zeropad Subtraction2Zeropad subtraction2_zeropad SubtractionRefpad GET_BLOCKS subtraction_refpad test_subtraction_refpad SubtractionZeropad GET_BLOCKS test_subtraction_zeropad subtraction_zeropad load_kernel Dtype Aggregation Subtraction Subtraction2 create_window gaussian _ssim SSIM ssim ShowDataset TrainValDataset TestDataset ensure_dir PSNR Session run_test DenseConnection SCConv Bottleneck Scale_attention ODE_DerainNet position Residual_Block SAM SCBottleneck run_show ensure_dir PSNR Session run_train_val ensure_dir PSNR Session subtraction2 aggregation subtraction GET_BLOCKS test_aggregation_refpad aggregation_refpad AggregationRefpad GET_BLOCKS aggregation_zeropad AggregationZeropad test_aggregation_zeropad test_subtraction2_refpad GET_BLOCKS Subtraction2Refpad subtraction2_refpad GET_BLOCKS test_subtraction2_zeropad Subtraction2Zeropad subtraction2_zeropad SubtractionRefpad GET_BLOCKS subtraction_refpad test_subtraction_refpad SubtractionZeropad GET_BLOCKS test_subtraction_zeropad subtraction_zeropad load_kernel Dtype Aggregation Subtraction Subtraction2 Tensor Variable contiguous unsqueeze pow conv2d create_window size type_as get_device cuda is_cuda makedirs mean shape clip get_dataloader items inf_batch print size eval load_checkpoints info Session enumerate get_dataloader inf_batch eval load_checkpoints info save_image Session enumerate inf_batch save_image Session open str tensorboard load_checkpoints_net next one_epoch get_dataloader save_checkpoints_net close get_test_dataloader eval info enumerate int print write inf_batch_test train step unsqueeze repeat aggregation_zeropad aggregation_refpad is_cuda subtraction_zeropad subtraction_refpad is_cuda subtraction2_refpad is_cuda subtraction2_zeropad is_cuda apply int view print aggregation_refpad Unfold ReflectionPad2d pow cuda is_cuda apply int aggregation_zeropad view print Unfold pow cuda is_cuda apply int view print Unfold ReflectionPad2d pow subtraction2_refpad cuda is_cuda apply int view print Unfold pow subtraction2_zeropad cuda is_cuda apply int view print Unfold ReflectionPad2d pow subtraction_refpad cuda is_cuda apply int view print subtraction_zeropad Unfold pow cuda DoubleTensor isinstance FloatTensor compile_with_cache substitute | # JDNet：Joint Self-Attention and Scale-Aggregation for Self-Calibrated Deraining Network [Cong Wang](https://supercong94.wixsite.com/supercong94)\*, [Yutong Wu](https://ohraincu.github.io/)\*, [Zhixun Su](http://faculty.dlut.edu.cn/ZhixunSu/zh_CN/index/759047/list/index.htm) †, Junyang Chen <\* Both authors contributed equally to this research. † Corresponding author.> This work has been accepted by ACM'MM 2020. [\[Arxiv\]](https://arxiv.org/abs/2008.02763) <div align=center> <img src="https://github.com/Ohraincu/JDNet/blob/master/fig/result.png" width="60%"> Fig1：An example from real-world datasets. </div> ## Abstract In this paper, we propose an effective algorithm, called JDNet, to solve the single image deraining problem and conduct the segmentation and detection task for applications. Specifically, considering the important information on multi-scale features, we propose a Scale-Aggregation module to learn the features with different scales. Simultaneously, Self-Attention module is introduced to match or outperform their convolutional counterparts, which allows the feature aggregation to adapt to each channel. Furthermore, to improve the basic convolutional feature transformation process of Convolutional Neural Networks (CNNs), Self-Calibrated convolution is applied to build long-range spatial and inter-channel dependencies around each spatial location that explicitly expand fields-of-view of each convolutional layer through internal communications and hence enriches the output features. By designing the Scale-Aggregation and Self-Attention modules with Self-Calibrated convolution skillfully, the proposed model has better deraining results both on real-world and synthetic datasets. Extensive experiments are con- ducted to demonstrate the superiority of our method compared with state-of-the-art methods. | 809 |
Olamyy/nass-ai | ['optical character recognition'] | ['NASS-AI: Towards Digitization of Parliamentary Bills using Document Level Embedding and Bidirectional Long Short-Term Memory'] | nassai.py code/custom.py code/utils.py code/mlp.py code/doc2vec.py code/sklearn_classifiers.py code/train.py code/keras_classifiers.py code/build.py code/preprocessing.py run nassai_cli Embedding LSTMClassifier BLSTM2DCNN YKimCNN FCholletCNN NassAIDoc2Vec KerasTextClassifier mlp_model MLP preprocess_data clean_text get_glove prep SklearnClassifierWrapper SVM RandomForest get_vectors MLP MLPCLF load_word2vec BernNB LinearSVM train prepare_data star fit_and_report save_model load_model MeanEmbeddingVectorizer get_path handle_format log_results show_report encode_label evaluate_and_log TfidfEmbeddingVectorizer f1 batch_generator format load_model echo get_path print Embedding predict update format print get_path train print Sequential add Dense summary Activation range compile Dropout join word_tokenize punctuation translate sub maketrans list print get_path to_csv index apply read_csv drop list format print set word_vec load_word2vec_format keys len print get_path load vectors print dict index2word zip fit_on_texts word_counts values get_path Tokenizer apply LabelEncoder fit_transform read_csv bill_class get time format isinstance pad_sequences TensorBoard EarlyStopping prepare_data set_up strftime fit_generator Pipeline startswith texts_to_matrix train_test_split batch_generator fit time format predict savez_compressed fit append TaggedDocument enumerate split format print classification_report f1_score accuracy_score print evaluate dump save format get_path to_categorical fit_transform LabelEncoder recall precision randint | # NASS-AI: Towards Digitization of Parliamentary Bills using Document Level Embedding and Bidirectional Long Short-Term Memory Code for [https://arxiv.org/abs/1910.04865]() A. Akinfaderin and O. Wahab. NASS-AI: Towards Digitization of Parliamentary Bills using Document Level Embedding and Bidirectional Long Short-Term Memory. To appear in the Proceedings of NeurIPS 2019 Workshop on Machine Learning for the Developing World, Vancouver, Canada, December 2019. 7 pages. ## Dataset Samples from Nigerian Parliamentary Bills  Three different bills showing some of the challenging quality of our parliamentary bills. Left: a bill to regulate local government elections. Center: a bill to prohibit the use of life bullets or Nigerian army to quell civil protests. Right: a bill to provide free screening and treatment of cancer and brain tumor.  Distribution of the class labels in our dataset. ### Link to Nigerian parliamentary bills from 1999-2018 in PDF format (pre-OCR) Dataset: [https://drive.google.com/open?id=19bxftHKcAe8Lq_yH3w8xNMVI9gVInQp8]() | 810 |
OlegArenz/O-NAIL | ['imitation learning'] | ['Non-Adversarial Imitation Learning and its Connections to Adversarial Methods'] | onail/ONAIL.py run_experiment.py models/ValueFunction.py models/Policy.py configs/offline_il_configs.py common/utlis.py overwrite_dict_with_kwargs set_seed get_final_experiment_config run_experiment argparser build_kwargs_from_command_line_args roll_out_policy process_demos process_trajectories boolean_string Policy ValueFunction ONAIL seed add_argument ArgumentParser list keys __dict__ dict overwrite_dict_with_kwargs parse_args keys __dict__ seed join make update set_seed max_iter strftime range dict path learn_one_step build_kwargs_from_command_line_args ONAIL _get_obs print squeeze hstack range render reset append step clip append zeros len convert_to_tensor std arange reshape float32 mean array len | # Offline Non-Adversarial Imitation Learning (O-NAIL) O-NAIL is an algorithm for offline imitation learning that is based on the non-adversarial formulation that is discussed in the article Non-Adversarial Imitation Learning and its Connections to Adversarial Methods. This repository contains supplementary code to reproduce the comparison with ValueDice as well as a script to obtain the expert dataset. ## Installation The python dependencies can be installed by executing (in a virtual environment) ``` pip3 install -r requirements.txt ``` As the experiments depend on MuJoCo, you need a valid license and setup [mujoco-py](https://github.com/openai/mujoco-py). | 811 |
Omairss/RepresentationLearning | ['graph clustering', 'link prediction'] | ['Variational Graph Auto-Encoders'] | src/layers.py src/input_data.py src/train.py src/model.py src/initializations.py src/optimizer.py src/preprocessing.py weight_variable_glorot parse_index_file load_data dropout_sparse get_layer_uid FullyConnectedDecoder GraphConvolution InnerProductDecoder GraphConvolutionSparse Layer Model GCNModelVAE GCNModelAE OptimizerAE OptimizerVAE mask_test_edges sparse_to_tuple preprocess_graph construct_feed_dict get_roc_score sqrt random_uniform append int strip open format lil_matrix from_dict_of_lists tolil tuple sort min adjacency_matrix parse_index_file max range len sparse_retain floor cast data shape transpose tocoo tocoo flatten coo_matrix array eye sum diags dict update int list ismember T eliminate_zeros ones sparse_to_tuple hstack csr_matrix shuffle delete dia_matrix floor append randint triu array range update T hstack z_mean average_precision_score dot sigmoid append roc_auc_score run | # RepresentationLearning ### Introduction This repository build on the work of https://github.com/tkipf/gae, (N., Thomas, and Max. “Variational Graph Auto-Encoders.” ArXiv.org, 21 Nov. 2016, arxiv.org/abs/1611.07308.) to learn representations of scientific citation data into a latent space. If latent space is then a representation of the the underlying *information* in the dataset, investigating the latent space and their correlations with features, we can begin to understand how the features interact with each other in a complex networks - such as the scientific citation network. ### Dataset 1. Citeseer 1. Web of Science ### Preliminary Results  | 812 |
Oneplus/Tweebank | ['part of speech tagging'] | ['Parsing Tweets into Universal Dependencies'] | converted/fixRoot.py | # Summary Tweebank v2 is a collection of English tweets annotated in Universal Dependencies that can be exploited for the training of NLP systems to enhance their performance on social media texts. # Introduction Tweebank v2 is built on the original data of Tweebank v1 (840 unique tweets, 639/201 for training/test set), along with an additional 210 tweets sampled from the POS-tagged dataset of Gimpel et al. (2011) and 2,500 tweets sampled from the Twitter stream from February 2016 to July 2016. The latter data source consists of 147.4M English tweets. In the same way as Kong et al. (2011), reference unit is always the tweet in its entirety -- which may thus consist of multiple sentences -- not the sentence alone. Before annotation, we use simple regular expression to anonymize username and URL. Our annotation process was conducted in two stages. In the first stage, 18 researchers worked on the Tweebank v1 | 813 |
OpenBioLink/OpenBioLink | ['link prediction'] | ['OpenBioLink: A benchmarking framework for large-scale biomedical link prediction'] | src/openbiolink/graph_creation/file_processor/edge/edgeStringActivationProcessor.py src/openbiolink/graphProperties.py src/openbiolink/graph_creation/file_processor/edge/edgeBgeeUnderExprProcessor.py src/openbiolink/graph_creation/file_reader/onto/ontoUberonReader.py test/train_test_split_tests/test_negativeSampler.py src/openbiolink/graph_creation/file_reader/edge/edgeStitchReader.py src/openbiolink/graph_creation/file_reader/edge/edgeStringReader.py src/openbiolink/graph_creation/metadata_edge/onto/edgeMetaGoOntoIsA.py src/openbiolink/graph_creation/types/dbType.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaGeneBindActGene.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStitchBinding.py src/openbiolink/graph_creation/file_processor/edge/edgeHpaProcessor.py src/openbiolink/graph_creation/metadata_infile/mapping/inMetaMapOntoDoUmls.py src/openbiolink/cli.py src/openbiolink/graph_creation/file_reader/edge/edgeHpaReader.py src/openbiolink/gui/splitFrame.py src/openbiolink/graph_creation/file_processor/edge/edgeBgeeOverExprProcessor.py src/openbiolink/graph_creation/file_processor/edge/edgeStitchBindInhProcessor.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaDrugBindInhGene.py src/openbiolink/graph_creation/file_processor/mapping/__init__.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStringBinding.py src/openbiolink/graph_creation/file_processor/edge/edgeStringBindActProcessor.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaDrugInhibitionGene.py src/openbiolink/graph_creation/file_processor/mapping/mapDrugCentralPubchemProcessor.py src/openbiolink/graph_creation/file_reader/edge/edgeStringActionReader.py src/openbiolink/graph_creation/metadata_infile/mapping/inMetaMapDrugCentralPubchem.py src/openbiolink/graph_creation/file_reader/edge/edgeTnHpoDisReader.py src/openbiolink/graph_creation/file_reader/onto/ontoDoReader.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaGeneOverAna.py src/openbiolink/graph_creation/metadata_edge/edge/tnEdgeMetaGeneUnderAna.py src/openbiolink/graph_creation/metadata_db_file/edge/dbMetaEdgeSiderInd.py src/openbiolink/graph_creation/metadata_db_file/mapping/dbMetaMapString.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaDrugBindingGene.py src/openbiolink/graph_creation/metadata_db_file/mapping/dbMetaMapDisGeNet.py src/openbiolink/graph_creation/file_reader/edge/edgeSiderSeReader.py src/openbiolink/graph_creation/metadata_infile/mapping/inMetaMapOntoDoOmim.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeBgeeNoExpr.py src/openbiolink/graph_creation/file_processor/onto/ontoGoPartOfProcessor.py src/openbiolink/graph_creation/graph_writer/pickle_writer.py src/openbiolink/graph_creation/metadata_edge/edgeMetadata.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaDisPheno.py test/test_utils.py test/train_test_split_tests/test_trainTestSetCreation.py src/openbiolink/graph_creation/file_processor/edge/edgeSiderSeProcessor.py src/openbiolink/graph_creation/metadata_db_file/edge/dbMetaEdgeHpa.py src/openbiolink/graph_creation/metadata_edge/edge/tnEdgeMetaGeneAna.py src/openbiolink/graph_creation/file_reader/edge/edgeHpoGeneReader.py src/openbiolink/graph_creation/file_reader/edge/edgeDrugCentralReader.py src/openbiolink/graph_creation/metadata_db_file/dbMetadata.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeDrugCentral.py src/openbiolink/graph_creation/file_processor/onto_mapping/ontoMapDoAltidProcessor.py src/openbiolink/graph_creation/file_processor/mapping/mapDisGeNetProcessor.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeHpa.py src/openbiolink/graph_creation/file_processor/edge/__init__.py src/openbiolink/graph_creation/metadata_edge/onto/edgeMetaAnatomyOntoIsA.py src/openbiolink/nodeType.py src/openbiolink/graph_creation/file_processor/onto_mapping/ontoMapHpoAltidProcessor.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaGenePtmodGene.py src/openbiolink/graph_creation/file_processor/edge/edgeStringBindingProcessor.py src/openbiolink/graph_creation/metadata_db_file/edge/dbMetaEdgeHpoGene.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeDrugCentralContraInd.py src/openbiolink/graph_creation/metadata_db_file/onto/__init__.py src/openbiolink/graph_creation/metadata_infile/onto/inMetaOntoGoIsA.py src/openbiolink/graph_creation/file_reader/myCsvReader.py src/openbiolink/graph_creation/file_reader/onto/__init__.py src/openbiolink/graph_creation/file_reader/csvReader.py setup.py src/openbiolink/graph_creation/file_reader/edge/edgeGoReader.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaGenePheno.py src/openbiolink/graph_creation/file_processor/edge/edgeDisGeNetProcessor.py src/openbiolink/train_test_set_creation/ttsConfig.py src/openbiolink/train_test_set_creation/trainTestSplitCreation.py src/openbiolink/graph_creation/metadata_db_file/edge/dbMetaEdgeBgeeExpr.py src/openbiolink/graph_creation/metadata_infile/mapping/inMetaMapOntoHpoUmls.py src/openbiolink/obl2021/__init__.py test/evaluation_tests/test_safran_evaluation.py src/openbiolink/graph_creation/metadata_db_file/edge/dbMetaEdgeTnHpoDis.py src/openbiolink/graph_creation/file_processor/edge/edgeStringPtmodeProcessor.py src/openbiolink/graph_creation/metadata_db_file/edge/dbMetaEdgeStitch.py test/evaluation_tests/test_dglke_evaluation.py src/openbiolink/graph_creation/file_reader/onto/ontoGoReader.py src/openbiolink/graph_creation/file_reader/mapping/mapStringReader.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaDrugReactionGene.py src/openbiolink/graph_creation/metadata_infile/infileMetadata.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStitchPredBind.py src/openbiolink/gui/console.py src/openbiolink/graph_creation/file_reader/mapping/__init__.py src/openbiolink/graph_creation/file_processor/edge/edgeStringInhibitionProcessor.py src/openbiolink/graph_creation/metadata_infile/onto/__init__.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStitchBindAct.py src/openbiolink/graph_creation/graph_writer/bel_writer.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaGeneActivationGene.py test/graph_creation_tests/test_graphCreator.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStringCatalysis.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaGeneDis.py src/openbiolink/edgeType.py src/openbiolink/graph_creation/file_processor/edge/edgeSiderIndProcessor.py src/openbiolink/graph_creation/metadata_db_file/edge/dbMetaEdgeDrugCentral.py src/openbiolink/graph_creation/graphCreator.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaDrugExpressionGene.py src/openbiolink/graph_creation/file_reader/myOboReader.py src/openbiolink/graph_creation/file_reader/edge/__init__.py src/openbiolink/graph_creation/file_reader/mapping/mapUniprotReader.py src/openbiolink/graph_creation/file_processor/onto_mapping/ontoMapGoAltidProcessor.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeBgeeOverExpr.py src/openbiolink/graph_creation/file_writer/__init__.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStringInhibition.py src/openbiolink/graph_creation/file_processor/edge/edgeBgeeExprProcessor.py src/openbiolink/graph_creation/metadata_infile/onto/inMetaOntoGoPartOf.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStitch.py src/openbiolink/graph_creation/metadata_db_file/onto/dbMetaOntoGo.py src/openbiolink/graph_creation/file_reader/onto/ontoHpoReader.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaDisDrug.py src/openbiolink/gui/gui.py src/openbiolink/obl2021/obl2021.py src/openbiolink/graph_creation/file_processor/edge/edgeBgeeNoExprProcessor.py docs/source/conf.py src/openbiolink/graph_creation/metadata_edge/edgeRegularMetadata.py src/openbiolink/graph_creation/metadata_edge/onto/edgeMetaPhenoOntoIsA.py src/openbiolink/graph_creation/file_processor/mapping/mapUniUniNcbiProcessor.py src/openbiolink/graph_creation/file_processor/mapping/mapStringProcessor.py src/openbiolink/graph_creation/metadata_db_file/edge/dbMetaEdgeBgeeDiffExpr.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStitchExpression.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeString.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStringBindAct.py src/openbiolink/train_test_set_creation/sampler.py src/openbiolink/graph_creation/metadata_infile/mapping/inMetaMapUniUniNcbi.py src/openbiolink/graph_creation/file_processor/onto_mapping/ontoMapDoOmimProcessor.py src/openbiolink/graph_creation/file_reader/mapping/mapDisGeNetReader.py src/openbiolink/graph_creation/metadata_db_file/edge/dbMetadataEdge.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStringBindInh.py src/openbiolink/graph_creation/metadata_db_file/onto/dbMetaOntoHpo.py src/openbiolink/graph_creation/metadata_infile/mapping/inMetaMapOntoHpoAltid.py src/openbiolink/gui/confirmFrame.py src/openbiolink/openBioLink.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaGeneReactionGene.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeTnHpoDis.py src/openbiolink/graph_creation/metadata_edge/edge/tnEdgeMetaDisDrug.py src/openbiolink/graph_creation/metadata_edge/onto/edgeMetaGoOntoPartOf.py src/openbiolink/graph_creation/file_downloader/__init__.py src/openbiolink/graph_creation/file_reader/edge/edgeCdtPathReader.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeHpoDis.py src/openbiolink/graph_creation/metadata_db_file/onto/dbMetaOntoDo.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaDrugPredBindGene.py src/openbiolink/graph_creation/file_reader/postgresDumpReader.py src/openbiolink/graph_creation/file_reader/myPostgrasDumpReader.py src/openbiolink/graph_creation/metadata_db_file/onto/dbMetadataOnto.py src/openbiolink/graph_creation/metadata_db_file/mapping/__init__.py src/openbiolink/graph_creation/metadata_edge/edgeOntoMetadata.py src/openbiolink/graph_creation/file_downloader/fileDownloader.py src/openbiolink/graph_creation/metadata_infile/__init__.py src/openbiolink/graph_creation/file_processor/onto/ontoUberonIsAProcessor.py src/openbiolink/graph_creation/graph_writer/graphRDFWriter.py src/openbiolink/graph_creation/metadata_db_file/__init__.py src/openbiolink/graph_creation/metadata_edge/__init__.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeHpoGene.py src/openbiolink/graph_creation/metadata_infile/mapping/inMetaMapUniEnsNcbi.py src/openbiolink/graph_creation/types/qualityType.py src/openbiolink/graph_creation/file_processor/edge/edgeDrugCentralContraIndProcessor.py test/evaluation_tests/obl2021.py test/graph_creation_tests/test_fileProcessor.py src/openbiolink/graph_creation/file_processor/onto_mapping/ontoMapUberonAltidProcessor.py src/openbiolink/graph_creation/file_processor/edge/edgeStitchProcessor.py src/openbiolink/graph_creation/file_reader/edge/edgeStitchActionReader.py src/openbiolink/graph_creation/graph_writer/__init__.py src/openbiolink/graph_creation/graphCreation.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaGenePath.py src/openbiolink/graph_creation/file_processor/edge/edgeGoProcessor.py src/openbiolink/graph_creation/metadata_db_file/edge/dbMetaEdgeGo.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaGeneBindingGene.py src/openbiolink/graph_creation/metadata_edge/edge/tnEdgeMetaGeneOverAna.py src/openbiolink/graph_creation/metadata_db_file/mapping/dbMetaMapUniprot.py src/openbiolink/graph_creation/file_processor/edge/edgeStringCatalysisProcessor.py src/openbiolink/graph_creation/metadata_edge/tnEdgeRegularMetadata.py src/openbiolink/graph_creation/file_processor/onto_mapping/ontoMapHpoUmlsProcessor.py test/train_test_split_tests/test_sampler.py src/openbiolink/graph_creation/file_processor/edge/edgeStitchBindActProcessor.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStitchBindInh.py src/openbiolink/graph_creation/metadata_db_file/edge/dbMetaEdgeHpoDis.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStringPtmod.py src/openbiolink/graph_creation/graph_writer/base.py src/openbiolink/graph_creation/metadata_db_file/edge/dbMetaEdgeStringAction.py src/openbiolink/graph_creation/metadata_edge/edge/tnEdgeMetaDisPheno.py src/openbiolink/graph_creation/file_processor/edge/edgeStitchBindingProcessor.py src/openbiolink/graph_creation/metadata_infile/onto/inMetaOntoHpoIsA.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeGo.py src/openbiolink/graph_creation/file_reader/parser/postgresDumpParser.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStringExpression.py src/openbiolink/gui/graphCreationFrame.py src/openbiolink/graph_creation/file_processor/edge/edgeStringReactionProcessor.py src/openbiolink/graph_creation/metadata_db_file/edge/dbMetaEdgeCtdPath.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStitchInhibition.py src/openbiolink/graph_creation/file_processor/onto_mapping/__init__.py src/openbiolink/graph_creation/file_processor/edge/edgeTnHpoDisProcessor.py src/openbiolink/graph_creation/file_processor/onto/ontoUberonPartOfProcessor.py src/openbiolink/graph_creation/file_reader/edge/edgeDisGeNetReader.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStitchReaction.py src/openbiolink/train_test_set_creation/trainTestSetWriter.py test/evaluation_tests/test_evaluator.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaGeneGene.py src/openbiolink/graph_creation/file_processor/myFileProcessor.py src/openbiolink/graph_creation/metadata_infile/onto/inMetaOntoUberonPartOf.py src/openbiolink/globalConfig.py src/openbiolink/graph_creation/file_processor/onto/ontoDoIsAProcessor.py src/openbiolink/graph_creation/file_processor/onto/__init__.py src/openbiolink/graph_creation/file_processor/onto/ontoHpoIsAProcessor.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeBgeeExpr.py src/openbiolink/graph_creation/metadata_db_file/mapping/dbMetadataMapping.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStringActivation.py src/openbiolink/graph_creation/metadata_infile/mapping/inMetaMapOntoGoAltid.py src/openbiolink/gui/startPage.py src/openbiolink/utils.py src/openbiolink/graph_creation/metadata_infile/edge/__init__.py src/openbiolink/graph_creation/metadata_infile/mapping/__init__.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaDrugBindActGene.py src/openbiolink/graph_creation/file_processor/__init__.py src/openbiolink/graph_creation/metadata_db_file/edge/dbMetaEdgeDisGeNet.py src/openbiolink/graph_creation/metadata_edge/edge/__init__.py src/openbiolink/graph_creation/metadata_infile/onto/inMetaOntoDoIsA.py src/openbiolink/graph_creation/file_processor/edge/edgeStringBindInhProcessor.py src/openbiolink/graph_creation/file_processor/edge/edgeHpoGeneProcessor.py src/openbiolink/graph_creation/file_writer/fileWriter.py src/openbiolink/graph_creation/file_processor/edge/edgeStringExpressionProcessor.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeBgeeUnderExpr.py src/openbiolink/node.py src/openbiolink/cli_helper.py src/openbiolink/graph_creation/metadata_edge/onto/__init__.py src/openbiolink/graph_creation/file_reader/edge/edgeSiderIndReader.py test/evaluation_tests/obl2021_mwe.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeSiderSe.py src/openbiolink/graph_creation/metadata_infile/mapping/inMetaMapOntoDoAltid.py test/test_sources.py src/openbiolink/graph_creation/file_reader/oboReader.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStringReaction.py src/openbiolink/graph_creation/file_processor/edge/edgeStitchExpressionProessor.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaDrugPheno.py src/openbiolink/evaluation/evaluation.py src/openbiolink/graph_creation/metadata_edge/onto/edgeMetaAnatomyOntoPartOf.py src/openbiolink/edge.py src/openbiolink/graph_creation/graph_writer/graphTSVWriter.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaGeneInhibitionGene.py src/openbiolink/graph_creation/file_processor/edge/edgeStitchActivationProcessor.py src/openbiolink/graph_creation/metadata_db_file/edge/__init__.py src/openbiolink/evaluation/dataLoader.py src/openbiolink/graph_creation/file_processor/edge/edgeStitchInhibitionProcessor.py src/openbiolink/graph_creation/file_processor/edge/edgeStitchCatalysisProcessor.py src/openbiolink/graph_creation/file_processor/onto/ontoGoIsAProcessor.py src/openbiolink/graph_creation/metadata_infile/mapping/inMetaMapOntoUberonAltid.py src/openbiolink/graph_creation/types/readerType.py src/openbiolink/gui/tqdmbuf.py src/openbiolink/graph_creation/file_processor/edge/edgeDrugCentralIndProcessor.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaGeneExpressionGene.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaGeneDrug.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaGeneUnderAna.py src/openbiolink/graph_creation/file_processor/edge/edgeStringProcessor.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeDisGeNet.py src/openbiolink/graph_creation/metadata_infile/mapping/inMetaMapString.py src/openbiolink/graph_creation/metadata_edge/onto/edgeMetaDisOntoIsA.py src/openbiolink/graph_creation/file_processor/fileProcessor.py src/openbiolink/graph_creation/file_reader/__init__.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStitchCatalysis.py src/openbiolink/graph_creation/metadata_db_file/onto/dbMetaOntoUberon.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaGeneAna.py src/openbiolink/graph_creation/file_processor/edge/edgeHpoDisProcessor.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaDrugActivationGene.py src/openbiolink/graph_creation/file_processor/edge/edgeStitchReactionProcessor.py test/graph_creation_tests/test_infileMetadataSubclasses.py src/openbiolink/graph_creation/types/infileType.py src/openbiolink/__main__.py src/openbiolink/graph_creation/file_processor/edge/edgeStitchPredBindProcessor.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaGeneGo.py src/openbiolink/__init__.py src/openbiolink/graph_creation/file_processor/mapping/mapUniEnsNcbiProcessor.py src/openbiolink/graph_creation/metadata_db_file/edge/dbMetaEdgeString.py src/openbiolink/graph_creation/file_reader/mapping/mapDrugCentralPubchemReader.py src/openbiolink/graph_creation/file_reader/edge/edgeBgeeReader.py src/openbiolink/graph_creation/file_reader/parser/oboParser.py src/openbiolink/graph_creation/file_processor/edge/edgeCdtPathProcessor.py src/openbiolink/graph_creation/file_reader/edge/edgeHpoDisReader.py src/openbiolink/graph_creation/file_reader/fileReader.py test/graph_creation_tests/test_edgeMetadataSubclasses.py src/openbiolink/namespace.py src/openbiolink/graph_creation/file_processor/onto_mapping/ontoMapDoUmlsProcessor.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaGeneBindInhGene.py src/openbiolink/graph_creation/metadata_db_file/edge/dbMetaEdgeSiderSe.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaGeneCatalysisGene.py src/openbiolink/graph_creation/metadata_db_file/edge/dbMetaEdgeStitchAction.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeCdtPath.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeStitchActivation.py test/graph_creation_tests/test_dbMetaSubclasses.py src/openbiolink/graph_creation/graphCreationConfig.py src/openbiolink/graph_creation/metadata_infile/mapping/inMetaMapDisGeNet.py src/openbiolink/graph_creation/metadata_infile/onto/inMetaOntoUberonIsA.py src/openbiolink/graph_creation/file_reader/edge/edgeBgeeDiffReader.py src/openbiolink/graph_creation/metadata_infile/edge/inMetaEdgeSiderInd.py src/openbiolink/graph_creation/metadata_edge/edge/edgeMetaDrugCatalysisGene.py Cli create_graph Edge EdgeType Namespace Namespaces Node NodeType time rand gui handle_quality generate main split remove_reverse_edges calc_corrupted_triples remove_inconsistent_edges remove_parent_duplicates_and_reverses map_elements split_list_in_batches_iter get_diff url_exists get_leaf_subclasses _get_corrupted_examples make_undir create_mappings rgetattr cls_list_to_dic _group_corrupted_examples db_mapping_file_to_dic DataLoader Evaluator Graph_Creation GraphCreator FileDownloader FileProcessor MyFileProcessor EdgeSiderSeProcessor EdgeBgeeExprProcessor EdgeBgeeNoExprProcessor EdgeBgeeOverExprProcessor EdgeBgeeUnderExprProcessor EdgeCdtPathProcessor EdgeDisGeNetProcessor EdgeDrugCentralIndProcessor EdgeDrugCentralIndProcessor EdgeGoProcessor EdgeHpaProcessor EdgeHpoDisProcessor EdgeHpoGeneProcessor EdgeSiderIndProcessor EdgeStitchActivationProcessor EdgeStitchBindActProcessor EdgeStitchBindingProcessor EdgeStitchBindInhProcessor EdgeStitchCatalysisProcessor EdgeStitchExpressionProcessor EdgeStitchInhibitionProcessor EdgeStitchPredBindProcessor EdgeStitchProcessor EdgeStitchReactionProcessor EdgeStringActivationProcessor EdgeStringBindActProcessor EdgeStringBindingProcessor EdgeStringBindInhProcessor EdgeStringCatalysisProcessor EdgeStringExpressionProcessor EdgeStringInhibitionProcessor EdgeStringProcessor EdgeStringPtmodeProcessor EdgeStringReactionProcessor EdgeTnHpoDisProcessor MapDisGeNetProcessor MapDrugCentralPubchemProcessor MapStringProcessor MapUniEnsNcbiProcessor MapUniUniNcbiProcessor OntoDoIsAProcessor OntoGoIsAProcessor OntoGoPartOfProcessor OntoHpoIsAProcessor OntoUberonIsAProcessor OntoUberonPartOfProcessor OntoMapDoAltidProcessor OntoMapDoUmlsProcessor OntoMapDoUmlsProcessor OntoMapGoAltidProcessor OntoMapHpoAltidProcessor OntoMapHpoUmlsProcessor OntoMapUberonAltidProcessor CsvReader FileReader MyCsvReader MyOntoReader MapDrugCentralPubchemReader OboReader PostgresDumpReader EdgeBgeeDiffReader EdgeBgeeReader EdgeCdtPathReader EdgeDisGeNetReader EdgeDrugCentralReader EdgeGoReader EdgeHpaReader EdgeHpoDisReader EdgeHpoGeneReader EdgeSiderIndReader EdgeSiderSeReader EdgeStitchActionReader EdgeStitchReader EdgeStringActionReader EdgeStringReader EdgeTnHpoDisReader MapDisGeNetReader MapDrugCentralPubchemReader MapStringReader MapUniprotReader OntoDoReader OntoGoReader OntoHpoReader OntoUberonReader OboParser PostgresDumpParser FileWriter OpenBioLinkGraphWriter GraphWriter convert _get_type_to_adder GraphBELWriter _get_type_to_dsl GraphRDFWriter GraphTSVWriter GraphPickleWriter _get_subclasses_recursive DbMetadata DbMetadataEdge DbMetaEdgeBgeeDiffExpr DbMetaEdgeBgeeExpr DbMetaEdgeCtdPath DbMetaEdgeDisGeNet DbMetaEdgeDrugCentral DbMetaEdgeGo DbMetaEdgeHpa DbMetaEdgeHpoDis DbMetaEdgeHpoGene DbMetaEdgeSiderInd DbMetaEdgeSiderSe DbMetaEdgeStitch DbMetaEdgeStitchAction DbMetaEdgeString DbMetaEdgeStringAction DbMetaEdgeTnHpoDis DbMetadataMapping DbMetaMapDisGeNet DbMetaMapString DbMetaMapUniprot DbMetadataOnto DbMetaOntoDo DbMetaOntoGo DbMetaOntoHpo DbMetaOntoUberon EdgeMetadata EdgeOntoMetadata EdgeRegularMetadata TnEdgeRegularMetadata EdgeMetaGeneBindActGene EdgeMetaDisDrug EdgeMetaDisPheno EdgeMetaDrugActivationGene EdgeMetaDrugBindActGene EdgeMetaDrugBindingGene EdgeMetaDrugBindInhGene EdgeMetaDrugCatalysisGene EdgeMetaDrugExpressionGene EdgeMetaDrugInhibitionGene EdgeMetaDrugPheno EdgeMetaDrugPredBindGene EdgeMetaDrugReactionGene EdgeMetaGeneActivationGene EdgeMetaGeneAna EdgeMetaGeneBindingGene EdgeMetaGeneBindInhGene EdgeMetaGeneCatalysisGene EdgeMetaGeneDis EdgeMetaGeneDrug EdgeMetaGeneExpressionGene EdgeMetaGeneGene EdgeMetaGeneGo EdgeMetaGeneInhibitionGene EdgeMetaGeneOverAna EdgeMetaGenePath EdgeMetaGenePheno EdgeMetaGenePtmodGene EdgeMetaGeneReactionGene EdgeMetaGeneUnderAna TnEdgeMetaDisDrug TnEdgeMetaDisPheno TnEdgeMetaGeneAna TnEdgeMetaGeneOverAna TnEdgeMetaGeneUnderAna EdgeMetaAnatomyOntoIsA EdgeMetaAnatomyOntoPartOf EdgeMetaDisOntoIsA EdgeMetaGoOntoIsA EdgeMetaGoOntoPartOf EdgeMetaPhenoOntoIsA InfileMetadata InMetaEdgeStitch InMetaEdgeBgeeExpr InMetaEdgeBgeeNoExpr InMetaEdgeBgeeOverExpr InMetaEdgeBgeeUnderExpr InMetaEdgeCdtPath InMetaEdgeDisGeNet InMetaEdgeDrugCentral InMetaEdgeDrugCentralContraInd InMetaEdgeGo InMetaEdgeHpa InMetaEdgeHpoDis InMetaEdgeHpoGene InMetaEdgeSiderInd InMetaEdgeSiderSe InMetaEdgeStitchActivation InMetaEdgeStitchBindAct InMetaEdgeStitchBinding InMetaEdgeStitchBindInh InMetaEdgeStitchCatalysis InMetaEdgeStitchExpression InMetaEdgeStitchInhibition InMetaEdgeStitchPredBind InMetaEdgeStitchReaction InMetaEdgeString InMetaEdgeStringActivation InMetaEdgeStringBindAct InMetaEdgeStringBinding InMetaEdgeStringBindInh InMetaEdgeStringCatalysis InMetaEdgeStringExpression InMetaEdgeStringInhibition InMetaEdgeStringPtmod InMetaEdgeStringReaction InMetaEdgeTnHpoDis InMetaMapDisGeNet InMetaMapDrugCentralPubchem InMetaMapOntoDoAltid InMetaMapOntoDoOmim InMetaMapOntoDoUmls InMetaMapOntoGoAltid InMetaMapOntoHpoAltid InMetaMapOntoHpoUmls InMetaMapOntoUberonAltid InMetaMapString InMetaMapUniEnsNcbi InMetaMapUniUniNcbi InMetaOntoDoIsA InMetaOntoGoIsA InMetaOntoGoPartOf InMetaOntoHpoIsA InMetaOntoUberonIsA InMetaOntoUberonPartOf DbType InfileType QualityType ReaderType ConfirmFrame QueueHandler ConsoleUi ConsoleFrame GraphCreationFrame AskForExitPopup show_info_box askForExit BimegGui on_closing start_gui SkipExistingFilesPopup skipExistingFiles SplitFrame StartPage TqdmBuffer OBL2021Evaluator OBL2021Dataset NegativeSampler Sampler TrainTestSetWriter TrainTestSetCreation _not_csv TestSources TestUtils main main MockupModel DglkeEvaluator FakeEdge SafranEvaluator TestDbMetaSubclasses TestEdgeMetadataSubclasses TestFileProcessor TestGraphCreation TestInfileMetadataSubclasses TestNegativeSampler TestSampler TestTrainTestSetCreation Graph_Creation info create_input_files download_db_files start_gui create_graph exit secho TrainTestSetCreation vars time_slice_split secho random_edge_split exit secho TrainTestSetCreation vars isclass union add set join list str reset_index itertuples warning append condition rgetattr urlopen install_opener build_opener COL_NAMES_TRIPLES join astype to_csv COL_NAMES_SAMPLES drop_duplicates drop remove_reverse_edges copy apply get_diff drop_duplicates list columns reset_index set_index index copy drop NODE1_ID_COL_NAME NODE2_ID_COL_NAME drop_duplicates values merge len _get_corrupted_examples zeros full column_stack get_diff DataFrame reset_index append items sorted DataFrame items source tqdm dict adder warning qscore BELGraph _get_type_to_adder sourcedb _get_type_to_dsl __subclasses__ DB_EDGE_BGEE_DIFF DB_EDGE_BGEE DB_EDGE_CDT_PATH DB_EDGE_DISGENET DB_EDGE_DRUGCENTRAL DB_EDGE_GO DB_EDGE_HPA DB_EDGE_HPO_DIS DB_EDGE_HPO_GENE DB_EDGE_SIDER_IND DB_EDGE_SIDER_SE DB_EDGE_STITCH DB_EDGE_STITCH_ACTION DB_EDGE_STRING DB_EDGE_STRING_ACTION DB_EDGE_TN_HPO_DIS DB_MAP_DISGENET DB_MAP_STRING DB_MAP_UNIPROT DB_ONTO_DO DB_ONTO_GO DB_ONTO_HPO DB_ONTO_UBERON GENE Namespace PUBCHEM IN_EDGE_STITCH ENSEMBL GENE_DRUG DRUG GENE MULTI Namespace ANATOMY IN_EDGE_BGEE_EXPR ENSEMBL GENE_EXPRESSED_ANATOMY GENE MULTI Namespace ANATOMY ENSEMBL IN_EDGE_BGEE_NO_EXPR GENE_EXPRESSED_ANATOMY GENE MULTI Namespace GENE_OVEREXPRESSED_ANATOMY ANATOMY IN_EDGE_BGEE_OVEREXPR ENSEMBL GENE MULTI Namespace GENE_UNDEREXPRESSED_ANATOMY ANATOMY ENSEMBL IN_EDGE_BGEE_UNDEREXPR NCBI GENE MULTI Namespace IN_EDGE_CDT_PATH GENE_PATHWAY PATHWAY NCBI GENE Namespace DIS UMLS GENE_DIS IN_EDGE_DISGENET Namespace DIS_DRUG PUBCHEM DIS IN_EDGE_DRUGCENTRAL_IND UMLS DRUG Namespace IN_EDGE_DRUGCENTRAL_CONTRA_IND DIS_DRUG PUBCHEM DIS UMLS DRUG UNIPROT GENE Namespace GO IN_EDGE_GO GENE_GO GENE Namespace ANATOMY NONE IN_EDGE_HPA GENE_EXPRESSED_ANATOMY DIS_PHENOTYPE MULTI Namespace DIS IN_EDGE_HPO_DIS PHENOTYPE HPO NCBI GENE GENE_PHENOTYPE Namespace IN_EDGE_HPO_GENE PHENOTYPE HPO Namespace DIS_DRUG IN_EDGE_SIDER_IND DIS NONE DRUG IN_EDGE_SIDER_SE DRUG_PHENOTYPE Namespace PUBCHEM UMLS PHENOTYPE DRUG IN_EDGE_STITCH_ACTIVATION GENE Namespace PUBCHEM ENSEMBL DRUG_ACTIVATION_GENE DRUG GENE IN_EDGE_STITCH_BINDACT Namespace PUBCHEM ENSEMBL DRUG_BINDACT_GENE DRUG GENE Namespace IN_EDGE_STITCH_BINDING PUBCHEM ENSEMBL DRUG_BINDING_GENE DRUG GENE Namespace PUBCHEM IN_EDGE_STITCH_BINDINH ENSEMBL DRUG_BINDINH_GENE DRUG IN_EDGE_STITCH_CATALYSIS GENE Namespace DRUG_CATALYSIS_GENE PUBCHEM ENSEMBL DRUG DRUG_EXPRESSION_GENE GENE Namespace PUBCHEM IN_EDGE_STITCH_EXPRESSION ENSEMBL DRUG IN_EDGE_STITCH_INHIBITION GENE Namespace PUBCHEM ENSEMBL DRUG_INHIBITION_GENE DRUG GENE Namespace DRUG_PREDBIND_GENE PUBCHEM IN_EDGE_STITCH_PREDBIND ENSEMBL DRUG DRUG_REACTION_GENE GENE Namespace IN_EDGE_STITCH_REACTION PUBCHEM ENSEMBL DRUG GENE Namespace GENE_GENE IN_EDGE_STRING ENSEMBL GENE Namespace IN_EDGE_STRING_ACTIVATION ENSEMBL GENE_ACTIVATION_GENE GENE Namespace IN_EDGE_STRING_BINDACT ENSEMBL GENE_BINDACT_GENE GENE Namespace IN_EDGE_STRING_BINDING ENSEMBL GENE_BINDING_GENE GENE_BINDINH_GENE GENE Namespace ENSEMBL IN_EDGE_STRING_BINDINH GENE Namespace IN_EDGE_STRING_CATALYSIS GENE_CATALYSIS_GENE ENSEMBL GENE IN_EDGE_STRING_EXPRESSION Namespace GENE_EXPRESSION_GENE ENSEMBL GENE Namespace IN_EDGE_STRING_INHIBITION GENE_INHIBITION_GENE ENSEMBL GENE Namespace IN_EDGE_STRING_PTMOD GENE_PTMOD_GENE ENSEMBL GENE_REACTION_GENE GENE Namespace IN_EDGE_STRING_REACTION ENSEMBL IN_EDGE_TN_HPO_DIS DIS_PHENOTYPE MULTI Namespace DIS PHENOTYPE HPO DIS IN_MAP_DISGENET Namespace PUBCHEM IN_MAP_DRUGCENTRAL_PUBCHEM Namespace DIS IN_MAP_ONTO_DO_ALT_ID Namespace DIS IN_MAP_ONTO_DO_OMIM Namespace DIS Namespace IN_MAP_ONTO_DO_UMLS IN_MAP_ONTO_GO_ALT_ID Namespace GO HPO IN_MAP_ONTO_HPO_ALT_ID Namespace HPO IN_MAP_ONTO_HPO_UMLS Namespace IN_MAP_ONTO_UBERON_ALT_ID MULTI Namespace IN_MAP_STRING NCBI Namespace IN_MAP_UNI_ENS_NCBI NCBI Namespace NCBI IN_MAP_UNI_UNI_NCBI Namespace DIS IN_ONTO_DO_IS_A IS_A Namespace IN_ONTO_GO_IS_A IS_A Namespace GO IN_ONTO_GO_PART_OF PART_OF Namespace GO IS_A Namespace IN_ONTO_HPO_IS_A PHENOTYPE HPO IS_A MULTI Namespace ANATOMY IN_ONTO_UBERON_IS_A MULTI Namespace ANATOMY IN_ONTO_UBERON_PART_OF PART_OF AskForExitPopup exit on_closing skip for_all on_closing isfile SkipExistingFilesPopup askokcancel exit destroy showinfo Style BimegGui mainloop protocol model OBL2021Evaluator entity_emb relation_emb create_neg head_neg_prepare tail_neg_prepare load_model squeeze cat testing ScoreInfer format create_neg_prepare eval load_model_config float long join time OBL2021Dataset get_test_batches print tqdm filter_scores getTop10Tails MockupModel append getTop10Heads | <p align="center"> <img height="200" src="./resources/logo/logo.svg"> </p> ------------------------------------------------ <p align="center"> <a href="https://pypi.org/project/openbiolink/"> <img src="https://img.shields.io/pypi/v/openbiolink" alt="pypi"> </a> <a href='https://openbiolink.readthedocs.io/en/latest/?badge=latest'> | 814 |
OpenXAIProject/Interactive_Attention_Learning | ['time series'] | ['Cost-effective Interactive Attention Learning with Neural Attention Processes'] | hil_medical_annotator/utils/print_utils.py models/anno.py models/GenericNeuralNet.py Task/diabetes1_Info.py models/model.py train.py models/experiments.py models/py_utils.py models/experiments_ifif.py models/experiments_ut_counterfactual.py models/np.py models/metric.py models/hessians.py hil_medical_annotator/utils/feature_name.py retrain.py retrain_eval.py hil_medical_annotator/utils/preprocess.py hil_medical_annotator/main.py models/anno_load_data.py models/dataset.py models/experiments_counterfactual.py evaluation_after_retrain.py models/load_data.py Task/Cardiovascular1_Info.py disease_search button_year patient_search non_hide_func save_result read_non_hide main start_check cf_estimation preprocess_real_inputs preprocess_feature_contrib load_textboxes print_error_message print_warning_message print_info_message print_log_message get_curr_time_stamp Anno anno_load_data DataSet further_stage_Find_influential_training_input Find_influential_training_input Find_final_evaluation_input get_try_check further_stage_Find_influential_training_input Find_influential_training_input Find_final_evaluation_input get_try_check further_stage_Find_influential_training_input Find_influential_training_input Find_final_evaluation_input get_try_check further_stage_Find_influential_training_input Find_influential_training_input get_try_check GenericNeuralNet hessian_vector_product _AsList hessians Baseline_version_load_Data Load_Data ROC_AUC RMSE accuracy IAL_NAP NP plot_y_mat plot random_sleep cprint auto_exe_multi_exep plot_pos_neg_hist create_dir get_exclusive_colors Logger load_accounts send_mail plot_cluster_scatter task_info task_info get int list load_textboxes min tolist absolute copy array preprocess_real_inputs sum max range len non_hide_func non_hide_func non_hide_func get int list time load_textboxes min absolute copy array preprocess_real_inputs sum max range len save preprocess_real_inputs max str list load_textboxes sum range get copy lower int time join print min absolute array len get int list str load_textboxes min absolute copy array preprocess_real_inputs sum max range len get int list load_textboxes print min randint absolute copy array preprocess_real_inputs sum max range len append round range append int range join exists print get_curr_time_stamp format exit print get_curr_time_stamp format print get_curr_time_stamp format print get_curr_time_stamp format load join print ones Anno negative zeros open seed join sess var arange savez print load_checkpoint Collect_incorrect_test_indices makedirs labels mean Feature_contributions_for_chosen_instance get_influence_on_test_loss range zeros len seed join sess time arange var savez print retrain_load_checkpoint Collect_incorrect_test_indices makedirs labels mean Feature_contributions_for_chosen_instance get_influence_on_test_loss range zeros len var sess join arange print load_checkpoint mean Feature_contributions_for_chosen_instance mkdir zeros range len save get_feature_importance_with_counterfactual abs append random_floats get_feature_influence_on_test_loss std get_ut_from_trainingpoints save get_feature_importance_with_counterfactual get_ut_from_trainingpoints gradients len ndims _AsList enumerate print array DataSet print array append DataSet roc_curve auc print __str__ makedirs add_subplot axis clf linspace tick_params use set_title set_xlabel axvline savefig legend tight_layout mean set_yticks hist set_ylabel figure set_xticks fill_between use set_title set_xlabel set_yticks add_subplot axis tight_layout scatter clf figure legend set_ylabel set_xticks tick_params savefig enumerate use set_title set_xlabel set_yticks add_subplot axis tight_layout clf set_ylabel figure legend set_xticks savefig tick_params add_subplot axis clf tick_params use set_title set_xlabel savefig legend plot set_xlim tight_layout mean enumerate set_yticks set_ylabel set_xticks figure set_ylim len rand sleep join login ehlo sendmail SMTP_SSL quit Process join cprint start Queue append print | # Cost-Effective Interactive Attention Learning with Neural Attention Processes This is the **TensorFlow implementation** for the paper "Cost-Effective Interactive Attention Learning with Neural Attention Processes (**ICML 2020**) : https://arxiv.org/abs/2006.05419 ## Abstract <p align="center"> <image width="950", height="400" src="/images/ial_concept_figure.png"> We propose a novel interactive learning framework which we refer to as Interactive Attention Learning (IAL), in which the human supervisors interactively manipulate the allocated attentions, to correct the model's behaviour by updating the attention-generating network. However, such a model is prone to overfitting due to scarcity of human annotations, and requires costly retraining. Moreover, it is almost infeasible for the human annotators to examine attentions on tons of instances and features. We tackle these challenges by proposing a sample-efficient attention mechanism and a cost-effective reranking algorithm for instances and features. First, we propose Neural Attention Processes (NAPs), which is an attention generator that can update its behaviour by incorporating new attention-level supervisions without any retraining. Secondly, we propose an algorithm which prioritizes the instances and the features by their negative impacts, such that the model can yield large improvements with minimal human feedback. We validate IAL on various time-series datasets from multiple domains (healthcare, real-estate, and computer vision) on which it significantly outperforms baselines with conventional attention mechanisms, or without cost-effective reranking, with substantially less retraining and human-model interaction cost. __Contribution of this work__ - We propose a __novel interactive learning framework__ which iteratively updates the model by interacting with the human supervisor via the generated attentions. - To minimize the retraining cost, we propose a __novel probabilistic attention mechanism__ which sampleefficiently incorporates new attention-level supervisions on-the-fly without retraining and overfitting. | 815 |
OpenXAIProject/Mixed-Effect-Composite-RNN-Gaussian-Process | ['gaussian processes', 'time series'] | ['Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare'] | GPflow/testing/test_likelihoods.py GPflow/gpflow/kullback_leiblers.py GPflow/gpflow/__init__.py GPflow/testing/test_autoflow.py GPflow/gpflow/gpr.py GPflow/testing/reference.py GPflow/testing/test_pickle.py GPflow/gpflow/gplvm.py GPflow/testing/test_profiling.py run_mecgp.py GPflow/gpflow/svgp.py GPflow/testing/test_model.py GPflow/testing/test_variational.py GPflow/testing/test_mean_functions.py GPflow/setup.py GPflow/gpflow/transforms.py GPflow/testing/test_method_equivalence.py GPflow/gpflow/conditionals.py GPflow/gpflow/layers.py GPflow/gpflow/gpmc.py GPflow/testing/test_priors.py GPflow/gpflow/_version.py data_loader.py GPflow/testing/test_data_object.py GPflow/testing/test_transforms.py GPflow/gpflow/kernels.py GPflow/gpflow/quadrature.py GPflow/testing/test_ekerns.py GPflow/testing/test_minibatch_data.py GPflow/testing/test_config.py GPflow/testing/test_kldiv.py GPflow/doc/source/conf.py GPflow/doc/source/notebooks/regression.py GPflow/gpflow/scoping.py GPflow/testing/test_coregion.py GPflow/gpflow/sgpmc.py GPflow/gpflow/mecgp.py GPflow/testing/test_hmc.py GPflow/testing/test_param.py GPflow/testing/test_methods.py GPflow/gpflow/param.py GPflow/gpflow/model.py GPflow/testing/test_conditionals.py utils.py GPflow/gpflow/dkgp.py GPflow/gpflow/session.py GPflow/doc/source/notebooks/compare_variational.py GPflow/testing/test_gplvm.py GPflow/testing/test_kerns.py GPflow/doc/source/notebooks/FITCvsVFE.py GPflow/testing/gpflow_testcase.py GPflow/testing/test_session.py GPflow/gpflow/ekernels.py GPflow/gpflow/vgp.py GPflow/gpflow/mean_functions.py GPflow/gpflow/sgpr.py GPflow/gpflow/feature_representations.py GPflow/gpflow/hmc.py GPflow/gpflow/_settings.py GPflow/testing/test_predict.py GPflow/testing/test_mecgp.py GPflow/gpflow/densities.py GPflow/gpflow/likelihoods.py GPflow/gpflow/priors.py GPflow/testing/test_notebooks.py GPflow/testing/test_triang.py GPflow/gpflow/minibatch.py GPflow/gpflow/tf_wraps.py GPflow/gpflow/tf_hacks.py GPflow/notebooks/example_profiler.py GPflow/doc/source/notebooks/simple_regression.py to_float data_rearrange_2 load_data_for_RNN_and_RETAIN data_rearrange_1 load_data load_data_for_RNN_and_RETAIN_in_array Kernel GP_zero_mean dK_dt0 data_from_GP GP_linear_mean simulation_data_for_mtgp square_dist K_inverse dK_dt2 dK_dt3 dK_dt1 dL_dW GP_MLP_mean simulation_data dL_dt log_likelihood kernel plot trainSparseModel cb snelsonDemo readCsvFile getTrainingTestData plotComparisonFigure plotPredictions getRegressionModel getKernel stretch printModelParameters getLogPredictiveDensities getSparseModel outputGraph plotOptimizationResult setModelPriors plotSamples showAllPlots plotData getRegressionModel getData getSamples runExperiments optimizeModel setModelPriors getRegressionModel getData getSamples runExperiments optimizeModel gaussian_gp_predict_whitened gaussian_gp_predict conditional gp_predict gp_predict_whitened exponential multivariate_normal bernoulli gaussian lognormal laplace beta student_t gamma poisson DKVGP DKGPR DKSVGP RBF Add Prod Linear MLP RNN FeatureRepresentation Identity PCA_reduce GPLVM BayesianGPLVM GPMC GPR sample_HMC PeriodicKernel Kern Constant Stationary Matern52 Matern32 Combination Polynomial Prod Add Bias Exponential make_kernel_names Matern12 White Cosine Linear RBF Coregion Static ArcCosine gauss_kl_white_diag gauss_kl_diag gauss_kl gauss_kl_white relu_backward affine_forward affine_relu_backward relu_forward affine_backward affine_relu_forward dL_do Gamma SwitchedLikelihood Bernoulli Poisson Beta StudentT Gaussian Exponential MultiClass Likelihood RobustMax Ordinal probit Zero Product MixtureExpertsMLP2 RNN_OneLayer MeanFunction RNN_TwoLayer RNN_TwoLayer_DKGP TwoLayerSigmoidMLP SwitchedMeanFunction Constant Additive TwoLayerReLUMLP RNN_OneLayer_DKGP MixtureExpertsMLP4 Linear MECGP MinibatchData ReplacementSampling SequenceIndices NoReplacementSampling IndexManager GPModel Model GPModel_MECGP ObjectiveWrapper AutoFlow Parameterized DataHolder Parentable Param ParamList Gamma Beta Gaussian Prior LogNormal Uniform Laplace hermgauss mvhermgauss mvnquad NameScoped get_session TracerSession SGPMC SGPR SGPRUpperMixin GPRFITC SVGP vec_to_tri tri_to_vec eye vec_to_tri Exp Transform DiagMatrix LowerTriangular Log1pe Identity Logistic Rescale VGP_RNN VGP VGP_opper_archambeau VGP_RNN_E2E parse MutableNamedTuple namedtuplify SettingsContextManager read_config_file SettingsManager GPflowTestCase referenceRbfKernel referencePeriodicKernel referenceArcCosineKernel TestDataHolder TestSVGP TestNoArgs TestGPmodel TestShareArgs AddModel TestAutoFlowSessionGraphArguments DumbModel TestResetGraph TestFixAndPredict TestAdd NoArgsModel IncrementModel WhitenTestGaussian WhitenTest DiagsTest TestConfigParsing TestSettingsManager TestEquivalence TestDataHolderSimple TestDataHolderModels TestDataHolderIntegers TestKernExpDiagXcov TestKernExpActiveDims TestKernExpQuadrature TestKernExpDelta TestKernProd _assert_pdeq TestExpxKxzActiveDims index_block TriDiagonalBlockRep TestAddCrossCalcs TestGPLVM TestBayesianGPLVM SamplesDictTest SampleGaussianTest SampleModelTest TestKernNamingProduct TestARDActiveProd TestSlice TestProd TestKernSymmetry TestKernDiags TestCoregion TestRbf TestArcCosine TestARDInit TestAdd TestPeriodic TestWhite TestKernNaming OneDTest DiagsTest np_kl_1d squareT np_kl_1d_many WhitenedTest EqualityTest TestQuadrature TestSwitchedLikelihood getTestSetups TestPredictConditional TestSetup TestLikelihoodChecks TestRobustMaxMulticlass TestMulticlassIndexFix TestModelsWithMeanFuncs TestBug277Regression TestModelCompositionOperations TestSwitchedMeanFunction TestMeanFuncs TestMECGP TestStochasticGradients TestSparseMCMC TestMethods TestSVGP TestUpperBound VGPTest TestEquivalence TestSequentialManager TestMinibatchData TestRandomIndexManagers TestLikelihoodAutoflow TestNeedsRecompile TestOptimize KeyboardRaiser TestNoRecompileThroughNewModelInstance TestKeyboardCatching TestModelSessionGraphArguments TestName TestNotebooks TestRandomizePrior SingleParamterizedInvariantTest TestPickleAndDict ParamTestsDeeper TestDictSimple TestRandomizeFeedPriors TestScopes TestDictSVGP TestParamList SingleParamInvariantTest NamingTests TestRandomizeDefault TestDictEmpty TestFixWithPrior ParamTestsWider TestRandomizeHierarchical ParamTestsScalar TestActiveDims TestPickleSVGP TestPickleGPR TestPickleFix TestTransforms TestPickleEmpty TestPickleSimple TestFullCovVGP TestGaussian TestFullCovSVGP1 TestFullCovGPMC TestFullCovSVGP2 TestFullCov TestFullCovSGPMC TestFullCovSVGP3 TestFullCovSGPR TestFullCovSVGP4 TestFullCovGPRFITC PriorModeTests TestProfiling TestSessionConfiguration TransformTests TestDiagMatrixTransform TestOverflow TestLowerTriTransform TestVecToTri kernel referenceUnivariateLogMarginalLikelihood referenceMultivariatePriorKL referenceUnivariatePosterior referenceUnivariatePriorKL VariationalUnivariateTest VariationalMultivariateTest print items list len print items list len items list print array split items list print append array len print items list append square_dist Kernel normal T concatenate reshape matmul dot cholesky eye meshgrid empty range Kernel int normal arange reshape rand maximum matmul shuffle mean uniform eye cholesky append zeros range arange rand Kernel str ones matmul uniform savetxt append range normal shuffle copy mean empty int reshape sort maximum eye cholesky zeros Kernel normal T reshape inv solve matmul dot sqrt eye cholesky sum diag len Kernel normal T reshape inv solve matmul mean sqrt dot eye cholesky sum diag len Kernel normal T reshape inv solve matmul mean sqrt dot eye cholesky sum diag len exp shape range sqrt predict_y append reader open append readCsvFile range log pi GPR SGPR GPRFITC print plot sort readCsvFile predict_y sqrt optimize cb print copy range getSparseModel value plot ones set_xlabel shape set_ylabel twinx plotPredictions legend linspace array set_ylim ones subplots flatten plotComparisonFigure plotPredictions getRegressionModel set_title tolist hold_out_likelihood plot stretch printModelParameters log_likelihoods optimize print n_iters getTrainingTestData argsort trainSparseModel embed len join remove write_graph graph_def mkdir isfile compile RandomState randn rand cos sin figure plot print Matern52 Linear print optimize sqrt figure plot predict_y Gaussian Gamma print sample subplots plot set_xlabel predict_y set_ylabel set_state figure show outputGraph plotOptimizationResult setModelPriors plotSamples showAllPlots plotData getRegressionModel getData getSamples optimizeModel jitter_level matrix_triangular_solve Kdiag transpose square reduce_sum matmul matrix_band_part stack eye cholesky K tile expand_dims warn warn warn warn log lgamma square pi cast log clip_by_value pi cast log matrix_triangular_solve eigh T cov randn print size f rand randint copy isnan dot log any zeros empty range append str lower matrix_triangular_solve transpose size square matrix_band_part reduce_sum reduce_prod matrix_diag_part cast cholesky tile eye expand_dims log warn warn warn matmul reshape transpose matmul shape maximum array relu_forward affine_forward affine_backward relu_backward list product hermgauss array prod reshape transpose f mvhermgauss matmul pi tile cholesky expand_dims len dump_timeline pop output_file_name each_time output_directory fill_kwargs ConfigProto warn VisibleDeprecationWarning warn VisibleDeprecationWarning warn VisibleDeprecationWarning list constant zip pop items list isinstance Mapping join read list ConfigParser map abspath T exp dot shape zeros range arccos cos pi range dot sqrt sin empty clip exp square pi sin sum assertTrue max abs all likelihoodClass Gamma Bernoulli RandomState randn __subclasses__ reshape Poisson astype TestSetup Exponential append randint array dot T trace solve RBF | Mixed Effect Composite RNN-Gaussian Process == Mixed Effect Composite RNN-Gaussian Process: Personalized and Reliable Predictive Models for Healthcare ## Reference Code The code depends on the Python package named [GPflow](https://github.com/GPflow/GPflow) which implements Gaussian Process models based on tensorflow. ## Reference Paper **"Mixed Effect Composite RNN-GP: A Personalized and Reliable Prediction Model for Healthcare"**. Ingyo Chung, Saehoon Kim, Juho Lee, Sung Ju Hwang, and Eunho Yang (https://arxiv.org/abs/1806.01551) ## Result We conducted experiments on diverse set of disease risk prediction tasks based on medical check-up features. Results shown below show that our model is superior to other baseline models. <p align="center"> | 816 |
Orchidaceae/AI_palette_recommendation | ['stochastic optimization'] | ['Adam: A Method for Stochastic Optimization'] | Recom_engine/palette_recommender.py NN_test.py maryam_ANN.py Recom_engine/reinforcement_palette_recommender.py Lovisa_NN.py Data_mining/plot_palette.py csv_reader.py Data_mining/palette_gen.py hex_to_rgb import_csvdata palette_to_int_list rgb_palette_from createNewSeqModel plot_training_history report predictWithModel trainModel get_users submit update_csv new_palette show_palette hex_to_rgb get_recommendation get_users submit update_csv collect_statistics display_statistics new_palette get_models predict_ratings show_palette rgb_palette_from hex_to_rgb get_recommendation get_users train_model submit update_csv collect_statistics display_statistics new_palette get_models predict_ratings show_palette rgb_palette_from hex_to_rgb int writer print writerow close flush open show plot set_text add_subplot subplots_adjust set figure legend ion range len fit Sequential add Dense adam compile Dropout append randint format range draw add_subplot subplots_adjust set_visible set_facecolor gca range get_axes writer print writerow close flush open close int append array rgb_palette_from predict print new_palette append predict_ratings range print print hex_to_rgb int evaluate print size to_categorical shuffle delete floor append array fit | # AI-driven Palette Recommendation Recommendation system for 3 color palettes written in python. It uses supervised learning in order to predict user rating of palettes, classifying palettes into 3 score classes: 1 2 3 where 1=dislike, 2=neither dislike nor like, 3=like. This classification model is utilized in a recommendation engine that gives palette recommendations based of the learned user preferences. # Problem Description The hexadecimal 3 byte web color encoding can represent 16<sup>6</sup> ≈ 16.8 million different colors. With a combination of 3 colors there are (16.8x10<sup>6</sup>)<sup>3</sup> ≈ 4.7 billion possible palettes to choose from. This is definitely too much for a person to go through. One possible solution of the problem of finding good matches for a persons preferences of color combination is to let a recommendation system do the bidding. ## Classification Problem Recommender systems are one machine learning technique that make prediction based on user’s historical behaviors. The most popular approaches to build such system are Content-based and Collaborative Filtering. Content-Based Filtering requires that there is a good amount of information of item’s own features which is based on the user’s previous ratings on data. Collaborative filtering on the other hand uses techniques that can filter out items that a user might like based on the user reaction by similar users [1]. ### Content-based filtering This type of filtering does not involve other users and is based only on one user interaction with the system, the algorithm will simply pick items with similar content to recommend to the user [2]. It turned out that content-based filtering is most applicable to our AI palette recommendation engine. ### Multi-class classification In machine learning classifying samples into one of three or more classes is called Multi-class classification. This classification method uses predictive modeling and assign each sample with one of more than two classes, which is implemented by predicting the probability of the example belonging to each known class [3]. | 817 |
OsamaMazhar/Random-Shadows-Highlights | ['data augmentation'] | ['Random Shadows and Highlights: A new data augmentation method for extreme lighting conditions'] | train_model.py AlexNet.py DiskAugmenter.py RandomShadowsHighlights.py main.py OtherTransforms.py AlexNet alexnet blur sharp_blur contrast cropND get_mask regular_augmenter saturation add_sharpness illumination_augmenter Augmenter RandomGamma RandomColorJitter RandomShadows train_model load_url AlexNet load_state_dict Linear array COLOR_BGR2HSV random cvtColor random max tuple add map shape squeeze cropND random_noise shape fliplr len int ellipse zeros_like min disk dt shape randint max fromarray ones_like uint8 COLOR_BGR2RGB contrast squeeze get_mask stack any saturation sharp_blur randint cvtColor zero_grad save device str to double range state_dict format size eval mkdir item flush enumerate deepcopy time print makedirs train step len | # Random Shadows and Highlights <p align="center"> <img src="./Samples/RSH_0.gif" width="300" /> <img src="./Samples/RSH_1.gif" width="300" /> </p> <p align="center"> <img src="./Samples/RSH_2.gif" width="300" /> <img src="./Samples/RSH_3.gif" width="300" /> </p> This repo has the source code for the paper: [Random Shadows and Highlights: A new data augmentation method for extreme lighting conditions](https://arxiv.org/abs/2101.05361). | 818 |
Oscared/thesis | ['semantic segmentation'] | ['Validating Hyperspectral Image Segmentation'] | data_augmentation.py fixmatch_hsi.py models.py teacher_student_hsi.py mixup_hsi.py run_tests_mk2.py run_tests_mk3.py datasets.py run_tests.py supervised_hsi.py utils.py get_patch_data HyperX TqdmUpTo get_dataset get_pixel_idx HyperX_patches moving_average augment_pool_2 spectral_mean band_combination augment_pool_1 augment_pool_mean identity radiation_noise RandAugment spatial_combinations cutout_spatial flip spectral_shift val HamidaEtAl save_model AverageMeter test main train get_cosine_schedule_with_warmup NalepaEtAl HamidaEtAl main main main val sigmoid_rampup get_consistency_weight save_model HamidaEtAl AverageMeter update_ema_variables test softmax_mse_loss main train get_cosine_schedule_with_warmup explore_spectrums camel_to_snake plot_spectrums open_file sample_gt build_dataset convert_to_color_ sliding_window display_dataset show_results display_predictions grouper plot_confusion_matrix convert_from_color_ metrics compute_imf_weights count_sliding_window get_device get_random_pos list asarray open_file minmax_scale print reshape set isnan shape mkdir append sum prod load int format asarray print min lower pad append listdir max range len int ones_like shuffle nonzero array len flipud fliplr normal uniform randint copy sum zeros_like reshape transpose delete dot shape uniform append range prod clip ceil stack range copy mean range clip copy roll zeros_like concatenate reshape delete randint shape ceil zeros range prod clip append n_bands NalepaEtAl get_patch_data batch_size sampling_percentage patch_size tuple zero_grad delete SGD n_classes iterations DataLoader ArgumentParser get_pixel_idx total_steps device dataset argmax cuda count_nonzero str add_text ConcatDataset ones len strftime get_dataset ignored_labels from_numpy pad sample_gt shape load_state_dict append parse_args HyperX_patches to class_balancing CrossEntropyLoss get_cosine_schedule_with_warmup SummaryWriter asarray results format concatenate HyperX close test show_results display_predictions unique convert_to_color train enumerate load HamidaEtAl metrics compute_imf_weights print reshape add_argument unlabeled_ratio float32 parameters get_device color_palette zeros load_file epochs array warmup makedirs save_model zero_grad iterations camel_to_snake device save_dir max str name epochs to range update detach_ val format chunk mean softmax avg zip item float net __name__ enumerate line backward add_scalar AverageMeter write tqdm zeros step criterion_labeled AverageMeter enumerate ignored_labels str dump format state_dict Module isinstance write now save makedirs sliding_window patch_size count_sliding_window n_classes tqdm eval grouper zeros center_pixel runs fixmatch server run_name sum range supervised mean mixup detach_ data model batch_size get_consistency_weight ema_model ema_decay softmax_mse_loss cat update_ema_variables Variable unlabeled_ratio softmax clip data min add_ parameters zip is_available format print device lower open_image splitext zeros items zeros all items add_images transpose images array add_image format asarray print transpose images get_rgb add_image std plot reshape maximum mean title figure unique matplot fill_between max range len items line len arange around xticks max yticks ylabel colorbar imshow title ylim range product astype tight_layout xlim xlabel text figure len nonzero unique randint range sliding_window tuple islice iter classification_report confusion_matrix trace zeros float sum range len replace add_figure add_text print text mean heatmap plot_confusion_matrix zip std count_nonzero int list format zeros_like print multiply copy nonzero unique zip zeros train_test_split ravel range count_nonzero median nonzero zeros range sub | # Semi-Supervised Methods for Classification of Hyperspectral Images with Deep Learning This repo is the main repositiory of my master thesis at Politecnico di Milano and KTH 2020. ## Abstract Hyperspectral images (HSI) can reveal more patterns than regular images. The dimensionality is high with a wider spectrum for each pixel. Few labeled datasets exists while unlabeled data is abundant. This makes semi-supervised learning well suited for HSI classification. Leveraging new research in deep learning and semi-supervised methods, two models called FixMatch and Mean Teacher was adapted to gauge the effectiveness of consistency regularization methods for semi-supervised learning on HSI classification. Traditional machine learning methods such as SVM, Random Forest and XGBoost was compared in conjunction with two semi-supervised machine learning methods, TSVM and QN-S3VM, as baselines. The semi-supervised deep learning models was tested with two networks, a 3D and 1D CNN. To enable the use of consistency regularization several new data augmentation methods was adapted to the HSI data. Current methods are few and most rely on labeled data, which is not available in this setting. The data augmentation methods presented proved useful and was adapted in a automatic augmentation scheme. The accuracy of the baseline and semi-supervised methods showed that the SVM was best in all cases. Neither semi-supervised method showed consistently better performance than their supervised equivalent. ## Structure The notebooks were mostly made for exploring different options for research, code parts, applications and bugs. The datasets used are taken from https://tinyurl.com/ieee-grsl and are presented by Nalepa et al in https://arxiv.org/pdf/1811.03707.pdf to remedy a common validation issue in HSI classification. | 819 |
Otochess/Audio | ['data augmentation'] | ['Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras'] | clean.py train.py predict.py models.py downsample_mono split_wavs test_threshold envelope check_dir save_sample Conv2D Conv1D LSTM make_prediction train DataGenerator append abs max apply int16 read resample astype float32 join str format write exists mkdir join delta_time format int arange dst_root glob check_dir downsample_mono sr tqdm src_root zeros envelope listdir enumerate save_sample show str format use threshold plot print glob downsample_mono grid sr fn title legend src_root envelope Model Input compile Model Input compile Model Input compile concatenate sr LabelEncoder flatten save argmax sorted load_model model_fn dt append envelope fit_transform range predict format glob mean pred_fn listdir enumerate int join reshape downsample_mono tqdm zeros array delta_time batch_size LabelEncoder src_root sorted model_type train_test_split format DataGenerator glob set listdir join sample_rate fit CSVLogger transform ModelCheckpoint len | # Audio-Classification (Kapre Version) Pipeline for prototyping audio classification algorithms with TF 2  <!-- TOC --> - [YouTube](#youtube) - [Environment](#environment) - [Jupyter Notebooks](#jupyter-notebooks) - [Audio Preprocessing](#audio-preprocessing) - [Training](#training) - [Plot History](#plot-history) | 820 |
Oushesh/CapsulesforSegmentation | ['semantic segmentation'] | ['Capsules for Object Segmentation'] | utils/loader.py utils/load_data.py coco_downloader categoriseLabels cocoSegmentationToSegmentationMap coco_annotations2mask getImageID compute_class_weights split_data load_data load_class_weights print extractall listdir zip getAnnIds COCO zeros loadAnns range len append listdir strip print show join str format showAnns print getAnnIds len set COCO loadCats getImgIds imshow getCatIds loadAnns annToMask range open train_test_split count_nonzero join tqdm ReadImage GetArrayFromImage join str join sorted glob extend KFold split makedirs | # CapsulesforSegmentation My adapted repo version of the paper: Capsules for Object Segmentation --> https://arxiv.org/abs/1804.04241 | 821 |
Oushesh/SegCaps-TF_Version | ['semantic segmentation'] | ['Capsules for Object Segmentation'] | main/train.py train.py main/data.py data.py main/deploy.py config.py main/config.py main/utils.py deploy.py utils.py main/model.py model.py get_config str2bool MSCOCO main SegCaps main triplet_loss contrastive_loss mask2onehot generatorfromDirectory ImageMaskGenerator generatorMSCOCOmask generatorImage get_config str2bool MSCOCO main SegCaps main triplet_loss contrastive_loss mask2onehot generatorfromDirectory ImageMaskGenerator generatorMSCOCOmask generatorImage device get_config ConfigProto ISBI2012Reader asarray ANTIALIAS print to_categorical shape resize zeros listdir open dict ImageDataGenerator flow_from_directory ImageDataGenerator print fit asarray ANTIALIAS print len shape resize zeros listdir open print shape mask2onehot generatorImage | # tf-SegCaps TensorFlow implementation of SegCaps [1] <br/> I'm now testing this implementation with MSCOCO Dataset <br/>  ### Requirements - python 3.6 (though works both on python 3.6 and 2.7) - Tensorflow >= 1.4 - numpy - scipy - skimage | 822 |
PARC/intricate-art-neural-transfer | ['style transfer'] | ['A Neural Algorithm of Artistic Style', 'Improving the Neural Algorithm of Artistic Style'] | intricate_style.py hex_to_rgb total_variation_loss pooling_func deprocess_image Evaluator load_mask_sil gram_matrix eval_loss_and_grads mask_content content_loss str_to_bool preprocess_image style_loss lstrip tuple int astype copy expand_dims shape img_size zeros float imread reshape astype convert array amax astype shape imread range imresize dot transpose batch_flatten permute_dimensions gram_matrix square reshape astype f_outputs | ## Creating Intricate Art with Neural Style Transfer This repository contains code and example images to generate intricate art designs. The corresponding blog article that goes along with this code is found here: https://medium.com/@kramea/creating-intricate-art-with-neural-style-transfer-e5fee5f89481 ### Try it now [](https://floydhub.com/run) Click this button to open a Workspace on FloydHub to run this code. ### Details In order to run the python program, the following arguments are required: - content image (found in silhouettes folder) - style image (found in style folder) - path to save the output | 823 |
PCJohn/dip-spectral | ['denoising'] | ['The Spectral Bias of the Deep Image Prior'] | dip.py make_dataset.py utils.py skip.py relunet.py parse_args dip parse_args get_noisy_image imread imwrite train_net parse_args ReLUNet gen_dataset skip imwrite conv Flatten bn Swish fft GenNoise get_noisy_image traj_file_list imshow shape imread mask_img channel_fft bandpass_set act add_module Concat save_traj ReshapeToImg power_variation preproc band_pass_filter add_noise load_traj add_mask_noise data str randn backward print zero_grad Adam skip mse range parameters DataParallel normal_ numpy append step cuda net add_argument ArgumentParser astype float32 astype cvtColor COLOR_BGR2RGB list hstack shape meshgrid float range data str backward print reshape zero_grad Adam mse range parameters append randint step cuda net int act Sequential Linear add Concat Flatten Sigmoid conv Upsample ReshapeToImg prod range bn len uint8 imsave transpose unsqueeze normal reshape size min copy shape ceil max multiply range copy shape multiply random uint8 array savez_compressed float32 str len isinstance int AvgPool2d MaxPool2d ReflectionPad2d Conv2d Downsampler abs fft2 fftshift zeros stack circle min array | # The Spectral Bias of the Deep Image Prior Code to reproduce results in the paper: https://arxiv.org/abs/1912.08905 **Dataset** Download images from Table 2 here: http://www.cs.tut.fi/~foi/GCF-BM3D/index.html. Save these images in a folder called data/ Generate noisy images: python make_dataset.py --downsample_factors 1,2,4 This will add different levels of noise to the images downsampled by the specified factors and save them in the data/ folder **Trajectory Experiment** | 824 |
PGijsbers/gama | ['automl'] | ['GAMA: a General Automated Machine learning Assistant'] | gama/genetic_programming/operator_set.py gama/logging/utility_functions.py gama/utilities/export.py tests/unit/test_data_formatting.py gama/gama.py tests/unit/test_configuration_parser.py gama/configuration/regression.py gama/genetic_programming/mutation.py gama/dashboard/components/cli_window.py gama/dashboard/app.py gama/genetic_programming/crossover.py gama/utilities/cli.py gama/configuration/classification.py tests/unit/test_utilities_generic_paretofront.py gama/utilities/evaluation_library.py tests/unit/test_selection.py gama/genetic_programming/components/__init__.py tests/unit/test_ea_crossover.py gama/genetic_programming/operations.py gama/utilities/generic/stopwatch.py setup.py gama/dashboard/pages/runningpage.py gama/dashboard/pages/analysispage.py tests/unit/test_data_loading.py gama/GamaClassifier.py gama/genetic_programming/components/primitive_node.py gama/__version__.py tests/unit/test_nsga2.py examples/arff_example.py tests/unit/test_cli.py gama/utilities/metrics.py gama/genetic_programming/compilers/scikitlearn.py gama/genetic_programming/components/primitive.py gama/configuration/parser.py gama/GamaRegressor.py gama/genetic_programming/selection.py gama/logging/__init__.py tests/unit/test_postprocessing.py gama/__init__.py docs/source/conf.py tests/system/test_gamaclassifier.py gama/dashboard/components/input_group.py gama/utilities/generic/paretofront.py tests/unit/test_logging_gamareport.py gama/logging/GamaReport.py gama/genetic_programming/nsga2.py gama/genetic_programming/components/terminal.py gama/genetic_programming/components/individual.py tests/unit/test_ea_mutation.py tests/system/test_gamaregressor.py gama/search_methods/__init__.py gama/postprocessing/__init__.py tests/unit/test_ea_metrics.py examples/classification_example.py gama/logging/evaluation_logger.py gama/dashboard/pages/base_page.py tests/unit/test_gama.py tests/unit/test_scikitlearn.py tests/unit/test_evaluation_library.py gama/postprocessing/best_fit.py gama/postprocessing/base_post_processing.py tests/unit/test_utilities_generic_stopwatch.py gama/dashboard/pages/__init__.py tests/system/test_gama.py gama/dashboard/plotting.py gama/search_methods/asha.py gama/search_methods/base_search.py gama/utilities/generic/timekeeper.py gama/utilities/preprocessing.py gama/genetic_programming/components/fitness.py gama/search_methods/async_ea.py gama/configuration/testconfiguration.py gama/dashboard/pages/homepage.py tests/unit/test_auto_ensemble.py gama/data_formatting.py gama/data_loading.py gama/dashboard/controller.py gama/search_methods/random_search.py tests/unit/test_utilities_generic_timekeeper.py gama/dashboard/components/headers.py tests/unit/test_automl_gp.py gama/utilities/generic/async_evaluator.py gama/postprocessing/ensemble.py tests/conftest.py examples/regression_example.py remove_unlabeled_rows series_looks_categorical numpy_to_dataframe format_y infer_categoricals_inplace format_x_y file_to_pandas X_y_from_file load_csv_header sniff_csv_meta csv_to_pandas load_feature_metadata_from_arff arff_to_pandas load_feature_metadata_from_file Gama GamaClassifier GamaRegressor pset_from_config merge_configurations build_app display_page_content create_generic_layout main create_tabs create_tab Controller plot_preset_graph aggregate_best_over_time individual_plot aggregate_plot enqueue_output CLIWindow _toggle_collapse button_header markdown_header CollapsableSection automark_slider _update_marks ToggleButton AnalysisPage BasePage toggle_button cpu_slider toggle_collapse time_nud text_input dropdown collapsable_section markdown_header build_data_navigator button_header HomePage build_configuration_menu update_marks RunningPage _shared_terminals _valid_crossover_functions crossover_primitives random_crossover crossover_terminals mut_shrink mut_replace_primitive mut_replace_terminal mut_insert random_valid_mutation_in_place fast_non_dominated_sort nsga2 nsga2_select crowding_distance_assignment NSGAMeta random_terminals_for_primitive create_random_expression random_primitive_node OperatorSet eliminate_from_pareto create_from_population compile_individual evaluate_pipeline evaluate_individual object_is_valid_pipeline primitive_node_to_sklearn Fitness Individual Primitive find_terminal find_primitive PrimitiveNode Terminal format_hyperparameter_value EvaluationLogger nested_getattr init_to_hps GamaReport register_stream_log BasePostProcessing BestFitPostProcessing EnsembleClassifier fit_and_weight EnsembleRegressor build_fit_ensemble EnsemblePostProcessing Ensemble NoPostProcessing asha AsynchronousSuccessiveHalving evaluate_on_rung async_ea AsyncEA _check_base_search_hyperparameters BaseSearch random_search RandomSearch main parse_args EvaluationLibrary Evaluation format_import transformers_to_str imports_and_steps_for_individual individual_to_python format_pipeline Metric MetricType scoring_to_metric select_categorical_columns basic_encoding basic_pipeline_extension AsyncFuture evaluator_daemon AsyncEvaluator ParetoFront Stopwatch TimeKeeper Activity SS_RBS_SS_BNB ForestPipeline SS_BNB LinearSVC RS_MNB pset InvalidLinearSVC opset GNB _gama_on_digits test_full_system_multi_core test_full_system_single_core test_missing_value_classification test_binary_classification_logloss test_multiclass_classification_accuracy _test_dataset_problem test_binary_classification_accuracy test_binary_classification_accuracy_asha test_multiclass_classification_logloss test_binary_classification_accuracy_random_search test_string_label_classification_accuracy test_missing_value_classification_arff test_string_label_classification_log_loss _test_dataset_problem _test_gama_regressor test_regression_mean_squared_error test_missing_value_regression test_individual_length test_fit_and_weight test_regressor_invocation cli_command test_complex_invocation test_classifier_invocation test_invalid_file test_classifier_invocation_csv test_invalid_argument test_merge_configuration test_find_categorical_columns TestSeriesLooksCategorical TestFormatXy TestFormatY TestLoadCsvHeader TestFileToPandas TestXyFromFile TestCsvToPandas TestArffToPandas _test_x_y_d23380 TestLoadFeatureMetadata TestSniffCsvMeta _test_df_d23380_500 _test_df_d23380 test_crossover test_crossover_terminal test_crossover_max_length test_crossover_max_length_exceeded test_crossover_primitives test_shared_terminals test_all_metrics_instantiate test_scoring_to_metric_mixed test_accuracy_string test_accuracy_numeric _test_metric test_logloss_numeric test_random_valid_mutation_with_all _mut_replace_primitive_is_applied _mut_insert_is_applied test_mut_replace_terminal_none_available test_mut_insert test_random_valid_mutation_without_insert test_mut_replace_primitive_len_1 test_mut_replace_primitive_len_2 test_random_valid_mutation_without_shrink test_random_valid_mutation_without_terminal _mut_shrink_is_applied _test_mutation test_mut_replace_terminal _mut_replace_terminal_is_applied _min_trials test_evaluation_convert_predictions_from_1darray_to_nparray test_evaluation_convert_predictions_from_series_to_nparray test_evaluation_library_max_number_evaluations test_evaluation_convert_predictions_from_2darray_to_nparray _short_name test_evaluation_library_sample_np2d_prediction _mock_evaluation test_evaluation_convert_predictions_from_dataframe_to_nparray _test_subsample test_evaluation_library_n_best test_evaluation_library_sample_pd1d_prediction test_evaluation_library_sample_pd2d_prediction test_evaluation_library_sample_np1d_prediction test_gama_fail_on_invalid_hyperparameter_values test_reproducible_initialization test_gamareport_asha_from_log test_gamareport_asyncEA_from_log test_gamareport_from_log test_crowding_distance_assignment test_nsgameta_value_assignment test_crowd_compare test_dominates test_crowding_distance_assignment_inf _tuples_to_NSGAMeta test_best_fit_processing test_no_post_processing test_evaluate_pipeline test_compile_individual test_evaluate_individual test_evaluate_invalid_pipeline test_eliminate_more_than_1_from_pareto test_create_from_population test_eliminate_from_pareto test_pareto_front_custom_function test_pareto_initialization_with_inferiors test_pareto_front_clear test_pareto_initialization_pareto_front test_pareto_update_unique test_pareto_initialization_empty test_pareto_initialization_with_duplicates test_stopwatch_elapsed_time_after_running test_stopwatch_elapsed_time_while_running test_stopwatch_initialization_zero _time_approx test_timekeeper_total_time_remaning_error_if_total_time_zero test_timekeeper_stopwatch_normal_behavior test_timekeeper_total_remaining_time is_numeric_dtype value_counts series_looks_categorical astype infer_objects infer_categoricals_inplace argmax ndarray isinstance Series squeeze DataFrame isnull isinstance any info DataFrame remove_unlabeled_rows ndarray isinstance numpy_to_dataframe format_y delimiter has_header get sniff_csv_meta get infer_objects sniff_csv_meta infer_categoricals_inplace DataFrame astype isinstance zip endswith arff_to_pandas csv_to_pandas file_to_pandas endswith OrderedDict Terminal items sorted defaultdict isinstance reversed append Primitive keys items list isinstance set type hasattr Store gama_started create_generic_layout append create_tabs build_page sorted build_app run_server reset_index value_counts Histogram total_seconds dict Bar individuals append len print len sorted set_index merge_asof Series mean Scatter unique DataFrame std enumerate Scatter reset_index unique enumerate readline close iter put dict update FormGroup append cpu_count FormGroup append Collapse button_header update toggle_button cpu_slider time_nud dropdown text_input collapsable_section Button append Div Input FormGroup _valid_crossover_functions choice list _shared_terminals replace_terminal choice terminals enumerate append list _shared_terminals list replace_terminal choice filter terminals enumerate primitives list replace_primitive choice filter random_primitive_node enumerate primitives list _data_node main_node cast randint len primitives list choice random_primitive_node _data_node primitives list partial choice filter mut_fn append terminals len append nsga2 range select_one fast_non_dominated_sort sorted crowding_distance_assignment dominates combinations dominating append sorted zip float range values len random_terminals_for_primitive choice randint random_primitive_node range mutate nsga2_select append mate ParetoFront list reversed tuple len start_time Fitness time elapsed_time score min now Evaluation split isinstance getattr split replace stdout addHandler debug StreamHandler setLevel fit time _total_model_weights debug build_initial_ensemble EnsembleClassifier EnsembleRegressor info expand_ensemble fit ceil range partial log evaluate_individual _check_base_search_hyperparameters add_argument_group add_argument ArgumentParser X_y_from_file dtype export_python cleanup seperator print GamaClassifier metric GamaRegressor is_categorical_dtype dict export_script parse_args fit list hasattr map copy join startswith __module__ join primitives list _terminals reversed append enumerate sorted join imports_and_steps_for_individual union all isinstance dtype nunique isinstance CategoricalDtype list select_categorical_columns extend Pipeline fit_transform append list select_categorical_columns get collect isinstance error result put execute cleanup GamaClassifier cleanup GamaClassifier replace replace replace load_digits predict_proba train_test_split predict fit _gama_on_digits GamaClassifier _gama_on_digits GamaClassifier y_type score_from_file asarray cleanup GamaClassifier score predict_proba_from_file print predict_proba log_loss predict_from_file float train_test_split predict accuracy_score _test_dataset_problem exec export_script fit _test_dataset_problem _test_dataset_problem _test_dataset_problem _test_dataset_problem _test_dataset_problem _test_dataset_problem _test_dataset_problem _test_dataset_problem _test_dataset_problem print mean_squared_error cleanup predict GamaRegressor _test_gama_regressor _test_dataset_problem float train_test_split GamaRegressor _test_gama_regressor load_iris LinearSVC fit_and_weight cli_command run cli_command extend run split cli_command run cli_command extend run split cli_command run cli_command append run merge_configurations Series list DataFrame range crossover_primitives crossover_terminals random_crossover primitives copy_as_new random_crossover append range len maximizable_score approx product pred_format y_format _test_metric Metric asarray _test_metric Metric asarray _test_metric Metric asarray Metric list scoring_to_metric _test_mutation _test_mutation _test_mutation _test_mutation defaultdict copy_as_new _min_trials random_valid_mutation_in_place range defaultdict copy_as_new _min_trials random_valid_mutation_in_place range defaultdict copy_as_new _min_trials random_valid_mutation_in_place range defaultdict copy_as_new _min_trials random_valid_mutation_in_place range mutation_check mutation compile_individual copy_as_new ndarray predictions random isinstance ndarray predictions random isinstance ndarray isinstance Series random predictions ndarray isinstance DataFrame random predictions save_evaluation EvaluationLibrary range _mock_evaluation save_evaluation EvaluationLibrary range _mock_evaluation save_evaluation EvaluationLibrary _mock_evaluation random _test_subsample DataFrame random _test_subsample random _test_subsample Series random _test_subsample cleanup GamaClassifier GamaReport GamaReport GamaReport _tuples_to_NSGAMeta _tuples_to_NSGAMeta _tuples_to_NSGAMeta crowding_distance_assignment _tuples_to_NSGAMeta crowding_distance_assignment fast_non_dominated_sort _tuples_to_NSGAMeta crowding_distance_assignment NoPostProcessing post_process post_process load_iris exec to_code BestFitPostProcessing fit individual now evaluate_individual compile_individual evaluate_pipeline load_iris pipeline evaluate_pipeline load_iris pipeline Fitness eliminate_from_pareto Fitness create_from_population ParetoFront ParetoFront ParetoFront ParetoFront update ParetoFront range len clear ParetoFront update ParetoFront Stopwatch sleep TimeKeeper sleep TimeKeeper sleep TimeKeeper |  **G**eneral **A**utomated **M**achine learning **A**ssistant An automated machine learning tool based on genetic programming. Make sure to check out the [documentation](https://openml-labs.github.io/gama/). [](https://travis-ci.org/openml-labs/gama) [](https://codecov.io/gh/openml-labs/gama) [](https://doi.org/10.21105/joss.01132) --- GAMA is an AutoML package for end-users and AutoML researchers. It generates optimized machine learning pipelines given specific input data and resource constraints. | 825 |
PIX2NVS/NVS_FeatureLearning | ['action recognition'] | ['Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing'] | code/main.py code/utils.py code/MyData.py code/inputsdata.py code/MyNet.py Net MyOwnDataset DataAug train parse_files test maybe_num_nodes Data MaxPool3dSamePadding ResidualBlock InceptionModule Net Unit3D calculate_accuracy AverageMeter Logger load_value_file print param_groups enumerate eval topk view size t eq | # Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing ## Summary This is the implemtation code and proposed dataset(ASL-DVS) for the following paper. Please cite following paper if you use this code or dataset in your own work. The paper is available via: https://ieeexplore.ieee.org/abstract/document/9199543 MLA: Bi, Y., Chadha, A., Abbas, A., Bourtsoulatze, E., & Andreopoulos, Y. (2019). Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing. IEEE Transactions on Image Processing, 9084-9008, 2020 BibTex: @article{bi2020graph, title={Graph-Based Spatio-Temporal Feature Learning for Neuromorphic Vision Sensing}, author={Bi, Yin and Chadha, Aaron and Abbas, Alhabib and Bourtsoulatze, Eirina and Andreopoulos, Yiannis}, | 826 |
PKU-TANGENT/NeuralEDUSeg | ['discourse segmentation'] | ['Toward Fast and Accurate Neural Discourse Segmentation'] | src/rst_edu_reader.py src/run.py src/layers.py src/base_seg.py src/elmo_crf_seg.py src/api.py src/lstm_seg.py src/rnn.py src/preprocess.py src/lstm_crf_seg.py src/vocab.py src/atten_seg.py src/config.py segment train evaluate prepare AttnSegModel BaseSegModel parse_args ELMOCRFSegModel mask_logits trilinear_similarity self_attention get_cell rnn LSTMCRFSegModel LSTMSegModel preprocess_rst_data preprocess_one_doc get_cell rnn RSTData Vocab join rst_dir format getLogger preprocess_rst_data shuffle call info makedirs join rst_dir train_files getLogger batch_size dev_files test_files RSTData AttnSegModel info epochs join restore rst_dir getLogger batch_size model_dir test_files RSTData AttnSegModel info gen_mini_batches getLogger batch_size model_dir AttnSegModel input_files run restore basename use_ema ema_assign_op RSTData append format ema_backup_op info gen_mini_batches zip enumerate load join result_dir pipe makedirs add_argument_group add_argument ArgumentParser cast float32 MultiRNNCell CudnnCompatibleGRUCell BasicRNNCell endswith DropoutWrapper append CudnnCompatibleLSTMCell range load insert startswith append pipe join format preprocess_one_doc endswith info listdir makedirs dynamic_rnn concat get_cell bidirectional_dynamic_rnn LSTMCell GRUCell | # Neural-EDU-Segmentation A toolkit for segmenting Elementary Discourse Units (clauses). We implement it as is described in our EMNLP paper: [Toward Fast and Accurate Neural Discourse Segmentation](http://www.aclweb.org/anthology/D18-1116) ### Requirements - Python 3.5 - Tensorflow>=1.5.0 - allennlp>=0.4.2 - See `requirements.txt` for the full list of packages ### Data We cannot provide the complete [RST-DT corpus](https://catalog.ldc.upenn.edu/products/LDC2002T07) due to the LDC copyright. | 827 |
PKUYeYuan/Constraint-Loss-AAAI-2020 | ['relation extraction'] | ['Integrating Relation Constraints with Neural Relation Extractors'] | ACNN/src/main.py APCNN/main.py APCNN/nrekit/data_loader.py ACNN/src/ACNN.py APCNN/nrekit/network/encoder.py APCNN/nrekit/network/__init__.py APCNN/nrekit/network/classifier.py APCNN/nrekit/network/APCNN.py APCNN/nrekit/framework.py APCNN/nrekit/__init__.py APCNN/nrekit/network/embedding.py ACNN/src/DataHandler.py APCNN/nrekit/network/selector.py ACNN/src/ConstraintLoss.py APCNN/nrekit/network/ConstraintLoss.py ACNN/src/__init__.py ACNN ConstraintLoss find_index get_entity_position levenshtein pos_embed DataLoader get_entity_position_not_completely_match DataPreprocessor train_step test get_auc train MetricsEvaluation ConvertTxt2Json npy_data_loader file_data_loader json_file_data_loader average_gradients re_framework APCNN soft_label_softmax_cross_entropy output sigmoid_cross_entropy softmax_cross_entropy ConstraintLoss word_position_embedding pos_embedding word_embedding birnn __dropout__ __piecewise_pooling__ __pooling__ __rnn_cell__ cnn rnn __cnn_cell__ pcnn bag_average bag_cross_max __dropout__ __attention_train_logit__ bag_one __attention_test_logit__ bag_attention __logit__ instance range len min range len int argmin levenshtein append array range len get_entity_position_not_completely_match range len print run DataLoader print join DataLoader PaddingData sorted plot print xlabel dataset write ylabel grid ylim title savefig legend train_mode xlim max reshape precision_recall_curve auc join dump list print readlines map split append exists open concat reduce_mean zip append expand_dims pos_embedding word_embedding reduce_max embedding_lookup expand_dims constant conv1d embedding_lookup reduce_sum transpose matmul __dropout__ __logit__ | # Constraint-Loss-AAAI-2020 The source code and dataset for our paper "[Integrating Relation Constraints with Neural Relation Extractors](https://arxiv.org/abs/1911.11493)" which is publicated at AAAI 2020. ### Methods: In this paper, we propose a unified framework to effectively integrate discrete relation constraints with neural networks for relation extraction. Specifically, we develop two approaches to evaluate how well *NRE* predictions satisfy our relation constraints in a batch-wise, from both general and precise perspectives. We explore our approach on English and Chinese dataset, and the experimental results show that our approach can help the base *NRE* models to effectively learn from the discrete relation constraints, and outperform popular *NRE* models as well as their *ILP* enhanced versions. Our study reveals that learning with the constraints can better utilize the constraints from a different perspective compared to the *ILP* post-processing method. And the framework and experimental results are shown as follows:  Fig1: Model Framework  Fig2: Experimental Results ### Requirements: | 828 |
PO-MC-model-NeurIPS2020/PO-MC-model | ['imitation learning'] | ['Policy learning with partial observation and mechanical constraints for multi-person modeling'] | preprocessing.py sequencing.py hidden_role_learning.py vrnn/models/__init__.py utilities.py vrnn/models/macro_vrnn.py vrnn/models/utils.py vrnn/models/rnn_gauss.py vrnn/datasets/__init__.py main.py features.py create_dynamic_features OneHotEncoding flatten_moments_soccer flatten_moments create_static_features HiddenStructureLearning bound run_sanity get_macro_intent batch_error label_macro_intents compute_macro_intents_stationary run_epoch loss_str unnormalize split_testdata_basket chunk_shotclock remove_non_eleven remove_outlier chunk_halfcourt process_game_data filters reorder_teams get_sequences subsample_sequence PlotGame plot_check_pred id_teams make_video check_game_roles_duplicates id_player LoadData id_position plot_check GeneralDataset MACRO_VRNN RNN_GAUSS kld_gauss acc_cost one_hot_encode entropy_gauss parse_model_params sample_gumbel vel_cost sample_gumbel_softmax index_by_agent num_trainable_params sample_multinomial nll_gauss calc_dist_cos_sin batch_error get_macro_ohe sample_gauss get_params_str cudafy_list roll_out load_model copy apply copy apply copy apply copy apply update model backward transpose clip_grad_norm_ zero_grad step parameters permute item sample sum cuda values enumerate fs format n_feat std concatenate print horizon copy mean shape in_out in_sma zeros unnormalize max range burn_in sqrt sum int multiply ndim tile normalize array int compute_macro_intents_fixed shape compute_macro_intents_stationary zeros range norm get_macro_intent reversed append zeros range len int bound copy apply apply copy len butter copy filtfilt append zeros moments range len copy apply copy apply copy append copy values data reorder_moment concat DataFrame subsample_sequence len HiddenStructureLearning append normalize range n_roles format load_game zip hmm_iter int process_game_data_ print subsample_factor event_threshold n_GorS T astype append zeros array range len format destroyAllWindows print float write imshow shape VideoWriter imread VideoWriter_fourcc release arrow plot grid figure array range len plot print reshape grid shape title figure randint range len print range values print range append keys id_team_ parameters size parse_known_args add_argument getattr cuda range len transpose clone data one_hot_encode size is_cuda zeros cuda range normal_ cuda is_cuda FloatTensor log pow cuda is_cuda pow log is_cuda pow to shape exp sample_gumbel sum nonzero pow sqrt sum nonzero sqrt pow nonzero is_available zeros cuda list remove reshape zeros calc_dist_cos_sin index repeat tensor to range cat append zeros long scatter_ one_hot_encode size squeeze cuda is_cuda lower | PO-MC-model-NeurIPS2020/PO-MC-model | 829 |
PRBonn/bonnetal | ['semantic segmentation'] | ['Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs'] | train/tasks/classification/modules/userCaffe2.py train/tasks/segmentation/dataset/cityscapes/parser.py train/tasks/classification/train.py train/backbones/mobilenetv2.py train/tasks/segmentation/decoders/aspp_residual.py train/tasks/segmentation/modules/traceSaver.py train/tasks/segmentation/modules/custom_losses.py train/tasks/segmentation/modules/colorizer.py train/backbones/resnet.py train/common/projections.py train/tasks/classification/modules/userPytorch.py train/common/sync_batchnorm/replicate.py train/auxiliary/calculate_means_stds.py train/common/sync_batchnorm/comm.py train/tasks/classification/infer_img.py train/auxiliary/calculate_segmentation_weights.py train/tasks/segmentation/dataset/synthia/parser.py train/tasks/classification/__init__.py train/tasks/segmentation/dataset/coco/parser.py train/common/onehot.py train/backbones/ERFNet.py train/common/logger.py train/tasks/segmentation/modules/trainer.py train/common/trtCalibINT8.py train/tasks/segmentation/__init__.py train/tasks/segmentation/modules/userPytorch.py train/tasks/classification/make_deploy_model.py train/tasks/segmentation/dataset/mapillary/parser.py train/tasks/segmentation/decoders/aspp_residual_attention.py train/common/sync_batchnorm/batchnorm.py train/backbones/darknet.py train/tasks/segmentation/train.py train/tasks/segmentation/dataset/persons/parser.py train/common/layers.py train/tasks/classification/modules/traceSaver.py train/tasks/segmentation/modules/segmentator.py train/tasks/segmentation/dataset/pascal/parser.py train/common/boxmeter.py train/tasks/segmentation/modules/user.py train/tasks/segmentation/modules/userCaffe2.py train/tasks/segmentation/decoders/ERFNet.py train/common/oneshot.py train/tasks/classification/modules/classifier.py train/tasks/classification/modules/trainer.py train/tasks/classification/modules/userTensorRT.py train/tasks/classification/dataset/imagenet/parser.py train/tasks/segmentation/make_deploy_model.py train/tasks/segmentation/dataset/coco/generate_gt.py train/tasks/segmentation/modules/head.py train/tasks/segmentation/infer_video.py train/tasks/classification/infer_video.py train/tasks/segmentation/modules/userTensorRT.py train/backbones/config.py train/tasks/classification/dataset/cifar10/parser.py train/tasks/segmentation/decoders/aspp_progressive.py train/tasks/segmentation/modules/__init__.py train/tasks/classification/modules/user.py train/tasks/classification/modules/head.py train/tasks/segmentation/modules/ioueval.py train/common/avgmeter.py train/tasks/segmentation/infer_img.py train/tasks/classification/dataset/mnist/parser.py is_image is_image BackboneConfig BasicBlock Backbone Backbone Backbone conv3x3 BasicBlock Backbone Bottleneck AverageMeter jaccard intersect InvertedResidual ConvBnRelu ASPP View non_bottleneck_1d Logger to_one_hot OneShot_LR random_projection EntropyCalibrator _sum_ft convert_model SynchronizedBatchNorm2d _unsqueeze_ft _SynchronizedBatchNorm SynchronizedBatchNorm1d SynchronizedBatchNorm3d SyncMaster FutureResult SlavePipe execute_replication_callbacks CallbackContext DataParallelWithCallback patch_replication_callback CaptureRunner Parser Parser Parser Classifier HeadConfig Head TraceSaver Trainer User UserCaffe2 UserPytorch UserTensorRT CaptureRunner ToLabel is_image is_label image_basename Parser image_path_city load_image cityscapes is_image is_label MS_COCO ToLabel is_image resize_and_fit is_label Parser load_label load_image ToLabel is_image Parser Mapillary load_image ToLabel is_image resize_and_fit is_label Parser Pascal load_label load_image ToLabel is_image Persons Parser load_image ToLabel is_image is_label image_basename Parser image_path_city synthia load_image Decoder Decoder Decoder Decoder Colorizer mIoULoss DecoderConfig HeadConfig Head iouEval Segmentator TraceSaver Trainer User UserCaffe2 UserPytorch UserTensorRT int float intersect expand_as clamp min max expand size unsqueeze is_cuda scatter_ GaussianRandomProjection matmul t shape device tensor to double is_cuda _make_random_matrix eps num_features affine isinstance named_children momentum running_mean add_module DataParallel DataParallelWithCallback zip running_var module sync_module detach list hasattr __data_parallel_replicate__ modules enumerate len replicate int NEAREST size new BILINEAR paste resize | PRBonn/bonnetal | 830 |
PSCLab-ASU/Learning-in-the-Frequency-Domain | ['instance segmentation', 'semantic segmentation'] | ['Learning in the Frequency Domain'] | segmentation/mmdet/ops/sigmoid_focal_loss/sigmoid_focal_loss.py segmentation/mmdet/models/detectors/__init__.py segmentation/mmdet/core/utils/misc.py segmentation/mmdet/datasets/registry.py segmentation/mmdet/datasets/custom.py segmentation/mmdet/models/backbones/resnetDCT.py segmentation/mmdet/core/anchor/point_generator.py segmentation/mmdet/models/roi_extractors/single_level.py segmentation/mmdet/models/mask_heads/maskiou_head.py segmentation/mmdet/models/detectors/single_stage.py classification/utils/progress/progress/helpers.py segmentation/mmdet/models/detectors/htc.py segmentation/mmdet/models/anchor_heads/reppoints_head.py segmentation/mmdet/models/detectors/test_mixins.py segmentation/mmdet/models/anchor_heads/retina_head.py segmentation/mmdet/models/roi_extractors/__init__.py segmentation/mmdet/datasets/pipelines/test_aug.py segmentation/mmdet/models/utils/conv_ws.py segmentation/mmdet/models/losses/balanced_l1_loss.py segmentation/mmdet/models/detectors/base.py segmentation/mmdet/ops/dcn/deform_conv.py segmentation/mmdet/core/post_processing/__init__.py segmentation/mmdet/models/anchor_heads/fcos_head.py segmentation/mmdet/models/backbones/gate.py segmentation/mmdet/datasets/loader/sampler.py segmentation/mmdet/models/anchor_heads/ssd_head.py segmentation/mmdet/apis/env.py segmentation/mmdet/models/losses/accuracy.py segmentation/mmdet/models/bbox_heads/__init__.py segmentation/mmdet/apis/__init__.py classification/datasets/cvfunctional.py segmentation/mmdet/core/evaluation/eval_hooks.py segmentation/mmdet/models/detectors/fast_rcnn.py classification/utils/progress/progress/bar.py segmentation/mmdet/apis/train.py segmentation/mmdet/models/mask_heads/fcn_mask_head.py segmentation/mmdet/ops/__init__.py segmentation/mmdet/apis/inference.py segmentation/tools/test.py segmentation/mmdet/datasets/builder.py segmentation/mmdet/models/__init__.py segmentation/mmdet/models/anchor_heads/guided_anchor_head.py segmentation/mmdet/models/detectors/rpn.py segmentation/mmdet/core/utils/__init__.py segmentation/mmdet/models/shared_heads/__init__.py segmentation/mmdet/models/detectors/cascade_rcnn.py segmentation/mmdet/models/losses/mse_loss.py segmentation/mmdet/core/post_processing/merge_augs.py segmentation/mmdet/utils/transfer_model.py segmentation/mmdet/models/detectors/mask_scoring_rcnn.py segmentation/mmdet/core/bbox/transforms.py segmentation/mmdet/models/backbones/__init__.py segmentation/mmdet/utils/__init__.py classification/main/imagenet_resnet_upscaled_static.py segmentation/mmdet/core/evaluation/class_names.py segmentation/mmdet/core/fp16/decorators.py segmentation/mmdet/datasets/xml_style.py segmentation/mmdet/datasets/pipelines/formating.py segmentation/mmdet/models/losses/iou_loss.py segmentation/mmdet/core/__init__.py segmentation/mmdet/models/utils/scale.py classification/datasets/cvtransforms.py segmentation/mmdet/core/anchor/anchor_target.py segmentation/mmdet/models/detectors/double_head_rcnn.py segmentation/mmdet/datasets/coco.py segmentation/setup.py segmentation/mmdet/core/bbox/assigners/base_assigner.py classification/datasets/vision.py segmentation/mmdet/models/losses/utils.py classification/utils/visualize.py segmentation/mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py segmentation/mmdet/models/utils/__init__.py segmentation/mmdet/datasets/loader/__init__.py classification/models/imagenet/resnet.py segmentation/mmdet/models/bbox_heads/double_bbox_head.py segmentation/mmdet/datasets/pipelines/dct_channel_index.py segmentation/mmdet/core/post_processing/bbox_nms.py segmentation/mmdet/models/utils/conv_module.py segmentation/mmdet/models/backbones/ssd_vgg.py classification/models/imagenet/mobilenetv2.py segmentation/mmdet/ops/nms/nms_wrapper.py segmentation/mmdet/utils/draw_inputgate.py segmentation/mmdet/models/plugins/non_local.py classification/utils/__init__.py segmentation/mmdet/models/losses/ghm_loss.py segmentation/mmdet/ops/dcn/deform_pool.py segmentation/mmdet/datasets/pipelines/loading.py segmentation/mmdet/core/anchor/anchor_generator.py segmentation/configs/faster_rcnn_r50_fpn_1x_static_24_wofreeze.py classification/datasets/dataset_imagenet_dct.py segmentation/mmdet/core/bbox/samplers/ohem_sampler.py segmentation/mmdet/models/builder.py segmentation/mmdet/ops/roi_align/gradcheck.py classification/utils/progress/setup.py segmentation/mmdet/ops/masked_conv/__init__.py segmentation/mmdet/models/utils/norm.py segmentation/tests/test_utils.py segmentation/mmdet/models/plugins/generalized_attention.py segmentation/mmdet/models/backbones/resnetDCT_dynamic.py segmentation/mmdet/core/anchor/__init__.py segmentation/mmdet/models/losses/focal_loss.py segmentation/mmdet/models/detectors/mask_rcnn.py segmentation/mmdet/core/bbox/samplers/random_sampler.py segmentation/mmdet/models/bbox_heads/convfc_bbox_head.py segmentation/mmdet/models/mask_heads/fused_semantic_head.py segmentation/mmdet/models/necks/bfp.py segmentation/mmdet/models/utils/weight_init.py segmentation/mmdet/core/mask/__init__.py segmentation/mmdet/core/bbox/assigners/assign_result.py segmentation/mmdet/datasets/dataset_wrappers.py segmentation/mmdet/core/evaluation/recall.py segmentation/configs/mask_rcnn_r50_rpn_1x_DCT_static_64_wofreeze.py segmentation/mmdet/models/necks/__init__.py segmentation/mmdet/models/detectors/reppoints_detector.py segmentation/mmdet/models/losses/smooth_l1_loss.py segmentation/mmdet/core/evaluation/mean_ap.py segmentation/mmdet/ops/nms/__init__.py segmentation/configs/mask_rcnn_r50_rpn_1x_DCT_static_24_wofreeze.py classification/main/imagenet_mobilenetv2_upscaled_static.py segmentation/mmdet/datasets/transforms.py segmentation/mmdet/datasets/pipelines/formatingDCT.py segmentation/mmdet/core/bbox/bbox_target.py segmentation/mmdet/models/detectors/grid_rcnn.py classification/models/imagenet/__init__.py classification/utils/logger.py segmentation/mmdet/datasets/extra_aug.py segmentation/mmdet/datasets/pipelines/transformsDCT.py segmentation/mmdet/models/plugins/__init__.py classification/utils/eval.py segmentation/mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py segmentation/mmdet/utils/plot_dct.py segmentation/mmdet/models/mask_heads/htc_mask_head.py segmentation/mmdet/ops/sigmoid_focal_loss/__init__.py segmentation/configs/mean_std.py segmentation/mmdet/models/backbones/hrnet.py segmentation/mmdet/core/evaluation/coco_utils.py segmentation/mmdet/models/detectors/faster_rcnn.py classification/utils/misc.py segmentation/mmdet/utils/registry.py segmentation/mmdet/core/anchor/point_target.py classification/utils/init_weights.py segmentation/mmdet/datasets/wider_face.py segmentation/mmdet/core/bbox/assigners/approx_max_iou_assigner.py classification/datasets/dataloader_imagenet_dct.py segmentation/mmdet/models/detectors/two_stage.py segmentation/mmdet/models/backbones/resnet_dynamic.py segmentation/mmdet/models/anchor_heads/anchor_head.py segmentation/mmdet/models/mask_heads/grid_head.py segmentation/mmdet/models/necks/fpn.py segmentation/mmdet/ops/roi_align/__init__.py segmentation/mmdet/core/evaluation/__init__.py segmentation/mmdet/core/bbox/samplers/pseudo_sampler.py segmentation/mmdet/core/bbox/samplers/__init__.py segmentation/mmdet/core/evaluation/bbox_overlaps.py segmentation/mmdet/models/backbones/resnet_static.py classification/datasets/__init__.py classification/utils/progress/test_progress.py segmentation/mmdet/core/utils/dist_utils.py segmentation/mmdet/ops/roi_align/roi_align.py segmentation/mmdet/models/detectors/fcos.py segmentation/mmdet/datasets/__init__.py segmentation/mmdet/datasets/pipelines/compose.py segmentation/mmdet/core/bbox/assign_sampling.py classification/utils/progress/progress/__init__.py segmentation/mmdet/core/fp16/utils.py segmentation/mmdet/apis/mean_std_cal.py segmentation/mmdet/core/mask/mask_target.py segmentation/mmdet/models/losses/__init__.py segmentation/mmdet/models/registry.py classification/utils/progress/progress/spinner.py segmentation/configs/faster_rcnn_r50_fpn_1x_static_64_wofreeze.py segmentation/mmdet/datasets/loader/build_loader.py segmentation/mmdet/core/bbox/geometry.py segmentation/mmdet/datasets/cityscapes.py segmentation/mmdet/models/anchor_heads/__init__.py segmentation/mmdet/models/detectors/retinanet.py segmentation/mmdet/core/anchor/guided_anchor_target.py segmentation/mmdet/core/bbox/samplers/sampling_result.py segmentation/mmdet/datasets/voc.py segmentation/mmdet/__init__.py segmentation/mmdet/models/anchor_heads/ga_retina_head.py segmentation/mmdet/ops/roi_pool/__init__.py classification/utils/progress/progress/counter.py segmentation/mmdet/core/mask/utils.py segmentation/mmdet/models/backbones/resnet.py segmentation/mmdet/models/bbox_heads/bbox_head.py segmentation/mmdet/models/backbones/gumbel.py segmentation/mmdet/ops/dcn/__init__.py segmentation/mmdet/core/bbox/samplers/combined_sampler.py segmentation/mmdet/models/shared_heads/res_layer.py segmentation/mmdet/core/bbox/assigners/point_assigner.py segmentation/mmdet/models/backbones/resnext.py segmentation/mmdet/ops/roi_pool/gradcheck.py segmentation/mmdet/ops/masked_conv/masked_conv.py segmentation/mmdet/models/anchor_heads/rpn_head.py segmentation/mmdet/core/bbox/assigners/__init__.py segmentation/mmdet/ops/roi_pool/roi_pool.py segmentation/mmdet/models/mask_heads/__init__.py segmentation/mmdet/core/fp16/__init__.py segmentation/mmdet/models/necks/hrfpn.py segmentation/mmdet/utils/flops_counter.py segmentation/mmdet/core/bbox/__init__.py segmentation/mmdet/core/bbox/samplers/base_sampler.py segmentation/mmdet/datasets/pipelines/transforms.py segmentation/mmdet/models/anchor_heads/ga_rpn_head.py segmentation/mmdet/models/losses/cross_entropy_loss.py segmentation/mmdet/datasets/pipelines/__init__.py segmentation/mmdet/ops/context_block.py segmentation/mmdet/core/fp16/hooks.py classification/models/utils.py classification/main/__init__.py segmentation/mmdet/core/bbox/assigners/max_iou_assigner.py hflip salt_and_pepper resize to_rgb_bgr to_tensor_dct cv_transform pil_transform to_grayscale center_crop transform_dct rotate imshow pad five_crop resized_crop normalize to_tensor _is_tensor_image adjust_gamma ten_crop adjust_saturation _is_numpy_image upscale crop perspective gaussian_noise affine6 to_cv_image affine poisson_noise adjust_hue adjust_brightness vflip adjust_contrast CenterCrop RandomRotation ToTensor RandomApply TransformUpscaledDCT RandomCrop RandomChoice RandomAffine RandomSPNoise RandomTransforms ToCVImage ToTensorDCT TenCrop Average AverageYUV AdjustDCT RandomPoissonNoise Resize RandomResizedCrop RandomHorizontalFlip FiveCrop RandomGrayscale Pad RandomPerspective NormalizeDCT Lambda Compose RandomVerticalFlip Normalize SubsetDCT adjust_size Upscale Grayscale RandomAffine6 Aggregate DCTCenterCrop RandomGaussianNoise LinearTransformation RandomOrder ColorJitter valloader_upscaled_static is_image_file make_dataset ImageFolderDCT accimage_loader default_loader has_file_allowed_extension DatasetFolderDCT adjust_size opencv_loader pil_loader StandardTransform VisionDataset main validate main str2bool test xavier_init constant_init uniform_init get_upsample_filter normal_init kaiming_init caffe2_xavier_init conv_1x1_bn conv_3x3_bn MobileNetV2DCT_Deconv_Subset mobilenetv2dct_deconv_subset MobileNetV2DCT_Subset_woinp_from_scratch mobilenetv2dct MobileNetV2 MobileNetV2DCT_Subpixel MobileNetV2DCT_Subset_woinp mobilenetv2 MobileNetV2DCT MobileNetV2DCT_Upscaled mobilenetv2dct_subset_woinp_from_scratch mobilenetv2dct_subset_woinp _make_divisible MobileNetV2DCT_Subpixel_Subset mobilenetv2dct_subpixel mobilenetv2dct_subpixel_subset InvertedResidual mobilenetv2dct_upscaled_subset mobilenetv2dct_upscaled MobileNetV2DCT_Upscaled_Subset conv1x1 resnext50_32x4d ResNet resnet50 ResNet50DCT resnext101_32x8d Bottleneck resnet152 ResNetDCT_Upscaled_Static conv3x3 _resnet resnet34 resnet18 main BasicBlock ResNet50DCT_Upscaled resnet101 accuracy weights_init_orthogonal weights_init_normal weights_init_xavier weights_init weights_init_kaiming plot_overlap savefig Logger LoggerMonitor get_mean_and_std_yuv AverageMeter init_params get_mean_and_std_dct yuv_loader mkdir_p get_mean_and_std_dct_resized 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 make_cuda_ext write_version_py readme get_requirements get_version get_git_hash get_hash make_cython_ext _init_dist_pytorch _init_dist_slurm init_dist set_random_seed get_root_logger _init_dist_mpi inference_detector show_result_pyplot LoadImage init_detector show_result _dist_train build_optimizer batch_processor _non_dist_train train_detector parse_losses _dist_train build_optimizer batch_processor _non_dist_train train_detector parse_losses AnchorGenerator anchor_target unmap anchor_inside_flags images_to_levels anchor_target_single ga_loc_target ga_shape_target_single calc_region images_to_levels ga_shape_target PointGenerator images_to_levels point_target unmap point_target_single assign_and_sample build_assigner build_sampler bbox_target_single expand_target bbox_target bbox_overlaps delta2bbox roi2bbox bbox_flip distance2bbox bbox2delta bbox_mapping bbox2result bbox_mapping_back bbox2roi ApproxMaxIoUAssigner AssignResult BaseAssigner MaxIoUAssigner PointAssigner BaseSampler CombinedSampler InstanceBalancedPosSampler IoUBalancedNegSampler OHEMSampler PseudoSampler RandomSampler SamplingResult bbox_overlaps get_classes imagenet_vid_classes voc_classes imagenet_det_classes coco_classes cityscapes_classes wider_face_classes coco_eval segm2json proposal2json fast_eval_recall xyxy2xywh results2json det2json CocoDistEvalRecallHook DistEvalmAPHook DistEvalHook CocoDistEvalmAPHook eval_map tpfp_imagenet print_map_summary average_precision get_cls_results tpfp_default plot_iou_recall set_recall_param print_recall_summary _recalls eval_recalls plot_num_recall force_fp32 auto_fp16 Fp16OptimizerHook wrap_fp16_model patch_forward_method patch_norm_fp32 cast_tensor_type mask_target mask_target_single split_combined_polys multiclass_nms merge_aug_scores merge_aug_masks merge_aug_bboxes merge_aug_proposals DistOptimizerHook allreduce_grads _allreduce_coalesced unmap tensor2imgs multi_apply build_dataset _concat_dataset CityscapesDataset CocoDataset CustomDataset RepeatDataset ConcatDataset PhotoMetricDistortion Expand RandomCrop ExtraAugmentation MaskTransform SegMapTransform bbox_flip ImageTransformDCT Numpy2Tensor BboxTransform VOCDataset WIDERFaceDataset XMLDataset build_dataloader GroupSampler DistributedSampler DistributedGroupSampler Compose DefaultFormatBundle Transpose Average ToTensor Collect DynamicInput to_tensor ImageToTensor ToDataContainer NormalizeDCTUpscaledStatic DefaultFormatBundleDCT NormalizeDCT LoadImageFromFile LoadProposals LoadAnnotations MultiScaleFlipAug RandomFlip Pad Corrupt PhotoMetricDistortion MinIoURandomCrop Resize RandomCrop SegResizeFlipPadRescale Normalize Expand ToDCTUpscaledStatic ToDCT build_shared_head build_detector build_loss build build_backbone build_roi_extractor build_head build_neck AnchorHead FCOSHead GARetinaHead GARPNHead FeatureAdaption GuidedAnchorHead RepPointsHead RetinaHead RPNHead SSDHead GateModule GateModule192 GumbleSoftmax HRModule HRNet ResNet BasicBlock make_res_layer Bottleneck BasicBlock make_res_layer Bottleneck ResNetDCT BasicBlock make_res_layer Bottleneck ResNetDCT_Dynamic BasicBlock make_res_layer Bottleneck ResNetUpscaledDynamic BasicBlock make_res_layer Bottleneck ResNetUpscaledStatic ResNeXt make_res_layer Bottleneck SSDVGG L2Norm BBoxHead SharedFCBBoxHead ConvFCBBoxHead DoubleConvFCBBoxHead BasicResBlock BaseDetector CascadeRCNN DoubleHeadRCNN FasterRCNN FastRCNN FCOS GridRCNN HybridTaskCascade MaskRCNN MaskScoringRCNN RepPointsDetector RetinaNet RPN SingleStageDetector MaskTestMixin BBoxTestMixin RPNTestMixin TwoStageDetector Accuracy accuracy BalancedL1Loss balanced_l1_loss binary_cross_entropy mask_cross_entropy _expand_binary_labels CrossEntropyLoss cross_entropy sigmoid_focal_loss py_sigmoid_focal_loss FocalLoss _expand_binary_labels GHMR GHMC bounded_iou_loss BoundedIoULoss iou_loss IoULoss MSELoss smooth_l1_loss SmoothL1Loss weight_reduce_loss weighted_loss reduce_loss FCNMaskHead FusedSemanticHead GridHead HTCMaskHead MaskIoUHead BFP FPN HRFPN GeneralizedAttention NonLocal2D SingleRoIExtractor ResLayer ConvModule build_conv_layer conv_ws_2d ConvWS2d build_norm_layer Scale xavier_init bias_init_with_prob uniform_init normal_init kaiming_init last_zero_init ContextBlock DeformConvFunction ModulatedDeformConv DeformConvPack ModulatedDeformConvPack DeformConv ModulatedDeformConvFunction DeformRoIPoolingPack DeformRoIPoolingFunction ModulatedDeformRoIPoolingPack DeformRoIPooling MaskedConv2dFunction MaskedConv2d nms soft_nms RoIAlign RoIAlignFunction RoIPool RoIPoolFunction SigmoidFocalLoss SigmoidFocalLossFunction draw_from_npy zigZag draw_inputgate add_flops_counting_methods add_flops_counter_hook_function bn_flops_counter_hook reset_flops_count deconv_flops_counter_hook relu_flops_counter_hook get_model_parameters_number add_flops_mask flops_to_string params_to_string remove_flops_mask remove_batch_counter_hook_function start_flops_count add_batch_counter_variables_or_reset pool_flops_counter_hook empty_flops_counter_hook add_flops_mask_variable_or_reset add_batch_counter_hook_function get_model_complexity_info conv_flops_counter_hook remove_flops_counter_hook_function batch_counter_hook add_flops_counter_variable_or_reset is_supported_instance stop_flops_count upsample_flops_counter_hook linear_flops_counter_hook compute_average_flops_cost print_model_with_flops unblockshaped plot_dct dct_flatten_2d build_from_cfg Registry test_params_to_string multi_gpu_test single_gpu_test collect_results main parse_args encode loads ascontiguousarray float shape show subplot set_title transpose axis zip enumerate len _is_tensor_image COLOR_GRAY2RGB _is_numpy_image transpose from_numpy cvtColor byte isinstance FloatTensor squeeze transpose numpy is_tensor _is_tensor_image _is_numpy_image zip div_ shape int isinstance Number copyMakeBorder isinstance int Number isinstance shape round crop resize Number isinstance center_crop shape crop Number isinstance hflip five_crop vflip clip astype float32 astype float32 mean round clip COLOR_GRAY2RGB COLOR_RGB2GRAY astype float32 clip cvtColor uint8 astype COLOR_RGB2HSV_FULL COLOR_HSV2RGB_FULL cvtColor power clip astype float32 COLOR_GRAY2RGB COLOR_RGB2GRAY cvtColor dtype warpAffine int min ceil getRotationMatrix2D shape floor append abs max warpAffine radians cos shape sin array warpAffine radians cos shape sin array dtype radians tan getPerspectiveTransform cos float32 dot shape sqrt sin zeros warpPerspective array range dtype clip astype float32 dtype astype float32 log2 unique ceil float clip poisson len dtype rand copy salt_and_pepper data join Compose DataLoader ImageFolderDCT join sorted is_valid_file append expanduser keys walk str COLOR_BGR2RGB COLOR_BGR2YCrCb imread cvtColor validate warn gpu_id pretrained DistributedDataParallel DataParallel arch features cuda seed load_state_dict dirname valloader_upscaled_static format init_process_group distributed resume mkdir_p startswith manual_seed checkpoint load evaluate print isfile eval AverageMeter Bar ResNetDCT_Upscaled_Static sum test eval AverageMeter Bar weight constant_ bias bias xavier_uniform_ xavier_normal_ weight constant_ normal_ weight constant_ bias uniform_ weight constant_ bias kaiming_uniform_ bias weight kaiming_normal_ constant_ kaiming_init abs int max load load_state_dict MobileNetV2 MobileNetV2DCT MobileNetV2DCT_Deconv_Subset MobileNetV2DCT_Upscaled MobileNetV2DCT_Upscaled_Subset MobileNetV2DCT_Subpixel MobileNetV2DCT_Subpixel_Subset MobileNetV2DCT_Subset_woinp MobileNetV2DCT_Subset_woinp_from_scratch ResNet load_state_dict load_state_dict_from_url _resnet SE_ResNet50DCT resnet50 ResNet50DCT model_seresnet50dct shape float model_resnet50dct topk size t eq mul_ expand_as append sum max uniform_ data __name__ constant_ data xavier_normal uniform_ __name__ constant_ data uniform_ __name__ constant_ kaiming_normal data print orthogonal uniform_ __name__ constant_ print apply asarray arange plot numbers enumerate len format print Compose ImageFolderDCT DataLoader div_ enumerate len time format print Compose ImageFolderDCT DataLoader iter div_ next range enumerate len str imread cvtColor COLOR_BGR2YCrCb print Compose DataLoader ImageFolder div_ zeros enumerate 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 decode _minimal_ext_cmd exists get_hash cythonize Extension format realpath dirname _init_dist_mpi set_start_method _init_dist_slurm _init_dist_pytorch int set_device init_process_group device_count int str format init_process_group set_device device_count getoutput seed manual_seed_all manual_seed basicConfig setLevel get_dist_info getLogger get_classes isinstance model load_checkpoint warn eval build_detector fromfile to Compose cfg dict test_pipeline device bool concat_list isinstance concatenate imshow_det_bboxes astype copy vstack randint imread bgr2rgb imshow show_result figure items isinstance OrderedDict mean item Tensor sum dict parse_losses model log_level _non_dist_train get_root_logger _dist_train pop get hasattr endswith search copy named_parameters getattr append optim module workflow log_level MMDistributedDataParallel DistSamplerSeedHook cuda run total_epochs issubclass build_optimizer checkpoint_config work_dir module get val CocoDistEvalRecallHook load_from resume_from register_training_hooks resume type optimizer DistOptimizerHook lr_config DistEvalmAPHook isinstance CocoDataset load_checkpoint register_hook CocoDistEvalmAPHook Runner log_config Fp16OptimizerHook workflow log_level cuda run total_epochs build_optimizer checkpoint_config work_dir optimizer_config get load_from resume_from register_training_hooks resume optimizer lr_config load_checkpoint Runner log_config Fp16OptimizerHook multi_apply images_to_levels any sum range cat len append stack squeeze assign_and_sample zeros_like PseudoSampler pos_gt_bboxes size pos_weight anchor_inside_flags unmap sample new_zeros build_assigner assign pos_inds bbox2delta allowed_border neg_inds pos_bboxes assigner uint8 type new_full clamp long new_full zeros_like calc_region size sqrt log2 floor full_like item append zeros float sum long range len multi_apply images_to_levels any append sum range cat len ga_assigner build_sampler ga_sampler PseudoSampler zeros_like reshape pos_gt_bboxes size unmap build_assigner assign pos_inds sample neg_inds pos_bboxes multi_apply images_to_levels any sum range cat len assign_and_sample zeros_like PseudoSampler pos_gt_bboxes size pos_weight unmap new_zeros build_assigner assign pos_inds sample neg_inds assigner BaseAssigner isinstance BaseSampler isinstance build_sampler sampler build_assigner assign sample assigner multi_apply cat bbox2delta size new_zeros squeeze new_zeros clamp size min max stack unsqueeze div_ float log exp clamp size repeat expand_as view_as abs log addcmul Tensor ndarray isinstance clone bbox_flip new_full new_zeros append cat enumerate cpu append unique numpy clamp minimum T astype maximum float32 zeros range items eval is_str list format isinstance COCOeval print evaluate summarize is_str COCO accumulate getImgIds loadRes fast_eval_recall array enumerate load getAnnIds is_str mean getImgIds eval_recalls append zeros loadAnns array range len tolist dict append float xyxy2xywh range len dict append float xyxy2xywh range len decode isinstance dict append float xyxy2xywh range len dump format ndarray isinstance segm2json dict proposal2json det2json arange ones hstack maximum zeros sum range minimum zeros_like len argsort zeros bbox_overlaps range enumerate zeros_like len argsort bbox_overlaps zeros argmax max enumerate append zeros range len eps cumsum tuple maximum average_precision argsort enumerate mean any vstack print_map_summary item zip append zeros range get_cls_results len get_classes table print len is_str AsciiTable append zeros range enumerate sum sort hstack copy zeros float argmax fliplr range enumerate array isinstance min set_recall_param print_recall_summary _recalls array append zeros bbox_overlaps range len arange table insert print size tolist AsciiTable append array enumerate show ndarray plot isinstance xlabel tolist axis ylabel figure show ndarray plot isinstance xlabel tolist axis ylabel figure hasattr patch_norm_fp32 modules half children isinstance half patch_forward_method float forward ndarray isinstance Iterable Tensor Mapping list map cat mask_size imresize size astype maximum new_zeros int32 device append to numpy range _pair tolist append slice_list range len pop new_full sort copy nms_op new_zeros getattr append range cat nms nms_thr sort min clone max_num zip append bbox_mapping_back cat append mean bbox_mapping_back zip Tensor isinstance average mean array _take_tensors _flatten_dense_tensors zip _unflatten_dense_tensors OrderedDict all_reduce copy_ div_ append type values all_reduce _allreduce_coalesced get_world_size div_ uint8 transpose size astype ascontiguousarray append array range map get deepcopy isinstance append build_dataset range len isinstance ConcatDataset _concat_dataset build_from_cfg RepeatDataset copy get get_dist_info DistributedSampler DataLoader DistributedGroupSampler Tensor ndarray isinstance isinstance block Sequential build_conv_layer append range expansion isinstance log abs e where float weight_reduce_loss new_full size squeeze expand size weight_reduce_loss binary_cross_entropy_with_logits _expand_binary_labels float squeeze arange type_as sigmoid pow weight_reduce_loss binary_cross_entropy_with_logits _sigmoid_focal_loss weight_reduce_loss view clamp view zeros_like size min where abs max abs where get_enum sum reduce_loss dict conv_layer pop copy size view pop str setdefault norm_layer copy parameters _specify_ddp_gpu_num hasattr hasattr hasattr hasattr float Sequential isinstance constant_init ndarray isinstance new_zeros Tensor to numpy is_cuda ndarray soft_nms_cpu isinstance Tensor numpy get_ticklabels set_tick_params concatenate squeeze set_xlabel average set_visible set_ylabel savefig save barplot get_xticks enumerate append range insert load subplot list arange print reshape savefig figure heatmap flops_model get_model_parameters_number input_constructor stop_flops_count add_flops_counting_methods start_flops_count compute_average_flops_cost new_empty print_model_with_flops print compute_average_flops_cost apply sum __get__ reset_flops_count apply __batch_counter__ is_supported_instance modules add_batch_counter_hook_function apply remove_batch_counter_hook_function apply add_batch_counter_variables_or_reset apply apply apply isinstance numel shape affine prod groups kernel_size out_channels in_channels list kernel_size out_channels groups in_channels expand sum prod print len register_forward_hook hasattr remove hasattr is_supported_instance register_forward_hook is_supported_instance isinstance hasattr remove is_supported_instance hasattr is_supported_instance shape int unblockshaped astype shape sqrt imshow title savefig figure dct_flatten_2d pop get items setdefault copy is_str isclass params_to_string assert_equal update show_result size ProgressBar eval append dataset range enumerate len update get_dist_info size collect_results ProgressBar eval append dataset range enumerate len rstrip tensor broadcast list get_dist_info mkdtemp encode append range dump format bytearray zip load join barrier extend rmtree mkdir_or_exist full str add_argument ArgumentParser local_rank config model tmpdir coco_eval launcher MMDistributedDataParallel show get_dist_info build_detector fromfile parse_args build_dataset get dump CLASSES init_dist single_gpu_test build_dataloader wrap_fp16_model json_out eval results2json join load_checkpoint coco multi_gpu_test out MMDataParallel | # Notice: This repository is deprecated, please use https://github.com/calmevtime/DCTNet. # Learning in the Frequency Domain This is the source code for the CVPR'20 paper entitled "Learning in the Frequency Domain" (https://arxiv.org/abs/2002.12416). ## Highlights * We propose a method of learning in the frequency domain (using DCT coefficients as input), which requires little modification to the existing CNN models that take RGB input. We validate our method on ResNet50 and MobileNetV2 for the image classification task and Mask R-CNN for the instance segmentation task. * We show that learning in the frequency domain better preserves image information in the pre-processing stage than the conventional spatial downsampling approach (spatially resizing the images to 224×224, the default input size of most CNN models) and consequently achieves improved accuracy, i.e., +1.41% on ResNet-50 and +0.66% on MobileNetV2 for the ImageNet classification task, +0.8% on Mask R-CNN for both object detection and instance segmentation tasks. * We analyze the spectral bias from the frequency perspective and show that the CNN models are more sensitive to low-frequency channels than high-frequency channels, similar to the human visual system (HVS). * We propose a learning-based dynamic channel selection method to identify the trivial frequency components for static removal during inference. Experiment results on ResNet-50 show that one can prune up to 87.5% of the frequency channels using the proposed channel selection method with no or little accuracy degradation in the ImageNet classification task. * To the best of our knowledge, this is the first work that explores learning in the frequency domain for object detection and instance segmentation. Experiment results on Mask R-CNN show that learning in the frequency domain can achieve a 0.8% average precision improvement for the instance segmentation task on the COCO dataset. Please refer to the [image classfication](classification) and [instance segmentation](segmentation) sections for more details. | 831 |
PYFTS/pyFTS | ['time series'] | ['Forecasting in Non-stationary Environments with Fuzzy Time Series'] | pyFTS/data/DowJones.py pyFTS/data/AirPassengers.py pyFTS/hyperparam/Evolutionary.py pyFTS/models/nonstationary/partitioners.py pyFTS/fcm/common.py pyFTS/common/transformations/boxcox.py pyFTS/common/transformations/transformation.py pyFTS/benchmarks/Measures.py pyFTS/benchmarks/naive.py pyFTS/conf.py pyFTS/models/ismailefendi.py pyFTS/models/multivariate/mvfts.py pyFTS/tests/pwfts.py pyFTS/models/yu.py pyFTS/data/EURUSD.py pyFTS/models/multivariate/grid.py pyFTS/partitioners/Singleton.py pyFTS/partitioners/som.py pyFTS/data/sunspots.py pyFTS/partitioners/parallel_util.py pyFTS/partitioners/Entropy.py pyFTS/models/hwang.py pyFTS/common/SortedCollection.py pyFTS/models/nonstationary/nsfts.py pyFTS/benchmarks/BSTS.py pyFTS/data/NASDAQ.py pyFTS/tests/sfts.py pyFTS/partitioners/Util.py pyFTS/common/FuzzySet.py pyFTS/models/nonstationary/flrg.py pyFTS/models/ensemble/ensemble.py pyFTS/data/__init__.py pyFTS/data/Enrollments.py pyFTS/fcm/GD.py pyFTS/models/multivariate/__init__.py pyFTS/models/ensemble/__init__.py pyFTS/common/transformations/adaptiveexpectation.py pyFTS/models/tsaur.py setup.py pyFTS/probabilistic/Mixture.py pyFTS/models/multivariate/wmvfts.py pyFTS/common/transformations/som.py pyFTS/tests/distributed.py pyFTS/partitioners/partitioner.py pyFTS/benchmarks/Util.py pyFTS/probabilistic/__init__.py pyFTS/tests/general.py pyFTS/tests/transformations.py pyFTS/common/fts.py pyFTS/tests/nonstationary.py pyFTS/tests/__init__.py pyFTS/models/nonstationary/util.py pyFTS/data/TAIEX.py pyFTS/models/nonstationary/common.py docs/conf.py pyFTS/tests/hyperparam.py pyFTS/data/Malaysia.py pyFTS/__init__.py pyFTS/models/nonstationary/cvfts.py pyFTS/models/multivariate/flrg.py pyFTS/benchmarks/ResidualAnalysis.py pyFTS/models/multivariate/variable.py pyFTS/common/transformations/roi.py pyFTS/models/multivariate/granular.py pyFTS/models/__init__.py pyFTS/data/EURGBP.py pyFTS/models/incremental/__init__.py pyFTS/data/Ethereum.py pyFTS/common/FLR.py pyFTS/data/Bitcoin.py pyFTS/models/multivariate/partitioner.py pyFTS/models/nonstationary/honsfts.py pyFTS/tests/fcm_fts.py pyFTS/data/lorentz.py pyFTS/models/incremental/TimeVariant.py pyFTS/models/chen.py pyFTS/tests/test_SOMTransformation.py pyFTS/common/flrg.py pyFTS/benchmarks/knn.py pyFTS/common/Transformations.py pyFTS/models/multivariate/common.py pyFTS/tests/seasonal.py pyFTS/models/cheng.py pyFTS/benchmarks/quantreg.py pyFTS/data/henon.py pyFTS/fcm/Activations.py pyFTS/models/multivariate/FLR.py pyFTS/common/transformations/scale.py pyFTS/models/ensemble/multiseasonal.py pyFTS/tests/spark.py pyFTS/models/seasonal/SeasonalIndexer.py pyFTS/models/seasonal/cmsfts.py pyFTS/models/seasonal/common.py pyFTS/common/Membership.py pyFTS/common/Composite.py pyFTS/common/transformations/autoencoder.py pyFTS/data/GBPUSD.py pyFTS/data/SONDA.py pyFTS/benchmarks/gaussianproc.py pyFTS/hyperparam/GridSearch.py pyFTS/models/incremental/IncrementalEnsemble.py pyFTS/models/nonstationary/__init__.py pyFTS/models/seasonal/msfts.py pyFTS/tests/cmsfts.py pyFTS/benchmarks/benchmarks.py pyFTS/benchmarks/__init__.py pyFTS/data/SP500.py pyFTS/probabilistic/ProbabilityDistribution.py pyFTS/models/ifts.py pyFTS/models/song.py pyFTS/common/tree.py pyFTS/data/logistic_map.py pyFTS/distributed/dispy.py pyFTS/models/seasonal/sfts.py pyFTS/data/rossler.py pyFTS/data/mackey_glass.py pyFTS/tests/multivariate.py pyFTS/common/Util.py pyFTS/benchmarks/Tests.py pyFTS/partitioners/CMeans.py pyFTS/tests/ensemble.py pyFTS/models/multivariate/cmvfts.py pyFTS/partitioners/Grid.py pyFTS/models/sadaei.py pyFTS/common/transformations/normalization.py pyFTS/models/pwfts.py pyFTS/hyperparam/random_search.py pyFTS/partitioners/Simple.py pyFTS/fcm/fts.py pyFTS/common/transformations/smoothing.py pyFTS/partitioners/__init__.py pyFTS/benchmarks/arima.py pyFTS/common/transformations/differential.py pyFTS/distributed/spark.py pyFTS/fcm/GA.py pyFTS/data/artificial.py pyFTS/hyperparam/Util.py pyFTS/partitioners/FCM.py pyFTS/probabilistic/kde.py pyFTS/models/hofts.py pyFTS/partitioners/SubClust.py pyFTS/data/common.py pyFTS/models/seasonal/partitioner.py pyFTS/partitioners/Huarng.py pyFTS/common/transformations/trend.py pyFTS/hyperparam/mvfts.py pyFTS/models/nonstationary/perturbation.py pyFTS/data/INMET.py ARIMA print_interval_statistics run_point2 process_probabilistic_jobs run_point pftsExploreOrderAndPartitions train_test_time multivariate_sliding_window_benchmarks2 common_process_point_jobs plot_point compareModelsPlot compareModelsTable get_probabilistic_methods process_interval_jobs2 distributed_model_train_test_time get_point_multivariate_methods plotCompared run_interval print_distribution_statistics common_process_probabilistic_jobs __build_model mv_run_point2 process_point_jobs mv_run_probabilistic2 process_probabilistic_jobs2 get_benchmark_interval_methods run_probabilistic2 get_benchmark_point_methods mv_run_interval2 run_interval2 run_probabilistic sliding_window_benchmarks2 print_point_statistics plot_compared_series common_process_time_jobs __pop simpleSearch_RMSE sliding_window_benchmarks process_point_jobs2 get_point_methods get_interval_methods process_interval_jobs get_benchmark_probabilistic_methods common_process_interval_jobs ARIMA GPR KNearestNeighbors sharpness pinball_mean brier_score smape pinball winkler_mean resolution coverage logarithm_score mape rmse_interval get_point_ahead_statistics winkler_score get_distribution_statistics nmrse get_distribution_ahead_statistics get_interval_ahead_statistics crps acf mape_interval UStatistic rmse get_point_statistics get_interval_statistics TheilsInequality Naive QuantileRegression compare_residuals single_plot_residuals ljung_box_test plot_residuals_by_model residuals BoxLjungStatistic post_hoc_tests BoxPierceStatistic test_mean_equality format_experiment_table process_common_data2 insert_benchmark save_dataframe_point process_common_data check_ignore_list unified_scaled_interval_pinball point_dataframe_synthetic_columns save_dataframe_probabilistic interval_dataframe_synthetic_columns tabular_dataframe_columns probabilistic_dataframe_synthetic_columns open_benchmark_db find_best unified_scaled_interval unified_scaled_point interval_dataframe_analytic_columns save_dataframe_interval analytical_data_columns probabilistic_dataframe_analytic_columns analytic_tabular_dataframe simple_synthetic_dataframe cast_dataframe_to_synthetic_interval unified_scaled_probabilistic plot_dataframe_interval_pinball create_benchmark_tables plot_dataframe_point plot_dataframe_probabilistic scale cast_dataframe_to_synthetic_probabilistic point_dataframe_analytic_columns cast_dataframe_to_synthetic get_dataframe_from_bd check_replace_list plot_dataframe_interval extract_measure scale_params cast_dataframe_to_synthetic_point base_dataframe_columns stats FuzzySet IndexedFLR FLR generate_non_recurrent_flrs generate_recurrent_flrs generate_high_order_recurrent_flr generate_indexed_flrs FLRG FTS fuzzyfy set_ordered get_maximum_membership_fuzzyset fuzzyfy_series_old get_maximum_membership_fuzzyset_index __binary_search fuzzyfy_instance check_bounds_index fuzzyfy_instances fuzzyfy_series FuzzySet grant_bounds check_bounds get_fuzzysets trimf trapmf gaussmf sigmf singleton bellmf SortedCollection FLRGTreeNode build_tree_without_order FLRGTree flat persist_env plot_density_rectange persist_obj sliding_window load_env plot_probability_distributions plot_interval2 plot_rules enumerate2 plot_distribution_tiled draw_sets_on_axis show_and_save_image plot_interval plot_compared_intervals_ahead plot_distribution2 uniquefilename plot_distribution load_obj AdaptiveExpectation AutoencoderTransformation BoxCox Differential Normalization ROI Scale AveragePooling ExponentialSmoothing MaxPooling MovingAverage SOMTransformation Transformation LinearTrend get_data get_dataframe SignalEmulator random_walk generate_gaussian_linear generate_uniform_linear _append generate_sinoidal_periodic_gaussian generate_linear_periodic_gaussian white_noise get_data get_dataframe get_dataframe get_data get_dataframe get_data get_dataframe get_data get_dataframe get_data get_dataframe get_data get_dataframe get_data get_dataframe get_data get_dataframe get_dataframe get_data get_data get_dataframe get_data get_data get_dataframe get_data get_dataframe get_data get_dataframe get_data get_dataframe get_data get_dataframe get_data get_dataframe get_data get_dataframe create_multivariate_model slave_train_univariate create_univariate_model share_parameters distributed_train slave_train_multivariate slave_forecast_multivariate get_partitioner create_spark_conf get_clustered_partitioner distributed_predict get_variables slave_forecast_univariate tanh relu sigmoid softmax step FuzzyCognitiveMap FCM_FTS crossover mutation genotype evaluate GeneticAlgorithm process_experiment phenotype persist_statistics elitism initial_population random_genotype tournament execute log_result GD crossover mutation_lags mutation genotype evaluate GeneticAlgorithm double_tournament process_experiment phenotype persist_statistics elitism initial_population random_genotype tournament execute lag_crossover2 log_result process_jobs cluster_method dict_individual execute crossover mutation genotype evaluate mutation_random_search phenotype_mf process_experiment phenotype random_param persist_statistics random_search random_genotype execute phenotype_partitioner log_result crossover_variable_params mutate_variable_params execute open_hyperparam_db create_hyperparam_tables insert_hyperparam ConventionalFTS ConventionalFLRG TrendWeightedFLRG TrendWeightedFTS HighOrderFTS WeightedHighOrderFLRG WeightedHighOrderFTS HighOrderFLRG HighOrderFTS WeightedIntervalFTS IntervalFTS ImprovedWeightedFLRG ImprovedWeightedFTS visualize_distributions ProbabilisticWeightedFTS ProbabilisticWeightedFLRG ExponentialyWeightedFTS ExponentialyWeightedFLRG ConventionalFTS MarkovWeightedFTS MarkovWeightedFLRG WeightedFTS WeightedFLRG AllMethodEnsembleFTS SimpleEnsembleFTS sampler EnsembleFTS SeasonalEnsembleFTS train_individual_model IncrementalEnsembleFTS Retrainer ClusteredMVFTS fuzzyfy_instance fuzzyfy_instance_clustered MultivariateFuzzySet FLR FLRG GranularWMVFTS IncrementalGridCluster GridCluster product_dict MVFTS MultivariatePartitioner Variable WeightedFLRG WeightedMVFTS window_index fuzzySeries fuzzify check_bounds_index FuzzySet check_bounds HighOrderNonstationaryFLRG ConditionalVarianceFTS NonStationaryFLRG HighOrderNonStationaryFTS HighOrderNonStationaryFLRG WeightedNonStationaryFLRG ConventionalNonStationaryFLRG NonStationaryFTS WeightedNonStationaryFTS simplenonstationary_gridpartitioner_builder SimpleNonStationaryPartitioner PolynomialNonStationaryPartitioner linear exponential periodic polynomial plot_sets plot_sets_conditional ContextualSeasonalFLRG ContextualMultiSeasonalFTS DateTime strip_datepart FuzzySet MultiSeasonalFTS TimeGridPartitioner LinearSeasonalIndexer DateTimeSeasonalIndexer DataFrameSeasonalIndexer SeasonalIndexer SeasonalFTS SeasonalFLRG c_means distance CMeansPartitioner entropy informationGain splitAbove splitBelow PMF bestSplit EntropyPartitioner fuzzy_distance fuzzy_cmeans membership FCMPartitioner GridPartitioner PreFixedGridPartitioner HuarngPartitioner explore_partitioners Partitioner SimplePartitioner SingletonPartitioner SOMPartitioner subclust imax SubClustPartitioner explore_partitioners plot_partitioners plot_sets KernelSmoothing Mixture ProbabilityDistribution from_point get_dataset gen_dates MyTestCase wait max list open_benchmark_db result synthesis_method append get sliding_window close stop_dispy_cluster job enumerate submit items __pop fts_method Variable print start_dispy_cluster experiment_method get submit product open_benchmark_db sliding_window print wait close result stop_dispy_cluster start_dispy_cluster experiment_method synthesis_method append job max enumerate __pop model wait get_probabilistic_methods open_benchmark_db result synthesis_method append get get_benchmark_interval_methods update get_benchmark_point_methods partitioner sliding_window append_transformation close stop_dispy_cluster job enumerate submit __pop deepcopy is_high_order print method tqdm start_dispy_cluster get_point_methods experiment_method get_interval_methods get_benchmark_probabilistic_methods len get str time partitions append_transformation order get_point_statistics shortname benchmark_only transformation fit get str time partitions append_transformation order get_interval_statistics alpha shortname benchmark_only transformation fit get str time partitions append_transformation order get_distribution_statistics alpha shortname has_seasonality benchmark_only transformation fit partitioner_method append_transformation fts_method get get_point_ahead_statistics time list get_point_statistics keys predict fit get time get_point_ahead_statistics list __build_model get_point_statistics keys predict fit get time list keys get_interval_statistics get_interval_ahead_statistics predict fit get time list keys __build_model get_interval_statistics get_interval_ahead_statistics predict fit get time list get_distribution_statistics get_distribution_ahead_statistics keys predict fit get time list get_distribution_statistics __build_model get_distribution_ahead_statistics has_seasonality keys predict fit append deepcopy extend insert_benchmark append deepcopy extend insert_benchmark process_common_data common_process_point_jobs process_common_data2 common_process_point_jobs range append deepcopy extend insert_benchmark process_common_data common_process_interval_jobs process_common_data2 range common_process_interval_jobs append deepcopy extend insert_benchmark common_process_probabilistic_jobs process_common_data common_process_probabilistic_jobs process_common_data2 range arange print get_point_statistics enumerate len get_interval_statistics print print get_distribution_statistics arange plot insert min max arange add_subplot max str ndarray set_xlabel tolist forecast plot_interval legend append plot insert set_xlim shortname enumerate isinstance min order forecast_interval set_ylabel figure get_legend_handles_labels set_ylim set_title plot set_xlabel set_xlim add_subplot set_ylabel figure legend get_legend_handles_labels range set_ylim len plot suptitle add_axes figure legend get_legend_handles_labels str suptitle table set_yticks add_axes mape set_xticks figure rmse append round sets model plot_surface str set_title set_xlabel forecast rmse_interval append meshgrid range plot insert append_transformation set_xlim add_axes show_and_save_image set_zlabel enumerate Axes3D forecast_interval set_ylabel figure rmse train array set_ylim subplots arange sets max str set_title set_xlabel forecast legend append plot insert set_xlim ProbabilisticWeightedFTS tight_layout show_and_save_image train min forecast_interval set_ylabel get_legend_handles_labels set_ylim get time process_common_data2 fts_method open_benchmark_db print sliding_window fit close enumerate append GridPartitioner common_process_time_jobs predict __pop get __pop process_common_data2 open_benchmark_db print sliding_window close enumerate common_process_time_jobs predict fit nanmean var arange len array isinstance array isinstance array isinstance arange subtract size append array sqrt nansum len sharpness append arange len append nansum find_le enumerate isinstance ProbabilityDistribution len float enumerate isinstance ProbabilityDistribution len float enumerate get list arange values order is_multivariate get_data UStatistic mape rmse append round array max_lag predict len UStatistic mape rmse range len get sharpness list arange pinball_mean values order resolution coverage is_multivariate winkler_mean append round max_lag predict len winkler_score pinball round range len get list time arange brier_score values crps is_multivariate append round max_lag predict len brier_score logarithm_score round range crps len acorr_ljungbox ppf cdf append enumerate acorr_ljungbox order extend mean append std predict residuals subplots set_title plot acorr set_xlabel order tight_layout mean set_ylabel hist show_and_save_image probplot shortname std enumerate predict residuals subplots set_title plot acorr set_xlabel tight_layout set_ylabel hist show_and_save_image probplot acf arange len acf arange len nanmin str insert DataFrame unique append round range values len format arange values print friedman_aligned_ranks_test append quade_test len finner_test holm_test arange bonferroni_dunn_test append len execute connect create_benchmark_tables execute commit cursor execute commit cursor benchmark_only connect str sort_values unique get_dataframe_from_bd groupby format Value sort_values nanmean nanstd unique append DataFrame columns base_dataframe_columns analytical_data_columns unique append DataFrame len insert deepcopy sorted point_dataframe_analytic_columns partitions uniquefilename name DataFrame order extend to_csv nanmean nanstd point_dataframe_synthetic_columns shortname round keys append len synthetize_measures insert analytic_columns to_csv analytical_data_columns unique append DataFrame read_csv extract_measure nanmean nanstd append round nanmax nanmin print subplots check_ignore_list str sorted set_title find_best analytical_data_columns append tight_layout show_and_save_image scale boxplot keys check_replace_list print extract_measure extend scale_params stats read_csv subplots check_ignore_list from_dict str sorted set_title find_best analytical_data_columns append replace tight_layout show_and_save_image boxplot keys uniquefilename check_replace_list extract_measure to_csv read_csv deepcopy sorted interval_dataframe_synthetic_columns partitions uniquefilename name DataFrame order extend interval_dataframe_analytic_columns to_csv nanmean nanstd shortname round keys append len insert extract_measure nanmean nanstd append round subplots check_ignore_list str sorted set_title find_best analytical_data_columns append tight_layout show_and_save_image scale boxplot keys check_replace_list print extract_measure extend scale_params read_csv subplots check_ignore_list from_dict str sorted set_title find_best analytical_data_columns append replace tight_layout show_and_save_image boxplot keys uniquefilename check_replace_list extract_measure to_csv read_csv set_ylim subplots check_ignore_list str sorted set_title find_best analytical_data_columns append tight_layout show_and_save_image scale boxplot keys check_replace_list print extract_measure extend scale_params read_csv subplots check_ignore_list from_dict str sorted set_title find_best analytical_data_columns append replace tight_layout show_and_save_image boxplot keys uniquefilename check_replace_list extract_measure to_csv read_csv deepcopy sorted probabilistic_dataframe_synthetic_columns partitions uniquefilename name DataFrame order extend to_csv nanmean nanstd probabilistic_dataframe_analytic_columns shortname round keys append len insert extract_measure nanmean nanstd append round subplots check_ignore_list str sorted set_title find_best analytical_data_columns append tight_layout show_and_save_image scale boxplot keys check_replace_list print extract_measure extend scale_params read_csv subplots check_ignore_list from_dict str sorted set_title find_best analytical_data_columns append replace tight_layout show_and_save_image boxplot keys uniquefilename check_replace_list extract_measure to_csv read_csv append FLR arange len FLR arange flatten append len generate_recurrent_flrs IndexedFLR arange apply get_data flatten fuzzyfy_series get_season_of_data append len len get isinstance sets fuzzyfy_instance fuzzyfy_instances append ordered_sets set_ordered __binary_search membership zeros len fuzzyfy_instance set_ordered append set_ordered set_ordered array fuzzyfy_instance append name items list set_ordered name fuzzyfy_instance argwhere append check_bounds ravel max enumerate round property isinstance FLRGTreeNode getChildren appendChild arange plot_density_rectange add_subplot max set_xlabel legend append plot insert set_xlim ScalarMappable show_and_save_image Normalize forecast_ahead_distribution get_cmap enumerate min order set_ylabel figure get_legend_handles_labels forecast_ahead_interval set_ylim set_cmap columns set_label set_clim index colorbar add_collection Rectangle append PatchCollection array set_array plot add_subplot figure legend get_legend_handles_labels enumerate set_cmap set_array set_label set_clim resolution bins Scale apply add_collection colorbar density Rectangle append PatchCollection array enumerate get int set_label subplots arange plot insert min ScalarMappable ColorbarBase Normalize make_axes quantile append fill_between get_cmap max len format subplots set_title plot set_xlabel axvline tight_layout range arange plot insert min max get subplots arange plot insert len subplots show list arrow append range plot set_xticklabels LHS set_xlim tight_layout draw_sets_on_axis keys enumerate RHS order set_xticks centroid len list subplots plot set_xticklabels set_yticklabels set_ylim set_xlim len extend set_yticks parameters axhline set_xticks centroid range ordered_sets enumerate split show close savefig get int arange tqdm round dump_session load_session get_dataframe array minimum normal arange extend maximum full minimum normal max arange extend maximum full minimum normal max arange extend maximum full minimum arange extend maximum uniform full append generate_gaussian_linear generate_uniform_linear range insert tolist extend array range len urlretrieve is_file Path append arange to_datetime append arange tolist len get SparkConf setMaster setAppName set list SimplePartitioner append_complex append keys value MultivariatePartitioner MultivariateFuzzySet build_index append append_set Variable value append get_partitioner value create_univariate_model train create_univariate_model predict value get_variables get_clustered_partitioner from_records train create_multivariate_model from_records predict create_multivariate_model explanatory_variables items list is_clustered sets name data_label order alpha_cut append type lags max_lag values broadcast get create_spark_conf get extend collect create_spark_conf sum dict append random_genotype range FCM_FTS get sliding_window phenotype nanmean nanstd rmse append predict len genotype reshape ndenumerate shape append range enumerate arange shape uniform randint range clip insert sorted mutation id elitism crossover append exception range get format job enumerate pop submit time stdout int evaluate print extend initial_population tournament print print insert_hyperparam get time format arange print GeneticAlgorithm process_experiment open_hyperparam_db stop_dispy_cluster start_dispy_cluster get fuzzyfy partitions arange print order dot activation_function len randint get create_random_individual trimf fts_method trapmf gaussmf GridPartitioner EntropyPartitioner fit WeightedHighOrderFTS nansum sum lags len tournament int arange min append round range int lag_crossover2 uniform randint float round len int arange min append randint max len normal int mutation_lags min max crossover_operator selection_operator initial_operator uniform mutation_operator elitism_operator evaluation_operator persist_statistics log_result WeightedHighOrderFTS get trimf sliding_window len WeightedHighOrderFTS trapmf gaussmf rmse append GridPartitioner EntropyPartitioner predict fit stdout list format print keys insert_hyperparam job exception enumerate pop submit product process_jobs append dict_individual len get tolist random_param append len randint get Variable phenotype_mf append phenotype_partitioner enumerate GridPartitioner EntropyPartitioner gaussmf trimf trapmf pop copy WeightedMVFTS get arange index crossover_variable_params int float round uniform get pop remove mutate_variable_params random_param append len get deepcopy remove pop arange mutate_variable_params random_param uniform append randint range len normal int min randint max get time format arange print process_experiment WeightedHighOrderFTS execute mutation mutation_operator evaluate evaluation_operator random_genotype range random_individual execute connect create_hyperparam_tables execute commit cursor execute commit cursor grid GridSpec barplot heatmap ordered_sets subplot list sorted set_title range get set_xticklabels set_tick_params keys enumerate rhs_unconditional_probability set_ylabel set_xticks figure zeros len insert min nanpercentile append max str partitions persist_obj sets name print append_transformation ContextualMultiSeasonalFTS train transformation fuzzyfy get search append list keys product values append array enumerate array argwhere append ravel max enumerate isinstance GridPartitioner SimpleNonStationaryPartitioner subplots arange plot set_xlabel get_legend_handles_labels len polyval extend tight_layout set_ylabel membership show_and_save_image legend get_color append xticks ordered_sets enumerate perturbate_parameters subplots arange plot set_xlabel tight_layout conditional_perturbation_factors forecast ordered_sets set_ylabel legend get_color show_and_save_image get_legend_handles_labels enumerate len hour tm_yday value second isinstance weekday strptime month minute day year reduce isinstance add distance reduce range add sum len PMF list sort splitBelow set flatten splitAbove append len reduce isinstance add len reduce add fuzzy_distance membership zeros range enumerate str plot_partitioners print cpu_count tolist append argmax imax norm exp reshape min vstack zeros sum max range str list set_title name set_ticks parameters set_ticklabels centroid round keys set_ylim plot_sets p name __name__ ProbabilityDistribution set to_datetime get_dataframe append len | # pyFTS - Fuzzy Time Series for Python [](http://perso.crans.org/besson/LICENSE.html) [](https://www.python.org/) [](https://pypi.python.org/pypi/pyFTS/) ## What is pyFTS Library? This package is intended for students, researchers, data scientists or whose want to exploit the Fuzzy Time Series methods. These methods provide simple, easy to use, computationally cheap and human-readable models, suitable for statistic laymans to experts. This project is continously under improvement and contributors are well come. ## How to reference pyFTS? [](https://doi.org/10.5281/zenodo.597359) Silva, P. C. L. et al. *pyFTS: Fuzzy Time Series for Python.* Belo Horizonte. 2018. DOI: 10.5281/zenodo.597359. Url: <http://doi.org/10.5281/zenodo.597359> | 832 |
PaginDm/keras-L1-pruning | ['network pruning'] | ['Structured Pruning of Deep Convolutional Neural Networks'] | utils.py pruning.py Pruner plot_and_save_stats prune_model int sorted list layers items name abs mean Surgeon operate reverse add_job startswith append sum std range len plot xlabel grid savefig figure legend append range len | # keras-L1-pruning Prune simply model with your custom loss and optimizer. Prune only convs. Implement this scheme:  ## Todo list: - [x] Convs L1-pruning by percent (https://openreview.net/pdf?id=rJqFGTslg) - [x] Using with custom optimizers and losses - [x] Limit the pruning part by relative values - [ ] Pruning flatten layer - [ ] Jump to tensorflow>=2.0 | 833 |
PanZiqiAI/disentangled-information-bottleneck | ['adversarial attack', 'out of distribution detection'] | ['Disentangled Information Bottleneck'] | methods/disenib_fc/config.py shared_libs/custom_packages/custom_io/show_progress_py3.py train_disenib_shapes.py methods/disenib_fc/dataloader.py shared_libs/custom_packages/custom_pytorch/layers.py shared_libs/utils/evaluations.py train_disenib.py methods/disenib_conv_mnist/dataloader.py methods/disenib_conv_sprites/dataloader.py shared_libs/dataset_apis/disentangling/datasets.py train_disenib_sprites.py shared_libs/custom_packages/custom_io/config.py shared_libs/modellib/fc.py shared_libs/custom_packages/custom_basic/visualizer.py shared_libs/dataset_apis/classification/datasets.py train_lagrangian.py shared_libs/dataset_apis/classification/transforms.py methods/disenib_conv_shapes/dataloader.py methods/lagrangian_fc/config.py shared_libs/custom_packages/custom_io/show_progress_py2.py shared_libs/utils/criterions.py methods/disenib_conv_shapes/config.py shared_libs/utils/operations.py methods/disenib_conv_mnist/config.py methods/disenib_conv_sprites/disen_ib.py train_disenib_mnist.py methods/disenib_conv_shapes/disen_ib.py methods/lagrangian_fc/ib_lagrangians.py shared_libs/custom_packages/custom_basic/operations.py shared_libs/custom_packages/custom_basic/metrics.py shared_libs/custom_packages/custom_io/logger.py shared_libs/custom_packages/custom_pytorch/operations.py shared_libs/dataset_apis/__init__.py shared_libs/custom_packages/custom_pytorch/base_models.py methods/disenib_conv_sprites/config.py methods/disenib_conv_mnist/disen_ib.py shared_libs/dataset_apis/classification/utils.py methods/disenib_fc/disenib.py shared_libs/modellib/conv.py methods/lagrangian_fc/dataloader.py ConfigTrain generate_data DisenIB ConfigTrain generate_data ShapesWithLabel DisenIB ConfigTrain generate_data SpritesWithLabel DisenIB ConfigTrain generate_data DisenIB ConfigTrain generate_data IBLagrangianModel BestPerfMeter TriggerLambda EMA EMAPyTorchModel TriggerPeriod _TimersManager StopWatch conf_interval FreqCounter TimersController mean_accuracy check_container chk_d TempDirManager is_tuple_list fet_d TempKwargsManager chk_ns ValidContainer IterCollector visualize_latent_by_tsne gray2heat plot_elapsed_scalars plot_multi_axes gradient_colors plot_two_scalars_vert IterVisualizer random_colors plot_two_scalars_in_one HTML save_visuals_package TreeConfig CanonicalConfigTrainPyTorch CanonicalConfig str2bool CustomParser show_arguments Logger tfboard_add_multi_scalars show_progress_py3 BaseModel IterativeBaseModel EpochBatchBaseModel get_sequential_layers get_spectral_norm SpectralNorm Reshape BaseCriterion TensorWrapper wraps set_requires_grad LossWrapper set_requires_grad_and_mode fix_grad collect_weight_keys init_weights network_param_m sampling_z DataCycle summarize_losses_and_backward MNIST mnist_paths ImageMNIST BaseClassification FlattenMNIST To32x32 Flatten decode_idx3_ubyte decode_idx1_ubyte Datasets Sprites Shapes EncoderSprites EncoderMNIST ReconstructorShapes Decoder DensityEstimator DiscriminatorMNIST ReconstructorSprites init_weights EncoderShapes ReconstructorMNIST VIBEncoder DensityEstimator init_weights NIBEncoder FCReconstructor FcBlock DisenIBEncoder FCDecoder GANLoss GaussKLDivLoss EstLoss RecLoss CrossEntropyLoss auroc aupr MIEvaluator vis_grid_disentangling OutDetectionEvaluator detection_error tpr95 AdvAttackEvaluator AccuracyEvaluator clustering gaussian_log_density_marginal resampling repeat gaussian_kl_div mean sqrt set append accuracy_score range len list lmd_k isinstance is_tuple_list dict OrderedDict eval lmd_v keys callable shuffle list map concatenate int uint8 COLOR_BGR2RGB NORM_MINMAX applyColorMap astype normalize cvtColor COLORMAP_JET join save_image items subplots arange plot set_xlabel close tight_layout title set_ylabel twinx savefig len subplot suptitle plot xlabel close ylabel tight_layout subplots_adjust title ylim savefig figure items arange plot xlabel close ylabel gradient_colors title savefig figure legend enumerate len update items join concatenate close set random_colors scatter title savefig zip append fit_transform len axhline set_title set_xlabel axvline OrderedDict ylim savefig legend plot close xlim enumerate items join isinstance axes text set_ylabel figure fill_between items add_scalars flush list vars sorted keys print int min append apply parameters isinstance parameters set_requires_grad Tensor loss_backprop isinstance backward items list filter append keys state_dict to str read reshape empty unpack_from range read unpack_from empty range isinstance Conv2d normal_ zero_ BatchNorm2d Linear xavier_uniform_ weight ones reshape size func_style unsqueeze save_image func_rec cat int sum len minimum arange min maximum sum max len minimum min maximum sum max len minimum arange min maximum sum max len add size unsqueeze randn size expand mean sum abs unsqueeze mean device to sum log unsqueeze log pi | # Disentangled Information Bottleneck PyTorch code for *"Disentangled Information Bottleneck"*. Paper Link: [[arXiv]](https://arxiv.org/pdf/2012.07372.pdf) ## Experiments ### Hardware & Software Dependency - **Hardware** We use a machine with AMD Ryzen 7 3700X CPU, 32GB RAM, and NVIDIA RTX2070S with 8GB graphics memory. - **Software** We use Windows 10 platform with Python 3.7 and CUDA 10.1. | 834 |
Parth27/ActionRecognitionVideos | ['action recognition'] | ['Gate-Shift Networks for Video Action Recognition'] | Code/Process_dataset.py Code/GSM.py Code/CosineAnnealingLR.py setup.py Code/Model.py Code/Main.py Code/Dataset.py WarmupCosineLR DataGenerator gsmModule main bninception evaluate predict bninception BNInception_gsm load_url load_state_dict BNInception_gsm dict print range len argmax sorted view print eval numpy resize append imread listdir array model SGD save device cuda seed list WarmupCosineLR view append CrossEntropyLoss range cat state_dict format DataGenerator set eval item sample is_available keys enumerate criterion backward print evaluate extend now dict parameters empty_cache train step len | # Action Recognition in Videos Implementation of a new technique called Gate-Shift Networks, with some modifications, to detect actions in videos. The paper that introduced this approach can be found her: https://arxiv.org/pdf/1912.00381.pdf The dataset used for this project is UCF-101, which can be found here: https://www.crcv.ucf.edu/data/UCF101.php Implemented the model using Pytorch and OpenCV. | 835 |
Parth27/Data2Text | ['table to text generation', 'text generation'] | ['Table-to-text Generation by Structure-aware Seq2seq Learning'] | evaluate.py preprocess.py train.py setup.py bleu_score rouge_score prep_data build_vocab join most_common range extend len max append array range len | # Data2Text Deep Learning for automated generation of text from structured data The dataset used is Wikibio dataset, to generate biographies from Wikipedia's tabular data https://github.com/DavidGrangier/wikipedia-biography-dataset. This model is inspired by https://arxiv.org/pdf/1711.09724v1.pdf | 836 |
ParthaEth/Regularized_autoencoders-RAE- | ['density estimation'] | ['From Variational to Deterministic Autoencoders'] | test/fid_computation/test_fid_computer.py models/wae_mmd/wae_celeba.py precision_recall_distributions/inception.py models/rae/loss_functions.py my_utility/save_restore_model_state.py train_raes_vaes.py models/std_vae/standard_vae_cifar.py plotting/stack_figs_together.py test/checkpoint_save_restore/test_checkpoint_save_restore.py my_utility/general_utils.py precision_recall_distributions/prd_score_test.py models/rae/rae_cifar.py dataloaders/dataloader.py my_utility/interpolations.py my_utility/estimate_density_and_sample.py my_utility/fid_from_dir_computer.py generate_nearest_neightbours.py my_utility/hard_to_soft_lbl.py models/rae/rae_svhn.py test/dataloaders/test_mnist_padded_dataloader.py models/my_layers/spectral_normalized_dense_conv.py configurations/config.py models/wae_mmd/wae_mmd.py my_utility/config_parser.py precision_recall_distributions/prd_from_image_folders.py models/wae_mmd/loss_functions.py models/rae/rae_celeba.py models/wae_mmd/wae_mnist.py test/aux_density_fitting/test_estimate_density_and_smpl.py precision_recall_distributions/inception_network.py plotting/image_figure.py my_utility/function_from_name.py models/std_vae/standard_vae_svhn.py models/rae/make_raes.py models/std_vae/standard_vae_mnist.py test/config_parser/test_config_parser.py models/std_vae/get_vae_given_enc_dec.py interpolation_fid_and_viz.py my_utility/accumulate_batches_of_data_frm_generator.py my_utility/save_batches_of_images.py precision_recall_distributions/prd_score.py my_utility/my_callbacks.py models/std_vae/std_vae.py models/my_layers/conv2D_middle_weight_zero.py models/std_vae/loss_functions.py plotting/plot_precision_recall.py models/wae_mmd/get_wae_given_enc_dec.py plotting/plott_2d_laten_space.py test/rae/test_loss_functions.py models/rae/rae_mnist.py my_utility/copy_over_models_only.py models/std_vae/standard_vae_celeba.py my_utility/print_receptive_field_sizes.py main predict_2stage main main DataLoader NoCentreWeightConv2D ZeroCenteredConv DenseSN ConvSN1D _ConvSN ConvSN2D ConvSN3D ConvSN2DTranspose EmbeddingSN embeddig_loss per_pix_recon_loss grad_pen_loss total_loss get_vae get_vae_cleba get_celeba_fully_convolutional get_vae_celeba_wae_architecture get_vae_cifar_tiny_architecture get_vae_cifar_wae_architecture get_vae_mnist get_vae_mnist_wae_architecture get_vae_mnist_tiny_architecture get_vae_svhn_wae_architecture get_vae get_sampler total_loss loss_kl_divergence get_vae_celeba get_vae_celeba_wae_architecture get_vae_cifar build_vae_cifar get_vae_cifar_wae_architecture get_vae_mnist get_vae_mnist_wae_architecture get_vae_svhn get_vae_svhn_wae_architecture get_vae get_wae per_pix_recon_loss total_loss mmd_loss get_wae_celeba_wae_architecture get_wae_celeba get_wae get_wae_mnist get_wae_mnist_wae_architecture get_n_batches_of_input get_process_id_given_mj_minor_idxs get_config_idxs VaeModelWrapper DensityEstimator get_fid InvalidFIDException calculate_activation_statistics _handle_path get_activations check_or_download_inception create_inception_graph _get_inception_layer calculate_fid_given_paths calculate_frechet_distance get_recon_loss_func softmax to_soft_label get_gaussian_kernel slerpolate slerp LatentSpaceSampler SaveReconstructedImages FIDComputer printLayer outFromIn save_set_of_images save_checkpoint_reduce_onplateau_callback save_model_state restore_model_state gen_random gen_reconstruction gen_interpolation process_directory main loadimg batch saveimg embed_images_in_inception preprocess_for_inception get_inception_features compute_prd plot prd_to_max_f_beta_pair _prd_to_f_beta _cluster_into_bins compute_prd_from_embedding PRDTest TestDensityEstimator TestModelSaveRestore TestConfigParsing TestPaddedMnist TestFidComputation TestLossfunctions get_data_loader set_random_seed query configurations DataLoader flatten seed str get_config_idxs get_n_batches_of_input squeeze shape get_vae imsave predict range normal KDTree hstack get_wae load_weights mkdir int join reshape get_fid score get_samples linspace get_test_labels save exists list predict_2stage append next sum compute_prd T square choice mean sqrt enumerate get_train_labels time fitorload get_data_dir savez norm print save_set_of_images data_std data_mean DensityEstimator get_validation_labels zeros LatentSpaceSampler SaveReconstructedImages ReduceLROnPlateau restore_model_state predict_generator TensorBoard get_zs fit_generator ModelCheckpoint makedirs mean sum square get_recon_loss_func l2 enumerate l2 callable enumerate output Dense get_vae_cleba get_vae_mnist Dense Dense Dense get_vae_svhn_wae_architecture reduce_mean reduce_sum get_vae_cleba get_recon_loss_func get_vae_mnist range concatenate len range len get_shape TensorShape get_operations outputs append get_tensor_by_name enumerate print reshape _get_inception_layer empty range run atleast_2d print iscomplexobj atleast_1d dot sqrtm trace eye real abs max imag mean cov get_activations print join urlretrieve load endswith calculate_activation_statistics close repeat expand_dims listdir array str create_inception_graph upper join calculate_fid_given_paths savez ones len split list exp reshape array range shape conv1d reshape norm arccos dot sin clip T inv dot linspace cholesky ceil floor print join imwrite astype mkdir enumerate get decode load layers load_weights_from_hdf5_group File loads deserialize save_weights savez save best savez patience cooldown_counter wait min_delta epochs_since_last_save verbose min_lr factor zeros cooldown imread cvtColor COLOR_BGR2RGB COLOR_RGB2BGR astype float32 cvtColor range len join sorted glob hstack shuffle batch join sorted list glob hstack shuffle append loadimg range len join sorted str ValueError hstack shuffle range vstack append listdir loadimg seed join str gen_random gen_reconstruction mkdir gen_interpolation expanduser saveimg rows cols outdir indir process_directory random_seed get_inception_features get_graph_def_from_disk float32 placeholder map_fn preprocess_for_inception tan pi linspace expand_dims sum max clip vstack labels_ MiniBatchKMeans compute_prd mean append array range _cluster_into_bins max _prd_to_f_beta show xlabel add_subplot ylabel tight_layout close ylim savefig figure legend tick_params xlim range len | # Regularized_autoencoders(RAE) ## This is the official implementation of the Paper titled 'From variational to deterministic Autoencoders' If you find our work useful please cite us as the following. ``` @inproceedings{ ghosh2020from, title={From Variational to Deterministic Autoencoders}, author={Partha Ghosh and Mehdi S. M. Sajjadi and Antonio Vergari and Michael Black and Bernhard Scholkopf}, booktitle={International Conference on Learning Representations}, year={2020}, | 837 |
PatrickFeng/RPNet | ['semantic segmentation'] | ['Recurrent Slice Networks for 3D Segmentation of Point Clouds'] | train.py data/utils/data_prep_util.py data/utils/eulerangles.py data/utils/indoor3d_util.py data/utils/plyfile.py layers/slice_pool_layer/slice_pool_layer.py layers/slice_pool_layer/build.py layers/slice_unpool_layer/slice_unpool_layer.py data/utils/tf_util.py eval_iou_accuracy.py net.py data/utils/pc_util.py data/gen_indoor3d_h5.py data/collect_indoor3d_data.py load_data.py utils.py layers/slice_unpool_layer/build.py load_obj gen_slice_idx gen_slice_idx_routine loadDataFile load_h5 iterate_data RSNet repackage_hidden AverageMeter accuracy avg_class_acc save_checkpoint adjust_learning_rate insert_batch load_ply_normal pad_arr_rows batch_mkdir save_h5_data_label_normal load_h5_data_label_normal load_h5_data_label_seg get_sampling_command load_h5 get_category_names save_h5 load_ply_data get_obj_filenames export_ply quat2euler euler2quat mat2euler angle_axis2euler euler2angle_axis euler2mat collect_point_label point_label_to_obj room2blocks_plus_normalized room2samples_wrapper_normalized sample_data room2samples_plus_normalized room2blocks_wrapper room2blocks room2blocks_wrapper_normalized room2samples room2blocks_plus sample_data_label write_ply pyplot_draw_point_cloud draw_point_cloud read_ply point_cloud_three_views_demo point_cloud_to_volume pyplot_draw_volume point_cloud_to_volume_batch point_cloud_three_views volume_to_point_cloud _open_stream _lookup_type PlyData _split_line PlyProperty PlyParseError make2d PlyListProperty PlyElement batch_norm_template batch_norm_for_conv1d conv2d_transpose dropout fully_connected conv3d batch_norm_for_conv2d batch_norm_for_fc avg_pool2d conv2d conv1d avg_pool3d max_pool3d max_pool2d _variable_with_weight_decay batch_norm_for_conv3d _variable_on_cpu SP Slice_Pool Slice_Unpool SU readlines close len zeros range open zeros range gen_slice_idx_routine ones int float range File int list transpose min astype shuffle copy floor float range isinstance copyfile save param_groups max topk size t eq mul_ expand_as append sum max range str format print save_h5 zeros PlyData write range join print join len join mkdir File close create_dataset File close create_dataset File File read array read array append array cos sin eps asarray atan2 sqrt flat cos sin angle_axis2mat join concatenate ones loadtxt glob print exit write close save append range open loadtxt write astype close range open choice sample_data int uniform ceil expand_dims sample_data_label range append len uint8 astype print load exit loadtxt uint8 astype room2blocks zeros max range print load exit loadtxt int arange min shuffle choice ceil zeros float range uint8 astype room2samples zeros max range print load exit loadtxt squeeze point_cloud_to_volume flatten append expand_dims range zeros float astype append vstack array range data read array write array describe int exp abs transpose min mean sqrt argsort round argwhere zeros sum max range euler2mat concatenate draw_point_cloud fromarray uint8 read_ply save point_cloud_three_views set_xlabel add_subplot scatter set_ylabel figure set_zlabel pyplot_draw_point_cloud volume_to_point_cloud append split dtype len property hasattr property property property multiply add_to_collection xavier_initializer _variable_on_cpu l2_loss truncated_normal_initializer | # Introduction This is the official inplementation of [Recurrent Slice Networks for 3D Segmentation on Point Clouds](https://arxiv.org/abs/1802.04402) (RSNet), which is going to appear in CVPR 2018. RSNet is a powerful and conceptually simple network for 3D point cloud segmentation tasks. It is fast and memory-efficient. In this repository, we release codes for training a RSNet on the S3DIS segmentation dataset. Training on other datasets can be easily achieved by following the same process. # Citation If you find our work useful in your research, please consider citing: @article{huang2018recurrent, title={Recurrent Slice Networks for 3D Segmentation on Point Clouds}, author={Huang, Qiangui and Wang, Weiyue and Neumann, Ulrich}, journal={arXiv preprint arXiv:1802.04402}, year={2018} | 838 |
Pay20Y/FOTS_TF | ['scene text detection', 'text spotting', 'scene text recognition'] | ['FOTS: Fast Oriented Text Spotting with a Unified Network'] | lanms/.ycm_extra_conf.py module/stn/__init__.py bktree.py lanms/__init__.py icdar.py eval.py lanms/__main__.py module/stn/transformer.py locality_aware_nms.py nets/resnet_v1.py multigpu_train.py module/Backbone_branch.py config.py data_util.py module/Recognition_branch.py nets/resnet_utils.py module/RoI_rotate.py GeneratorEnqueuer standard_nms weighted_merge nms_locality intersection GetCompilationInfoForFile IsHeaderFile MakeRelativePathsInFlagsAbsolute FlagsForFile DirectoryOfThisScript merge_quadrangle_n9 unpool mean_image_subtraction Backbone affine_grid_generator get_pixel_value spatial_transformer_network bilinear_sampler Block conv2d_same subsample resnet_arg_scope stack_blocks_dense resnet_v1_152 resnet_v1_101 bottleneck resnet_v1_200 resnet_v1_50 resnet_v1 reshape area Polygon append array append weighted_merge append join startswith IsHeaderFile compiler_flags_ exists compiler_flags_ GetCompilationInfoForFile compiler_working_dir_ MakeRelativePathsInFlagsAbsolute DirectoryOfThisScript nms_impl array copy range split reshape affine_grid_generator bilinear_sampler reshape shape stack tile range ones_like reshape float32 matmul stack cast linspace tile meshgrid expand_dims cast clip_by_value floor add_n zeros expand_dims get_pixel_value pad | # Fast Oriented Text Spotting with a Unified Networkt ### Update [08/17/2019] A new version is updated, please checkout the branch 'dev' ([link](https://github.com/Pay20Y/FOTS_TF/tree/dev)). ### Introduction This is an implementation of [FOTS: Fast Oriented Text Spotting with a Unified Network](https://arxiv.org/pdf/1801.01671.pdf) ### Install + Python2 + tensorflow + OpenCV ### Model | 839 |
Pay20Y/GCAN | ['scene text recognition'] | ['Gaussian Constrained Attention Network for Scene Text Recognition'] | train.py sar_model.py module/Decoder.py module/Encoder.py module/attention_loss.py utils/transcription_utils.py test.py data_provider/generator_enqueuer.py data_provider/lmdb_char_data_generator.py data_provider/lmdb_data_generator.py data_provider/data_utils.py module/Backbone.py config.py data_provider/evaluator_data.py module/nets/resnet_utils.py utils/visualization.py module/nets/resnet_v1.py get_args SARModel test get_images data_preprocess main_test_lmdb resize_pad_img get_data main_test get_data_lexicon main_test_with_lexicon get_data main_train get_batch_data Evaluator GeneratorEnqueuer generator get_batch attention_regress_loss KLDivLoss gaussian_kl L2_loss two_d_gaussian_kl_div_loss params_regress_loss smooth_L1 KLDivLossContirb CrossEntropyLoss mean_image_subtraction Backbone Backbone_v2 Decoder Encoder Block conv2d_same subsample resnet_arg_scope stack_blocks_dense resnet_v1_152 resnet_v1_101 bottleneck resnet_v1_200 resnet_v1_50 resnet_v1 calc_metrics _lexicon_search idx2label calc_metrics_lexicon _normalize_text calc_metrics_length line_mask_visualize line_sep_visualize mask_visualize heatmap_visualize_bak line_visualize contour_visualize heatmap_visualize parse_args decode minimize SARModel rand randint placeholder inference loss join format endswith print append walk len Syn90KLoader get_images test_data_dir parse_gt test_data_gt SynTextLoader int shape resize zeros rot90 height resize_pad_img max_len array width append full items format isinstance print SARModel placeholder get_vocabulary Saver ExponentialMovingAverage sar_model variables_to_restore calc_metrics_length get_variable format print SARModel calc_metrics_lexicon placeholder get_vocabulary Saver ExponentialMovingAverage sar_model variables_to_restore get_variable SARModel placeholder get_vocabulary Saver ExponentialMovingAverage sar_model variables_to_restore get_variable append get_batch isinstance len append next extend concatenate workers trainable_variables keep_ratio sar_model_val voc_type set_random_seed get_data localtime Saver train_data_dir get_variable loss att_loss_dtype checkpoints str open global_variables exit get_collection placeholder get_vocabulary merge_all strftime apply width decay_bound sar_model piecewise_constant format height SARModel Evaluator group FileWriter close ConfigProto join time print lr_stage max_len reset UPDATE_OPS ExponentialMovingAverage train_batch_size scalar makedirs decode arange random randint resize open begin seek get_vocabulary shape append rot90 get format shuffle copy lower rotate_img full int BytesIO print convert write zeros array len generator get is_running print start sleep GeneratorEnqueuer subtract multiply float32 add cast less abs as_list print reshape reduce_sum smooth_L1 zeros unstack pad join filter print ndarray isinstance get_vocabulary sum len tolist min array max range asarray argmin eval append _normalize_text sum len addWeighted uint8 join imwrite format reshape astype shape resize zeros enumerate arange reshape shape resize max enumerate addWeighted uint8 join arange imwrite format polylines reshape resize astype copy shape int32 interp zeros enumerate join format arange imwrite zip polylines reshape astype copy shape int32 interp enumerate join format arange imwrite polylines reshape astype copy shape int32 save interp enumerate addWeighted uint8 join imwrite format reshape astype copy shape rectangle resize zeros enumerate addWeighted uint8 join imwrite format reshape applyColorMap astype copy shape resize COLORMAP_JET max enumerate | # Gaussian Constrained Attention Network for Scene Text Recognition ## Introduction Implementation of the paper "Gaussian Constrained Attention Network for Scene Text Recognition" (Under Review) ## How to use ### Install ``` pip3 install -r requirements.txt ``` ### Train * <b> Data prepare</b> | 840 |
Pay20Y/SEED | ['optical character recognition', 'scene text recognition'] | ['SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition'] | lib/utils/logging.py lib/evaluation_metrics/metrics.py lib/trainers.py lib/utils/serialization.py lib/models/embedding_head.py lib/__init__.py lib/utils/labelmaps.py lib/loss/__init__.py lib/models/tps_spatial_transformer.py lib/datasets/dataset.py lib/utils/meters.py lib/evaluation_metrics/__init__.py lib/utils/__init__.py lib/models/model_builder.py lib/tools/create_svtp_lmdb.py lib/models/resnet_aster.py lib/loss/embeddingRegressionLoss.py lib/models/attention_recognition_head.py lib/evaluators.py lib/utils/visualization_utils.py lib/models/__init__.py main.py lib/datasets/concatdataset.py lib/models/stn_head.py lib/loss/sequenceCrossEntropyLoss.py config.py lib/tools/create_all_synth_lmdb.py lib/utils/osutils.py get_args get_dataloader get_data_lmdb get_data_txt get_dataset main BaseEvaluator Evaluator Trainer BaseTrainer ConcatDataset AlignCollate debug CustomDataset LmdbDataset ResizeNormalize RandomSequentialSampler EditDistance_with_lexicon EditDistance _lexicon_search get_str_list RecPostProcess Accuracy_with_lexicon Accuracy _normalize_text names factory AttentionRecognitionHead AttentionUnit DecoderUnit PositionwiseFeedForward MultiHeadAttention Embedding self_block Embedding_self_att ScaledDotProductAttention conv1x1 AsterBlock ResNet_ASTER conv3x3 get_sinusoid_encoding conv3x3_block STNHead compute_partial_repr TPSSpatialTransformer grid_sample build_output_control_points names create createDataset _is_difficult writeCache checkImageIsValid createDataset _is_difficult writeCache checkImageIsValid char2id labels2strs get_vocabulary id2char TFLogger Logger AverageMeter make_symlink_if_not_exists mkdir_if_missing load_checkpoint copy_state_dict read_json save_checkpoint write_json stn_vis _save_plot_pool recognition_vis to_numpy to_torch parse_args ConcatDataset print CustomDataset DataLoader zip append len isinstance ConcatDataset print LmdbDataset DataLoader append len isinstance ConcatDataset print LmdbDataset append len list permutation ConcatDataset print SubsetRandomSampler DataLoader len workers batch_size keep_ratio voc_type Trainer MultiStepLR DataParallel Logger num_test device cuda max seed get_data_lmdb test_data_dir logs_dir vis_dir epochs ModelBuilder TFLogger load_state_dict width to range manual_seed_all height inf format num_train debug Evaluator close resume manual_seed vars set_default_tensor_type join time evaluate Adadelta print load_checkpoint max_len parameters synthetic_train_data_dir evaluation_metric filter train step makedirs print DataLoader CustomDataset enumerate join filter size append to_numpy keys range len asarray argmin eval append _normalize_text get_str_list sum len get_str_list sum append len get_str_list sum get_str_list sum append exp get_str_list size min map append to_numpy sum log enumerate len arange cos pow unsqueeze sin float BatchNorm2d Sequential ReLU Conv2d fill_ masked_fill_ size log view concatenate ones stack linspace Tensor imdecode fromstring IMREAD_GRAYSCALE join print len encode writeCache range open list ascii_lowercase append ascii_letters digits join unsqueeze append to_numpy range makedirs format system makedirs dirname mkdir_if_missing join print run_on_remote copy make_dirs dirname save mkdir_if_missing load format print run_on_remote shift isfile data items isinstance print size set copy_ add keys state_dict fromarray join uint8 format get_str_list permute save zip to_numpy enumerate fromarray uint8 save set_yticklabels get_str_list axis unsqueeze open show seek imshow scatter savefig permute append range format set_xticklabels size astype close zip enumerate int uint8 BytesIO join figure to_numpy is_tensor | # SE_ASTER ## Introduction This is the implementation of the paper "SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition" This code is based on the [aster.pytorch](https://github.com/ayumiymk/aster.pytorch), we sincerely thank ayumiymk for his awesome repo and help. ## How to use ### Env ``` PyTorch == 1.1.0 torchvision == 0.3.0 fasttext == 0.9.1 | 841 |
Pehlevan-Group/NTK_Learning_Curves | ['gaussian processes'] | ['Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks'] | plot_two_layer.py compute_NTK_spectrum.py approx_learning_curves.py plot_curve.py kernel_regression_lc.py gegenbauer.py two_layer.py noisy_kernel_lc.py mnist_multiple_classes.py NTK_gd_comparison.py mode_err_MOC uc_implicit simulate_uc_noise solve_total gamma total_err dynamical simulate_uc fprime get_noise_errs dynamics_errors implicit_total_err_eq upper_cont_approx NTK NNGP f get_effective_spectrum_hermite get_effective_spectrum one_layer_NNGP get_gegenbauer surface_area normalizing_factor check_orthogonality hermite_to_gegenbauer_activation_coeffs degeneracy get_hermite get_gegenbauer_fast2 surface_area_ratio monte_carlo_coeffs get_gegenbauer_fast calculate_activation_coeffs hermite_to_gegenbauer_coeffs get_hermite_fast inner_product compute_kernel generalization sample_random_points f_prime_imp kernel_gen_expt2 train_network solve_implicit_negative_moment kernel_gen_expt neural_net_gen_expt implicit_fn_true f_prime_true implicit_equation gamma solve_implicit_z theory_learning_curves compute_kernel generalization sample_random_points target_fn train_network get_datasets sample_random_points NTK_test_err create_network generalization_expt_kteach sample_random_points_jit SGD compute_kernel sample_random_points get_gegenbauer_gram generalization_expt get_mode_errs feedfoward root root_scalar ones sum len converged root_scalar root zeros range len ones len gamma zeros total_err range len zeros gamma range len mode_err_MOC solve_total total_err shape logspace zeros gamma range len mode_err_MOC solve_total total_err shape logspace get_noise_errs zeros gamma range len sqrt cos pi f cos pi sqrt range roots_gegenbauer NTK T arccos surface_area ones NNGP get_gegenbauer_fast2 outer heaviside zeros array range len roots_hermite NTK T arccos ones NNGP range outer sqrt shape heaviside hermite_to_gegenbauer_coeffs zeros abs array get_hermite_fast amax len roots_gegenbauer get_gegenbauer arccos pi array range zeros range ones shape zeros range len zeros eval_gegenbauer range get_gegenbauer normalizing_factor dot linspace zeros range normalizing_factor dot zeros range len zeros eval_hermite range ones zeros range len surface_area surface_area_ratio sqrt array len roots_hermite T ones maximum calculate_activation_coeffs sqrt heaviside hermite_to_gegenbauer_coeffs zeros get_hermite_fast len roots_gegenbauer tanh surface_area ones get_gegenbauer_fast2 maximum heaviside zeros array len get_gegenbauer normal tanh T show maximum sqrt scatter zeros array len norm multivariate_normal eye zeros range reshape T get_gegenbauer_fast2 array T norm random_sample ones reshape print inv get_gegenbauer_fast2 sign sample_random_points compute_kernel eye zeros sum array range sem grad_loss write opt_update nn_loss get_params range flush init_fn norm train_network apply_fn print write opt_init choice mean enumerate split zeros get_params std range PRNGKey len print choice gp_inference append zeros abs range amax len T norm print choice gp_inference mean zeros sum std range enumerate len print roots root_scalar root zeros sum range len array array root zeros enumerate len T array zeros sum solve_implicit_z diag enumerate normal T matmul ones outer normal target_fn sample_random_points zeros array range range init_fn concatenate reshape min opt_init randint loss gp_inference T norm print ones maximum copy randint outer shape sqrt heaviside zeros range prange standard_normal norm zeros T maximum shape reshape T get_gegenbauer_fast2 T reshape get_gegenbauer_gram zeros range time T norm std print sample_random_points_jit standard_normal min maximum SGD mean sqrt feedfoward zeros sum array range get_mode_errs time T norm std print sample_random_points_jit standard_normal SGD compute_kernel mean sqrt feedfoward zeros sum array range get_mode_errs | # Learning Curves for NTK and Wide Neural Networks See our preprint on [Arxiv](https://arxiv.org/abs/2002.02561) ## Experiments ### Kernel Regression experiment with NTK To generate experimental and theoretical learning curves for kernel regression with ReLU NTK run `python kernel_regression_lc.py --input_dim [d] --lamb [lamb] --NTK_depth [depth]` The optional arguments are: `input_dim` is the dimension of the data, `lamb` is the explicit regularizer, `NTK_depth` is the number of layers for the fully connected ReLU NTK. These parameters default to the values used in the paper. | 842 |
PeiyanFlying/https-github.com-PeiyanFlying-pytorch-kaldi | ['speech recognition', 'noisy speech recognition', 'distant speech recognition'] | ['The PyTorch-Kaldi Speech Recognition Toolkit'] | kaldi_decoding_scripts/utils/nnet/gen_hamm_mat.py kaldi_decoding_scripts/utils/reverse_arpa.py kaldi_decoding_scripts/utils/nnet/gen_splice.py kaldi_decoding_scripts/utils/nnet/gen_dct_mat.py kaldi_decoding_scripts/utils/filt.py kaldi_decoding_scripts/utils/nnet/make_nnet_proto.py kaldi_decoding_scripts/utils/nnet/make_cnn_proto.py tune_hyperparameters.py kaldi_decoding_scripts/utils/nnet/make_lstm_proto.py run_exp.py kaldi_decoding_scripts/utils/nnet/make_blstm_proto.py plot_acc_and_loss.py save_raw_fea.py data_io.py utils.py neural_networks.py core.py kaldi_decoding_scripts/utils/nnet/make_cnn2d_proto.py extract_data_from_shared_list convert_numpy_to_torch run_nn run_nn_refac01 read_next_chunk_into_shared_list_with_subprocess load_counts UnknownMatrixHeader _read_vec_flt_binary open_or_fd _read_mat_ascii read_vec_int_ark context_window_old read_vec_flt_scp UnknownVectorHeader read_cntime_ark load_chunk read_vec_flt_ark write_mat read_cntime UnsupportedDataType write_vec_int BadInputFormat read_post_ark SubprocessFailed write_vec_flt read_vec_int read_mat load_dataset BadSampleSize read_vec_flt read_post_rxspec read_post_scp read_ali_ark read_lab_fea_refac01 _read_vec_flt_riff _read_mat_binary read_key read_cnet_ark _read_compressed_mat read_segments_as_bool_vec read_lab_fea context_window popen read_mat_scp read_post read_mat_ark act_fun MLP BCMGOOLSTM GRU LayerNorm GOOLSTM BCMLSTM LSTM flip BCMGRU _max_nr_of_parallel_forwarding_processes _is_first_validation _run_forwarding_in_subprocesses nth_replace_string run_command compute_cw_max create_curves run_shell read_args_command_line check_cfg parse_model_field create_lists get_chunks_after_which_to_validate optimizer_init create_block_diagram run_shell_display dump_epoch_results is_sequential export_loss_acc_to_txt split_chunks write_cfg_chunk expand_section_proto get_val_cfg_file_path expand_str_ep create_block_connection get_val_lst_file_path compute_avg_performance check_consistency_with_proto parse_fea_field change_lr_cfg check_field expand_section get_all_archs get_val_info_file_path forward_model terminal_node_detection model_init create_configs parse_lab_field dict_fea_lab_arch is_sequential_dict do_validation_after_chunk _get_val_file_name_base shift check_cfg_fields cfg_item2sec list_fea_lab_arch forward_model_refac01 compute_n_chunks progress fix_filt_step Glorot start Thread join float cuda view _optimization_step load_counts strtobool convert_numpy_to_torch write_mat _prepare_input log _write_info_file list _get_dim_from_data_set len map _read_chunk_specific_config sum range detach extract_data_from_shared_list close _get_batch_size_from_config _get_batch_config read_next_chunk_into_shared_list_with_subprocess forward_model join time _save_model is_sequential_dict _initialize_random_seed shift _update_progress_bar _open_forward_output_files_and_get_file_handles _load_model_and_optimizer numpy split load_counts strtobool open_or_fd zero_grad DataParallel numpy save write_mat round cuda max log seed str list optimizer_init len exit map load_state_dict sum range detach state_dict Thread replace ConfigParser close start manual_seed item float keys model_init forward_model load int read join time backward is_sequential_dict contiguous shift write randint step progress split _read_features_and_labels_with_kaldi _match_feature_and_label_sequence_lengths _chunk_features_and_labels _concatenate_features_and_labels _input_is_wav_file flatten empty range concatenate empty range roll min mean context_window load_dataset std column_stack _reorder_data_set _append_to_shared_list _read_features_and_labels _read_from_config _read_chunk_specific_config update int read list dict_fea_lab_arch ConfigParser is_sequential_dict write exit shuffle load_chunk compute_cw_max keys append column_stack rsplit int seek search popen split open start Popen open decode strip read_vec_int open_or_fd read_key decode remove read open_or_fd close frombuffer array split pack char write open_or_fd encode range len read_vec_flt open_or_fd split read_vec_flt open_or_fd read_key decode remove open_or_fd close array split read frombuffer unpack decode read frombuffer pack char write open_or_fd encode tobytes read_mat open_or_fd split read_mat open_or_fd read_key decode _read_mat_ascii _read_mat_binary open_or_fd decode read reshape startswith frombuffer decode vstack append array split dtype read reshape zeros frombuffer array pack char write open_or_fd encode tobytes print exit startswith open_or_fd read_post split open_or_fd read_post read_key decode read tolist open_or_fd close append frombuffer range read_cntime open_or_fd read_key decode read tolist open_or_fd close frombuffer loadtxt repeat astype size view contiguous strtobool get_chunks_after_which_to_validate _get_nr_of_valid_per_epoch_from_config decode readline print append Popen decode write Popen flush communicate wait Popen int str findall group write exit nth_replace_string split append range compile len read ConfigParser mean append float sum int list write exit map float split append sections read list add_section ConfigParser remove_section set sections append keys range values len ConfigParser read list set list write exit any sections keys read ConfigParser exit check_cfg_fields expand_section open rstrip strtobool values open run_shell str list sorted parse_model_field len exit create_block_diagram append sum range replace check_consistency_with_proto parse_fea_field sections join items parse_lab_field int read write split findall makedirs write sections exit append range len list _partition_chunks append get_chunks_after_which_to_validate _get_nr_of_valid_per_epoch_from_config format _get_val_lst_file_name _get_val_info_file_name _get_val_cfg_file_name strtobool max open str check_cfg list exit log10 ceil append range write_cfg_chunk expand_str_ep get_val_cfg_file_path format get_val_lst_file_path replace close get_all_archs float get_val_info_file_path keys int items do_validation_after_chunk write split compute_n_chunks len __add__ max open seed str sorted list map log10 reverse writelines ceil append split_chunks range format get_val_lst_file_path parse_fea_field close shuffle _get_validation_data_for_chunks _shuffle_forward_data int do_validation_after_chunk cfg_item2sec split len add_section str list sorted remove_section append range replace check_consistency_with_proto ConfigParser glob remove_option sections keys int read join items cfg_item2sec findall len sorted write exit sub append split write exit sub append split glob int sorted format read list replace ConfigParser len write exit findall float range append open list str list index append range len run_shell str read list remove replace create_block_connection ConfigParser findall append list replace strtobool len map cfg_item2sec findall range append split list replace strtobool len map cfg_item2sec findall range append split strtobool list keys strtobool int list max append keys NLLLoss list out_dim strtobool nn_class set eval import_module getattr train cuda list strtobool map SGD Adam RMSprop parameters float keys split list _get_network_output _get_labels_from_input mean _add_input_features_to_outs_dict shape _compute_layer_values float cat len list view float mean shape long bool keys cat len str list int print write close keys log10 ceil max open int write float round flush str asarray ndarray readlines makedirs savetxt split append float range len arange axis str use exit ylabel title savefig legend append export_loss_acc_to_txt range plot readlines clear print loadtxt xlabel write amax len find str read list ConfigParser set keys int write extend exit append float split range with_glorot | # The PyTorch-Kaldi Speech Recognition Toolkit <img src="pytorch-kaldi_logo.png" width="220" img align="left"> PyTorch-Kaldi is an open-source repository for developing state-of-the-art DNN/HMM speech recognition systems. The DNN part is managed by PyTorch, while feature extraction, label computation, and decoding are performed with the Kaldi toolkit. This repository contains the last version of the PyTorch-Kaldi toolkit (PyTorch-Kaldi-v1.0). To take a look into the previous version (PyTorch-Kaldi-v0.1), [click here](https://bitbucket.org/mravanelli/pytorch-kaldi-v0.0/src/master/). If you use this code or part of it, please cite the following paper: *M. Ravanelli, T. Parcollet, Y. Bengio, "The PyTorch-Kaldi Speech Recognition Toolkit", [arXiv](https://arxiv.org/abs/1811.07453)* ``` @inproceedings{pytorch-kaldi, title = {The PyTorch-Kaldi Speech Recognition Toolkit}, author = {M. Ravanelli and T. Parcollet and Y. Bengio}, | 843 |
Pele324/ChronicWoundSeg | ['semantic segmentation'] | ['Fully Automatic Wound Segmentation with Deep Convolutional Neural Networks'] | utils/learning/patch/extraction.py evaluate_with_post_processing.py models/SegNet.py utils/io/data.py utils/config/read.py utils/BilinearUpSampling.py utils/padding.py utils/config/memory.py utils/io/write.py train.py models/FCN.py utils/postprocessing/hole_filling.py utils/learning/losses.py utils/learning/callbacks.py models/unets.py utils/learning/patch/reconstruction.py predict.py utils/preprocessing/threshold.py utils/io/read.py utils/postprocessing/remove_small_noise.py utils/postprocessing/threshold.py utils/augment.py utils/learning/metrics.py models/deeplab.py utils/preprocessing/normalisation.py evaluate Deeplabv3 preprocess_input BilinearUpsampling relu6 _xception_block SepConv_BN _make_divisible _inverted_res_block _conv2d_same FCN_Vgg16_16s SegNet Unet2D resize_images_bilinear BilinearUpSampling2D paddingjpg paddingpng get_model_memory_usage readConfig readConfig_OLD load_jpg_images DataGen get_jpg_filename_list save_results load_png_images save_rgb_results load_test_images save_history load_data normalize get_png_filename_list niiToNp getAffine readRawDataset reshapeDataset getDataset getAffine_subdir readTrainValid readDatasetPart readDataset npToNii npToNiiAffine learningRateSchedule tversky_loss jaccard_distance_loss dice_loss dice_coef_ dice_coef_loss dice_coef_loss_ dice_coef jaccard_coef_logloss specificity precision f1 dice_coef recall generateFullPatchsCentered generateFullPatchs generateRandomPatchs noNeg extractPatchOut generatorRandomPatchsDolz generatorRandomPatchs3216 extractPatch generateFullPatchsPlus generatorRandomPatchs randomPatchsAugmented generatorRandomPatchsAugmented generatorRandomPatchsLabelCentered generateRandomPatch generatorRandomPatchsLinear dolzReconstruction fullPatchsPlusToImage fullPatchsToImage fill_holes remove_small_areas intensityProjection intensityMaxClipping intensityNormalisationFeatureScaling standardization_intensity_normalization linear_intensity_normalization getThreshold thresholding str threshold imwrite fill_holes print float tqdm shape zeros imread range remove_small_areas format add SepConv_BN _conv2d_same range int max int format _make_divisible get_file format _xception_block get_source_inputs SepConv_BN _make_divisible Model load_weights _inverted_res_block Input _conv2d_same range Model Input constant resize_bilinear astype image_data_format set_shape permute_dimensions int_shape print sort new paste save append walk range open print sort new paste save append walk range open sum layers round output_shape get int RawConfigParser read_file open get int RawConfigParser read_file open amin absolute amax append sort range walk append sort range walk get_jpg_filename_list astype append imread array astype append imread get_png_filename_list array load_png_images normalize load_png_images normalize imwrite imwrite get show format plot xlabel extend ylabel title clf savefig save legend astype join niiToNp print sort empty listdir load sort join listdir load sort join listdir print sort exit astype get_data listdir print readRawDataset load join sort exit get_data_dtype astype get_data listdir load join sort exit get_data_dtype astype get_data listdir range Nifti1Image eye save Nifti1Image save flatten sum abs sum abs sum flatten sum flatten sum flatten sum cast greater sum round clip sum round clip sum round clip recall precision zeros range randint extractPatch empty range generateRandomPatch int empty extractPatch range int empty extractPatch range extractPatchOut int range zeros zeros range extractPatch randint zeros range extractPatch randint int randint extractPatch zeros range int to_categorical randint flatten extractPatch zeros range int exit randint rotate extractPatch zeros int exit randint extractPatch zeros rot90 range range ones zeros range shape print reshape copy argmax range ones where append label range len ones where append label range len max mean std min max max range len range copy | # 2D Wound Segmentation This project aims at wound area segmentation from natural images in clinical settings. The architectures tested so far includes: U-Net, MobileNetV2, Mask-RCNN, SegNet, VGG16.   ## Publication Wang, C., Anisuzzaman, D.M., Williamson, V. et al. Fully automatic wound segmentation with deep convolutional neural networks. Sci Rep 10, 21897 (2020). https://doi.org/10.1038/s41598-020-78799-w ## Data The training dataset is built by our lab and collaboration clinic, Advancing the Zenith of Healthcare (AZH) Wound and Vascular Center. With their permission, we are sharing this dataset (./data/wound_dataset/) publicly. This dataset was fully annotated by wound professionals and preprocessed with cropping and zero-padding. Update 3/12/2021: | 844 |
Peng154/Delay-Embedding-based-Forecast-Machine | ['time series'] | ['Multi-step-ahead Prediction from Short-term Data by Delay-embedding-based Forecast Machine'] | forecast/lorenz_time_invariant/eval.py forecast/lorenz_time_invariant/lorenz_time_invariant_config.py configs/config.py utils/utils.py forecast/st_delay_model.py utils/layers.py data/lorenz.py forecast/lorenz_time_invariant/train.py data/data_processing.py Config add_noise get_data_idxs_for_gene get_data_idxs_as_predict_idx_no_overlap window load_traffic_data load_gene_data get_data_idxs_for_wind_speed get_data_idxs_for_normal load_solar_irradiance load_typhoon_data get_data_idxs_for_traffic load_hk_data load_lorenz_data load_wind_speed_data get_data_idxs_for_lorenz get_data_idxs_for_solar my_lorenz DataGeneratorForLengthCmp STDelayModel time_distributed_graph DataGenerator get_model_path draw_pic LorenzTimeInvariantConfig point_wise_feed_forward_network scaled_dot_product_attention Matrix2Y feed_forword_graph PredictYLoss MultiHeadAttention get_angles get_known_mask batch_slice TimeConsistentLoss PositionEncodingLayer Encoder positional_encoding KnownYLoss encoder_graph BatchNorm get_same_idxs EncoderLayer get_y_from_matrix seed normal DATA_NOISE_STRENGTH ADD_NOISE copy seed int print shuffle append array range seed int arange print shuffle TRAIN_LEN EMBEDDING_LEN seed int dtype arange print shuffle seed int dtype arange print shuffle TRAIN_LEN seed arange EMBEDDING_LEN shuffle TRAIN_LEN seed EMBEDDING_LEN ones maximum shuffle enumerate join T print shape read_csv values print int my_lorenz format join list items T print File shape append array join print reshape mean shape loadmat join format print read_hdf to_csv mean shape stack nan append range fillna values join print loadtxt min mean shape append max range print mean shape stack append range join window print shape loadmat int rand zeros range range len show format arange plot concatenate xlabel len ylabel title ylim stack scatter legend savefig clf xticks yticks join format name print EPOCHS match listdir compile list graph_fn zip append range len concatenate min append zeros range ones range float32 matmul sqrt cast softmax feed_forword_graph power float32 get_angles cos sin TRAIN_LEN arange EMBEDDING_LEN mean append sum array range len | # Delay-Embedding-based-Forecast-Machine - The source code of paper "Multi-step-ahead Prediction from Short-term Data by Delay-Embedding-based Forecast Machine". - This project is used to make time series forecasting on the target variable in a high-dimensional dynamical system only with short-term observed data. ## Data avalability For the reason that all the data files are too large, thus all datasets can be download from [Google Drive](https://drive.google.com/open?id=1MLwkQ4APxGHVxnTFOM_TShdHQRJg8dzX). After downloading the zip file, you should extract all dataset folders in the zip file to the target folder `logs/data/` ## Environment requirements - python = 3.6 - tenforflow = 2.1 - cuda-version = 10.1 - cudnn-version = 7.6.5 | 845 |
PengWan-Yang/commonLocalization | ['action localization'] | ['Localizing the Common Action Among a Few Videos'] | lib/tf_model_zoo/models/street/python/vgslspecs.py lib/tf_model_zoo/models/slim/nets/inception_v2_test.py lib/tf_model_zoo/ECO/pytorch_load.py lib/setup.py lib/datasets/imagenet.py lib/tf_model_zoo/models/street/python/vgsl_eval.py lib/tf_model_zoo/models/inception/inception/dataset.py lib/tf_model_zoo/bninception/parse_caffe.py lib/tf_model_zoo/models/video_prediction/prediction_train.py lib/tf_model_zoo/models/im2txt/im2txt/ops/image_processing.py lib/tf_model_zoo/models/street/python/vgsl_model.py lib/datasets/factory.py lib/tf_model_zoo/models/slim/preprocessing/inception_preprocessing.py lib/tf_model_zoo/models/inception/inception/slim/variables.py lib/tf_model_zoo/models/im2txt/im2txt/run_inference.py lib/tf_model_zoo/models/differential_privacy/multiple_teachers/input.py lib/tf_model_zoo/models/differential_privacy/dp_sgd/dp_optimizer/dp_optimizer.py lib/model/roi_temporal_pooling/functions/roi_temporal_pool.py lib/tf_model_zoo/C3DRes18/layer_factory.py lib/tf_model_zoo/models/inception/inception/imagenet_train.py lib/model/utils/config.py lib/tf_model_zoo/models/differential_privacy/multiple_teachers/train_student.py lib/tf_model_zoo/inceptionresnetv2/tensorflow_dump.py lib/tf_model_zoo/models/autoencoder/AutoencoderRunner.py lib/tf_model_zoo/models/inception/inception/flowers_eval.py lib/tf_model_zoo/models/inception/inception/imagenet_data.py lib/tf_model_zoo/models/slim/nets/vgg.py lib/tf_model_zoo/models/slim/preprocessing/preprocessing_factory.py lib/tf_model_zoo/models/syntaxnet/syntaxnet/parser_trainer.py lib/tf_model_zoo/models/im2txt/im2txt/data/build_mscoco_data.py lib/tf_model_zoo/models/slim/nets/inception_v1.py lib/tf_model_zoo/models/slim/nets/overfeat.py lib/tf_model_zoo/models/street/python/vgslspecs_test.py lib/tf_model_zoo/models/swivel/nearest.py lib/tf_model_zoo/models/syntaxnet/syntaxnet/reader_ops_test.py lib/tf_model_zoo/models/compression/encoder.py lib/tf_model_zoo/models/inception/inception/slim/collections_test.py lib/tf_model_zoo/models/neural_gpu/neural_gpu.py lib/tf_model_zoo/models/slim/nets/overfeat_test.py lib/tf_model_zoo/models/im2txt/im2txt/inference_utils/vocabulary.py lib/tf_model_zoo/ECO/layer_factory.py lib/tf_model_zoo/models/syntaxnet/syntaxnet/structured_graph_builder.py lib/model/roi_temporal_pooling/build.py lib/tf_model_zoo/models/compression/decoder.py lib/tf_model_zoo/models/differential_privacy/dp_sgd/per_example_gradients/per_example_gradients.py lib/tf_model_zoo/inceptionv4/tensorflow_dump.py lib/tf_model_zoo/models/inception/inception/slim/scopes.py evaluation/get_detection_performance.py lib/model/utils/transforms.py lib/tf_model_zoo/bninception/layer_factory.py lib/roi_data_layer/roibatchLoader.py lib/tf_model_zoo/models/slim/nets/resnet_v2_test.py lib/tf_model_zoo/models/differential_privacy/privacy_accountant/python/gaussian_moments.py preprocess/activitynet/test_pickle.py lib/tf_model_zoo/models/slim/nets/nets_factory_test.py lib/tf_model_zoo/models/slim/nets/inception_v4_test.py lib/tf_model_zoo/models/resnet/resnet_model.py lib/datasets/pascal_voc_rbg.py lib/tf_model_zoo/models/transformer/tf_utils.py lib/tf_model_zoo/models/inception/inception/imagenet_eval.py lib/tf_model_zoo/models/slim/datasets/download_and_convert_cifar10.py lib/tf_model_zoo/models/im2txt/im2txt/evaluate.py lib/tf_model_zoo/models/lm_1b/data_utils.py lib/tf_model_zoo/models/inception/inception/slim/ops_test.py lib/tf_model_zoo/models/slim/datasets/cifar10.py lib/datasets/vg.py lib/tf_model_zoo/models/slim/nets/alexnet.py lib/tf_model_zoo/models/street/python/vgsl_input.py lib/tf_model_zoo/models/slim/nets/resnet_v1_test.py lib/tf_model_zoo/models/swivel/text2bin.py lib/tf_model_zoo/models/textsum/seq2seq_attention_decode.py lib/tf_model_zoo/models/inception/inception/slim/variables_test.py lib/datasets/__init__.py lib/model/rpn/proposal_layer.py lib/tf_model_zoo/models/street/python/nn_ops.py lib/model/nms/nms_gpu.py lib/model/utils/non_local_dot_product.py lib/tf_model_zoo/inceptionv4/pytorch_load.py lib/tf_model_zoo/models/slim/nets/inception_resnet_v2_test.py lib/tf_model_zoo/models/syntaxnet/syntaxnet/beam_reader_ops_test.py lib/tf_model_zoo/C3DRes18/pytorch_load.py lib/tf_model_zoo/models/inception/inception/data/build_imagenet_data.py lib/tf_model_zoo/models/autoencoder/VariationalAutoencoderRunner.py lib/tf_model_zoo/models/inception/inception/slim/losses_test.py lib/tf_model_zoo/models/neural_programmer/model.py lib/tf_model_zoo/models/resnet/resnet_main.py lib/tf_model_zoo/models/slim/nets/inception_v2.py lib/tf_model_zoo/models/slim/datasets/imagenet.py lib/tf_model_zoo/models/slim/datasets/dataset_factory.py lib/tf_model_zoo/models/neural_gpu/data_utils.py lib/tf_model_zoo/models/neural_gpu/neural_gpu_trainer.py lib/tf_model_zoo/models/textsum/batch_reader.py lib/tf_model_zoo/models/slim/download_and_convert_data.py lib/model/rpn/rpn.py lib/tf_model_zoo/models/autoencoder/Utils.py lib/datasets/ds_utils.py main.py lib/tf_model_zoo/models/neural_programmer/neural_programmer.py lib/tf_model_zoo/models/slim/eval_image_classifier.py lib/tf_model_zoo/models/street/python/decoder.py lib/tf_model_zoo/models/syntaxnet/syntaxnet/graph_builder_test.py lib/tf_model_zoo/models/differential_privacy/privacy_accountant/tf/accountant.py lib/tf_model_zoo/models/inception/inception/slim/inception_model.py lib/tf_model_zoo/models/textsum/seq2seq_attention_model.py lib/tf_model_zoo/models/swivel/glove_to_shards.py lib/tf_model_zoo/models/transformer/example.py lib/tf_model_zoo/models/textsum/data_convert_example.py lib/tf_model_zoo/models/im2txt/im2txt/inference_wrapper.py lib/model/rpn/resnet.py lib/tf_model_zoo/models/differential_privacy/multiple_teachers/deep_cnn.py lib/tf_model_zoo/models/slim/nets/resnet_v1.py lib/tf_model_zoo/models/video_prediction/lstm_ops.py lib/tf_model_zoo/models/street/python/decoder_test.py lib/tf_model_zoo/models/syntaxnet/syntaxnet/conll2tree.py lib/tf_model_zoo/models/im2txt/im2txt/show_and_tell_model_test.py lib/tf_model_zoo/models/im2txt/im2txt/ops/inputs.py lib/datasets/imdb.py lib/tf_model_zoo/models/differential_privacy/multiple_teachers/utils.py lib/tf_model_zoo/bninception/caffe_pb2.py lib/tf_model_zoo/models/transformer/cluttered_mnist.py lib/model/rpn/twin_transform.py lib/tf_model_zoo/models/slim/nets/lenet.py lib/tf_model_zoo/models/lm_1b/lm_1b_eval.py lib/datasets/vg_eval.py lib/datasets/pascal_voc.py lib/tf_model_zoo/models/inception/inception/flowers_data.py lib/tf_model_zoo/models/im2txt/im2txt/ops/image_embedding.py lib/tf_model_zoo/inceptionresnetv2/pytorch_load.py lib/tf_model_zoo/models/inception/inception/inception_distributed_train.py lib/tf_model_zoo/models/street/python/vgsl_train.py evaluation/eval_detection.py lib/tf_model_zoo/__init__.py lib/tf_model_zoo/models/slim/deployment/model_deploy_test.py lib/tf_model_zoo/models/slim/datasets/dataset_utils.py lib/model/roi_temporal_pooling/modules/roi_temporal_pool.py lib/tf_model_zoo/models/slim/nets/cifarnet.py lib/tf_model_zoo/models/inception/inception/slim/scopes_test.py lib/datasets/voc_eval.py lib/tf_model_zoo/models/neural_programmer/data_utils.py lib/tf_model_zoo/models/inception/inception/imagenet_distributed_train.py lib/model/rpn/anchor_target_layer.py lib/tf_model_zoo/models/swivel/prep.py lib/tf_model_zoo/models/slim/nets/resnet_utils.py lib/tf_model_zoo/models/inception/inception/data/process_bounding_boxes.py lib/tf_model_zoo/models/namignizer/model.py lib/tf_model_zoo/models/neural_programmer/nn_utils.py lib/tf_model_zoo/models/syntaxnet/syntaxnet/parser_eval.py lib/tf_model_zoo/models/inception/inception/inception_model.py lib/tf_model_zoo/models/autoencoder/autoencoder_models/DenoisingAutoencoder.py lib/tf_model_zoo/models/slim/nets/inception_v1_test.py lib/tf_model_zoo/models/swivel/vecs.py lib/tf_model_zoo/models/im2txt/im2txt/show_and_tell_model.py lib/tf_model_zoo/models/slim/preprocessing/cifarnet_preprocessing.py lib/model/nms/nms_wrapper.py lib/tf_model_zoo/models/street/python/shapes_test.py lib/tf_model_zoo/models/neural_programmer/parameters.py lib/tf_model_zoo/models/inception/inception/data/preprocess_imagenet_validation_data.py lib/tf_model_zoo/models/slim/preprocessing/vgg_preprocessing.py lib/tf_model_zoo/models/swivel/swivel.py lib/model/rpn/generate_anchors.py lib/tf_model_zoo/ECOfull/pytorch_load.py lib/tf_model_zoo/models/slim/datasets/flowers.py lib/tf_model_zoo/models/slim/nets/inception_v3_test.py lib/tf_model_zoo/models/textsum/seq2seq_attention.py lib/datasets/tools/mcg_munge.py lib/tf_model_zoo/models/neural_programmer/wiki_data.py lib/tf_model_zoo/models/autoencoder/autoencoder_models/Autoencoder.py lib/model/nms/build.py lib/tf_model_zoo/models/slim/datasets/download_and_convert_mnist.py lib/tf_model_zoo/models/slim/nets/resnet_v2.py lib/tf_model_zoo/models/syntaxnet/syntaxnet/load_parser_ops.py lib/tf_model_zoo/models/slim/nets/alexnet_test.py lib/tf_model_zoo/models/differential_privacy/dp_sgd/dp_optimizer/sanitizer.py lib/model/utils/blob.py lib/tf_model_zoo/models/inception/inception/slim/losses.py lib/roi_data_layer/minibatch.py lib/tf_model_zoo/models/inception/inception/data/build_image_data.py lib/tf_model_zoo/models/transformer/spatial_transformer.py lib/tf_model_zoo/models/differential_privacy/multiple_teachers/train_teachers.py lib/tf_model_zoo/models/im2txt/im2txt/inference_utils/caption_generator.py lib/tf_model_zoo/models/inception/inception/slim/inception_test.py lib/tf_model_zoo/models/namignizer/data_utils.py lib/tf_model_zoo/models/im2txt/im2txt/ops/image_embedding_test.py lib/tf_model_zoo/ECOfull/layer_factory.py lib/tf_model_zoo/models/autoencoder/autoencoder_models/VariationalAutoencoder.py lib/tf_model_zoo/models/syntaxnet/syntaxnet/lexicon_builder_test.py lib/model/utils/net_utils.py lib/tf_model_zoo/models/textsum/seq2seq_lib.py _init_paths.py lib/tf_model_zoo/bninception/pytorch_load.py lib/tf_model_zoo/models/im2txt/im2txt/inference_utils/caption_generator_test.py lib/tf_model_zoo/models/resnet/cifar_input.py lib/tf_model_zoo/models/video_prediction/prediction_model.py lib/model/nms/nms_cpu.py lib/tf_model_zoo/models/autoencoder/MaskingNoiseAutoencoderRunner.py lib/tf_model_zoo/models/slim/nets/inception_utils.py lib/tf_model_zoo/models/street/python/errorcounter.py lib/tf_model_zoo/models/video_prediction/prediction_input.py lib/model/rpn/proposal_target_layer_cascade.py lib/model/nl/fusion_modules.py lib/tf_model_zoo/models/inception/inception/inception_train.py lib/roi_data_layer/__init__.py lib/tf_model_zoo/models/slim/nets/vgg_test.py lib/tf_model_zoo/models/namignizer/names.py lib/tf_model_zoo/models/swivel/wordsim.py lib/tf_model_zoo/models/compression/msssim.py lib/tf_model_zoo/models/syntaxnet/syntaxnet/text_formats_test.py lib/tf_model_zoo/models/im2txt/im2txt/configuration.py lib/tf_model_zoo/models/slim/nets/inception_v3.py lib/tf_model_zoo/models/inception/inception/flowers_train.py lib/tf_model_zoo/models/street/python/errorcounter_test.py lib/model/tdcnn/modules.py lib/tf_model_zoo/models/textsum/data.py lib/tf_model_zoo/models/differential_privacy/multiple_teachers/metrics.py lib/tf_model_zoo/models/street/python/vgsl_model_test.py lib/model/nms/_ext/nms/__init__.py lib/tf_model_zoo/models/inception/inception/inception_eval.py lib/tf_model_zoo/models/differential_privacy/dp_sgd/dp_optimizer/utils.py lib/tf_model_zoo/models/differential_privacy/dp_sgd/dp_optimizer/dp_pca.py lib/tf_model_zoo/models/differential_privacy/multiple_teachers/aggregation.py lib/tf_model_zoo/models/inception/inception/slim/slim.py lib/tf_model_zoo/models/slim/nets/inception.py lib/tf_model_zoo/models/slim/nets/inception_v4.py lib/tf_model_zoo/models/slim/preprocessing/lenet_preprocessing.py lib/tf_model_zoo/models/slim/nets/inception_resnet_v2.py lib/tf_model_zoo/models/im2txt/im2txt/train.py lib/tf_model_zoo/models/slim/nets/nets_factory.py lib/tf_model_zoo/models/syntaxnet/syntaxnet/graph_builder.py lib/model/roi_temporal_pooling/_ext/roi_temporal_pooling/__init__.py evaluation/utils.py lib/datasets/coco.py lib/tf_model_zoo/models/differential_privacy/multiple_teachers/analysis.py lib/tf_model_zoo/models/autoencoder/AdditiveGaussianNoiseAutoencoderRunner.py lib/tf_model_zoo/models/differential_privacy/dp_sgd/dp_mnist/dp_mnist.py lib/tf_model_zoo/models/inception/inception/image_processing.py lib/tf_model_zoo/models/slim/train_image_classifier.py lib/tf_model_zoo/models/slim/datasets/download_and_convert_flowers.py lib/tf_model_zoo/models/im2txt/im2txt/inference_utils/inference_wrapper_base.py lib/tf_model_zoo/models/inception/inception/slim/ops.py lib/tf_model_zoo/models/slim/deployment/model_deploy.py lib/tf_model_zoo/models/street/python/shapes.py lib/tf_model_zoo/models/slim/datasets/mnist.py lib/tf_model_zoo/models/textsum/beam_search.py sampler do_test train_net nms_cpu get_roidb parse_args test_net add_path compute_average_precision_detection ANETdetection main parse_input get_blocked_videos segment_iou interpolated_prec_rec wrapper_segment_iou find_in_path customize_compiler_for_nvcc custom_build_ext coco unique_boxes xywh_to_xyxy validate_boxes xyxy_to_xywh filter_small_boxes get_imdb list_imdbs imagenet imdb pascal_voc pascal_voc vg vg_eval parse_rec voc_eval voc_ap munge BasicBlock LayerNorm _NonLocalBlockND fusion_modules nms_cpu nms_gpu nms _import_symbols RoITemporalPoolFunction _RoITemporalPooling _import_symbols _AnchorTargetLayer _unmap _compute_targets_batch generate_anchors _scale_enum _mkanchors _whctrs _ProposalLayer _ProposalTargetLayer res _RPN twins_overlaps twins_overlaps_batch twin_transform_batch twin_transform clip_twins twin_transform_inv make_layers _TDCNN C3D c3d_tdcnn prep_im_for_blob video_list_to_blob cfg_from_list get_output_tb_dir cfg_from_file _merge_a_into_b get_output_dir load_net _smooth_l1_loss _affine_theta vis_detections clip_gradient adjust_learning_rate save_checkpoint mask_rpn_losses _affine_grid_gen _crop_pool_layer save_net weights_normal_init NONLocalBlock1D NONLocalBlock2D _NonLocalBlockND NONLocalBlock3D GroupScale GroupOverSample GroupRandomSizedCrop GroupMultiScaleCrop get_minibatch MyThread prepare_im_func _get_video_blob roibatchLoader ReductionParameter ROIPoolingParameter HingeLossParameter BatchReductionParameter BlobProto BlobProtoVector NetStateRule LayerParameter PowerParameter FillerParameter ArgMaxParameter SegDataParameter InnerProductParameter V0LayerParameter ConvolutionParameter SolverState EltwiseParameter LossParameter SliceParameter WindowDataParameter DummyDataParameter HDF5OutputParameter TanHParameter TransformationParameter SoftmaxParameter ConcatParameter DataParameter SPPParameter ParamSpec SolverParameter MVNParameter ContrastiveLossParameter NetState NetParameter BiasParameter PoolingParameter DropoutParameter Datum VideoDataParameter SigmoidParameter BlobShape ExpParameter AccuracyParameter LogParameter ThresholdParameter MemoryOptimizationParameter MemoryDataParameter BNParameter LRNParameter ImageDataParameter ReLUParameter ReshapeParameter InfogainLossParameter ScaleParameter V1LayerParameter HDF5DataParameter PReLUParameter FlattenParameter PythonParameter build_relu build_bn build_pooling build_linear build_dropout build_conv get_basic_layer parse_expr CaffeVendor InceptionV3 BNInception build_pooling3d build_relu build_conv3d build_bn build_bn3d build_pooling build_linear build_dropout build_conv get_basic_layer parse_expr C3DRes18 build_pooling3d build_relu build_conv3d build_bn build_bn3d build_pooling build_identity build_linear build_dropout Identity build_conv get_basic_layer parse_expr ECO build_pooling3d build_relu build_conv3d build_bn build_bn3d build_pooling build_linear build_dropout build_conv get_basic_layer parse_expr ECOfull test_block35 load_conv2d_nobn load_block8 Mixed_6a test_conv2d inceptionresnetv2 load_block17 InceptionResnetV2 test_mixed_7a test_conv2d_nobn Mixed_5b load_linear test_mixed_5b test_mixed_6a test_block17 load_mixed_6a load_mixed_7a test BasicConv2d load_block35 Block35 load Block17 Block8 load_mixed_5b Mixed_7a test_block8 load_conv2d dump_block17 dump_mixed_6a dump_logits dump_block35 dump_mixed_5b dump_conv2d dump_block8 dump_mixed_7a dump_conv2d_nobn make_padding Mixed_4a test_conv2d load_mixed_7 load_mixed_4a_7a load_mixed_5 Mixed_5a load_linear Inception_C InceptionV4 Reduction_B load_mixed_6 test BasicConv2d test_mixed_4a_7a Reduction_A load Inception_B Inception_A Mixed_3a inceptionv4 load_conv2d dump_mixed_5 dump_mixed_7 dump_logits dump_mixed_4a_7a dump_conv2d make_padding dump_mixed_6 get_random_block_from_data standard_scale get_random_block_from_data standard_scale get_random_block_from_data standard_scale xavier_init min_max_scale get_random_block_from_data Autoencoder AdditiveGaussianNoiseAutoencoder MaskingNoiseAutoencoder VariationalAutoencoder main get_output_tensor_names get_input_tensor_names main get_output_tensor_names main _SSIMForMultiScale MultiScaleSSIM _FSpecialGauss main Eval MnistInput Train DPGradientDescentOptimizer ComputeDPPrincipalProjection AmortizedGaussianSanitizer NetworkParameters BuildNetwork SoftThreshold GenerateBinomialTable LayerParameters VaryRate BatchClipByL2norm GetTensorOpName ConvParameters AddGaussianNoise Conv2DPXG MatMulPXG Interface AddPXG PerExampleGradients PXGRegistry _ListUnion aggregation_most_frequent labels_from_probs noisy_max logmgf_exact compute_q_noisy_max smoothed_sens compute_q_noisy_max_approx main logmgf_from_counts sens_at_k train_op_fun loss_fun _input_placeholder train inference_deeper inference _variable_with_weight_decay _variable_on_cpu softmax_preds moving_av ld_svhn extract_mnist_labels partition_dataset ld_mnist unpickle_cifar_dic create_dir_if_needed image_whitening ld_cifar10 extract_mnist_data extract_svhn extract_cifar10 maybe_download accuracy main ensemble_preds train_student prepare_student_data main train_teacher batch_indices integral_bounded_mp integral_inf_mp _compute_delta compute_log_moment compute_b_mp cropped_ratio distributions distributions_mp _to_np_float64 _compute_eps compute_b pdf_gauss_mp integral_bounded pdf_gauss compute_a_mp get_privacy_spent integral_inf compute_a GaussianMomentsAccountant AmortizedAccountant MomentsAccountant DummyAccountant TrainingConfig ModelConfig main run_once evaluate_model run InferenceWrapper main ShowAndTellModel ShowAndTellModelTest ShowAndTellModel main _process_dataset _process_image_files _int64_feature Vocabulary _bytes_feature_list _create_vocab _load_and_process_metadata _process_caption _bytes_feature ImageDecoder _to_sequence_example main _int64_feature_list Caption TopN CaptionGenerator FakeVocab CaptionGeneratorTest FakeModel InferenceWrapperBase Vocabulary inception_v3 InceptionV3Test process_image distort_image parse_sequence_example batch_with_dynamic_pad prefetch_input_data Dataset FlowersData main main ImagenetData main main main image_preprocessing batch_inputs inputs parse_example_proto distorted_inputs distort_color eval_image decode_jpeg distort_image train evaluate _eval_once inference _activation_summaries loss _activation_summary train _average_gradients _tower_loss ImageCoder _convert_to_example _process_image_files _process_image _int64_feature _process_dataset _is_cmyk _find_image_files _build_bounding_box_lookup _find_human_readable_labels _build_synset_lookup _find_image_bounding_boxes _bytes_feature _float_feature main _process_image_files_batch _is_png ImageCoder _convert_to_example _process_image_files _process_dataset _int64_feature _find_image_files _bytes_feature _is_png main _process_image_files_batch _process_image BoundingBox FindNumberBoundingBoxes GetInt ProcessXMLAnnotation GetItem get_variables_by_name get_variables CollectionsTest inception_v3 inception_v3_parameters InceptionTest l2_regularizer cross_entropy_loss l1_loss l1_l2_regularizer l1_regularizer l2_loss RegularizersTest LossesTest CrossEntropyLossTest one_hot_encoding batch_norm dropout fc max_pool _two_element_tuple conv2d flatten repeat_op avg_pool FCTest ConvTest FlattenTest DropoutTest OneHotEncodingTest AvgPoolTest BatchNormTest MaxPoolTest _current_arg_scope _add_op add_arg_scope _get_arg_stack arg_scope has_arg_scope func2 func1 ArgScopeTest variable_device global_step variable get_unique_variable get_variables_by_name add_variable get_variables_to_restore VariableDeviceChooser get_variables VariablesTest GetVariablesByNameTest GlobalStepTest CharsVocabulary LM1BDataset get_batch Vocabulary _SampleModel _SampleSoftmax _LoadModel _DumpSentenceEmbedding main _DumpEmb _EvalModel read_names _letter_to_number name_to_batch namignizer_iterator NamignizerModel decode init_data get_batch to_symbol accuracy to_id add pad print_out safe_exp tanh_cutoff quantize NeuralGPU sigmoid_cutoff relaxed_distance check_for_zero conv_linear conv_gru quantize_weights_op _custom_id_grad relaxed_average make_dense initialize animate multi_test evaluate single_test main train interactive exact_match complete_wiki_processing perform_word_cutoff word_dropout word_lookup get_max_entry generate_feed_dict convert_to_int_2d_and_pad pick_one add_special_words construct_vocab partial_match partial_column_match convert_to_bool_and_pad group_by_max exact_column_match return_index check_processed_cols list_join Graph LSTMCell apply_dropout get_embedding Parameters WikiExample simple_normalize TableInfo is_number WikiQuestionLoader WikiQuestionGenerator final_normalize correct_unicode is_date is_nan_or_inf full_normalize strip_accents build_input main train evaluate ResNet main main _configure_learning_rate _configure_optimizer _get_init_fn main _add_variables_summaries _get_variables_to_train get_split get_dataset download_and_uncompress_tarball image_to_tfexample write_label_file int64_feature bytes_feature read_label_file has_labels _add_to_tfrecord _download_and_uncompress_dataset _get_output_filename _clean_up_temporary_files run _dataset_exists _convert_dataset run _clean_up_temporary_files _get_filenames_and_classes _get_dataset_filename ImageReader _add_to_tfrecord _extract_labels _get_output_filename _download_dataset _extract_images _clean_up_temporary_files run get_split get_split create_readable_names_for_imagenet_labels get_split _gather_clone_loss deploy _add_gradients_summaries _optimize_clone create_clones optimize_clones DeploymentConfig _sum_clones_gradients DeploymentConfigTest DeployTest OptimizeclonesTest BatchNormClassifier CreatecloneTest LogisticClassifier alexnet_v2 alexnet_v2_arg_scope AlexnetV2Test cifarnet_arg_scope cifarnet inception_resnet_v2_arg_scope inception_resnet_v2 block8 block35 block17 InceptionTest inception_arg_scope inception_v1_base inception_v1 InceptionV1Test inception_v2_base _reduced_kernel_size_for_small_input inception_v2 InceptionV2Test inception_v3 _reduced_kernel_size_for_small_input inception_v3_base InceptionV3Test inception_v4 block_reduction_b inception_v4_base block_inception_b block_inception_c block_reduction_a block_inception_a InceptionTest lenet lenet_arg_scope get_network_fn NetworksTest overfeat overfeat_arg_scope OverFeatTest Block conv2d_same subsample resnet_arg_scope stack_blocks_dense resnet_v1_152 resnet_v1_101 bottleneck resnet_v1_200 resnet_v1_50 resnet_v1 ResnetUtilsTest ResnetCompleteNetworkTest create_test_input resnet_v2_50 resnet_v2_200 resnet_v2_101 resnet_v2_152 bottleneck resnet_v2 ResnetUtilsTest ResnetCompleteNetworkTest create_test_input vgg_16 vgg_arg_scope vgg_a vgg_19 VGG16Test VGGATest VGG19Test preprocess_image preprocess_for_train preprocess_for_eval distorted_bounding_box_crop preprocess_for_train preprocess_for_eval preprocess_image distort_color apply_with_random_selector preprocess_image get_preprocessing _aspect_preserving_resize preprocess_for_train _crop _central_crop _smallest_size_at_least _mean_image_subtraction preprocess_for_eval preprocess_image _random_crop Decoder DecoderTest _testdata ComputeErrorRates AddErrors CountWordErrors CountErrors ComputeErrorRate ErrorcounterTest lstm_layer _variable_lstm_shape _variable_lstm_grad rnn_helper rotate_dimensions tensor_dim transposing_reshape tensor_shape DataTest _rand ShapesTest VGSLSpecs _rand VgslspecsTest main _ImageProcessing ImageInput _ReadExamples VGSLImageModel _ParseInputSpec _PadLabels2d Train _ParseOutputSpec _PadLabels3d Eval _AddRateToSummary InitNetwork _rand VgslModelTest _testdata main make_shard_files main write_vocab_and_sums compute_coocs words create_vocabulary write_shards main count_matrix_input write_embeddings_to_disk embeddings_with_init SwivelModel write_embedding_tensor_to_disk main read_marginals_file go Vecs evaluate ParsingReaderOpsTest main to_dict GreedyParser BatchedSparseToDense EmbeddingLookupFeatures GraphBuilderTest LexiconBuilderTest main Eval RewriteContext OutputPath WriteStatus Train RewriteContext main Eval StageName ParsingReaderOpsTest AddCrossEntropy StructuredGraphBuilder TextFormatsTest Batcher BeamSearch Hypothesis Pad SnippetGen Ids2Words GetExFeatureText Vocab ToSentences ExampleGen GetWordIds main _binary_to_text _text_to_binary main _Train _RunningAvgLoss _Eval BSDecoder DecodeIO Seq2SeqAttentionModel _extract_argmax_and_embed sampled_sequence_loss sequence_loss_by_example linear transformer batch_transformer linear conv2d dense_to_one_hot bias_variable weight_variable basic_conv_lstm_cell init_state build_tfrecord_input construct_model scheduled_sample cdna_transformation stp_transformation dna_transformation main peak_signal_to_noise_ratio Model mean_squared_error add_argument ArgumentParser load open sum ne shot backward info tdcnn_demo zero_grad numel tqdm mean item train step cuda range enumerate minimum maximum item append numpy data test_nms batch_size tdcnn_demo ANETdetection clip_twins dataset twin_transform_inv cuda seed list num_classes view transpose append get_logger_dir range cat format shot insert hstack eval TWIN_NORMALIZE_TARGETS_PRECOMPUTED mAP tile info nms_cpu item enumerate join RNG_SEED evaluate TWIN_REG sort print type_as tqdm dict cpu numpy array len items list format replace isinstance test_nms gpus OrderedDict DataParallel eval load_state_dict info is_available train cuda test_net insert groupby iterrows reset_index get_group ones astype interpolated_prec_rec range zeros float empty segment_iou enumerate values len evaluate ANETdetection add_argument ArgumentParser Request urlopen format sum hstack max minimum clip astype maximum empty segment_iou range pathsep pjoin exists split append _compile compiler_so dot array unique minimum max argmax eps cumsum float astype maximum voc_ap argsort zip zeros bool sum array range len 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 join format print rename splitext listdir makedirs int nms_cuda dir _wrap_function getattr append callable fill_ array _scale_enum hstack _whctrs _mkanchors stack log exp unsqueeze clone clamp_ stack log expand_as view size min mask_fill_ expand max view size min contiguous expand masked_fill_ max Conv3d transpose shape zeros range len astype float32 resize join EXP_DIR name abspath ROOT_DIR makedirs join EXP_DIR name abspath ROOT_DIR makedirs items list ndarray isinstance type array _merge_a_into_b literal_eval zip split items list File create_dataset items list asarray File from_numpy copy_ isinstance Conv2d normal_ modules Linear new_full requires_grad norm parameters sqrt mul_ max minimum tuple putText FONT_HERSHEY_PLAIN rectangle range param_groups save sum sorted mean pow float abs float size view binary_cross_entropy view grid_sample Variable Size size affine_grid zero_ max_pool2d POOLING_SIZE detach view Variable Size affine_grid zero_ detach Variable view zero_ detach _get_video_blob randint empty zeros len join sorted tuple prep_im_for_blob waitKey zfill imshow PIXEL_MEANS imread listdir CROP_SIZE destroyAllWindows tuple destroyAllWindows list sorted TEMP_SPARSE_SAMPLING prep_im_for_blob waitKey imshow PIXEL_MEANS ceil append imread CROP_SIZE range format listdir zeros enumerate int join video_list_to_blob print zfill randint len split parse_expr Conv2d MaxPool2d AvgPool2d Conv3d MaxPool3d AvgPool3d int isinstance load_url InceptionResnetV2 load_state_dict ones size File close from_numpy permute from_numpy File close permute t File close from_numpy load_conv2d load_conv2d_nobn load_conv2d load_conv2d load_conv2d_nobn load_conv2d load_conv2d load_conv2d_nobn load_conv2d load_block8 load_mixed_5b load_mixed_6a load_mixed_7a load_block35 load_block17 load_linear range load_conv2d transpose_ sum data ones print Variable File close dist mean from_numpy softmax imread forward std transpose_ register_forward_hook File close from_numpy transpose_ register_forward_hook File close from_numpy test_conv2d branch0 conv2d test_conv2d branch0 test_conv2d_nobn test_conv2d branch0 conv2d test_conv2d branch0 test_conv2d_nobn test_conv2d conv2d test_conv2d branch0 test_conv2d_nobn decode exit get_shape get_operation_by_name File system close eval create_dataset get_attr make_padding get_tensor_by_name get_shape get_operation_by_name File system close eval create_dataset get_attr make_padding get_tensor_by_name print get_operation_by_name File close eval create_dataset get_tensor_by_name dump_conv2d dump_conv2d dump_conv2d_nobn dump_conv2d dump_conv2d dump_conv2d_nobn dump_conv2d dump_conv2d dump_conv2d_nobn load_url load_state_dict InceptionV4 load_conv2d load_conv2d load_conv2d load_conv2d load_mixed_6 load_mixed_7 load_mixed_4a_7a load_mixed_5 eval zeros test_conv2d dump_conv2d dump_conv2d dump_conv2d dump_conv2d transform fit randint len sqrt transform fit append format range print output_directory iteration MkDir append format range packbits asarray BytesIO reshape savez_compressed exp float64 reshape min astype mean shape _FSpecialGauss fftconvolve ones size _SSIMForMultiScale append array range MultiScaleSSIM placeholder string expand_dims decode_png read TFRecordReader string_input_producer reshape float32 cast parse_single_example shuffle_batch decode_png batch update batch_size hidden_layer_num_units projection_dimensions NetworkParameters eval_data_path save_path default_gradient_l2norm_bound num_hidden_layers Train num_training_steps LayerParameters ConvParameters training_data_path range append self_adjoint_eig l2_normalize slice sanitize transpose reshape matmul shape top_k rsplit patch_size layer_parameters input_size with_bias weight_decay num_units projection_dimensions name matmul conv2d relu in_channels sqrt truncated_normal Variable reshape max_pool gradient_l2norm_bound num_outputs zeros bias_gradient_l2norm_bound conv_parameters zeros range append pop list isinstance inputs extend set OrderedDict append union pxg_rule list gradients zip Interface OrderedDict _ListUnion append pxg_registry shape argmax len int asarray labels_from_probs reshape shape bincount zeros argmax range int asarray labels_from_probs reshape shape bincount zeros argmax range argmax array exp argmax array len pow exp log compute_q_noisy_max print logmgf_from_counts sorted exp max sens_at_k input_is_counts log str list getcwd shape delta astype max_examples moments noise_eps maybe_download load counts_file min indices_file int32 beta array mul add_to_collection _variable_on_cpu l2_loss truncated_normal_initializer lrn max_pool sparse_softmax_cross_entropy_with_logits int64 reduce_mean cast add_to_collection get_collection ExponentialMovingAverage apply int trainable_variables learning_rate batch_size name scalar_summary apply nb_teachers apply_gradients moving_av ExponentialMovingAverage exponential_decay float epochs_per_decay histogram_summary batch_size _input_placeholder inference_deeper deeper reset_default_graph softmax ExponentialMovingAverage Saver ceil inference variables_to_restore zeros len MakeDirs urlretrieve endswith print stat append ones print maximum mean sqrt shape std range len load close open unpickle_cifar_dic reshape extractall swapaxes save append data_dir hstack image_whitening vstack extract_svhn maybe_download data_dir image_whitening extract_cifar10 maybe_download data_dir extract_mnist_data extract_mnist_labels maybe_download int len sum float argmax len str print deeper teachers_max_steps teachers_dir zeros range softmax_preds ld_svhn noisy_max str lap_scale ld_mnist print data_dir accuracy ld_cifar10 ensemble_preds softmax_preds str print accuracy prepare_student_data deeper max_steps train_dir ld_svhn str softmax_preds partition_dataset ld_mnist train_dir print accuracy ld_cifar10 deeper max_steps len int inf quad quad int format print binom ceil range format print distributions b_fn integral_bounded integral_inf log compute_a quad quad int integral_inf_mp format print distributions_mp ceil int integral_inf_mp format integral_bounded_mp print distributions_mp b_fn compute_a_mp ceil log exp min write float min write log assert_array_less compute_b_mp isinf compute_a_mp compute_a assert_allclose int time exp flush info batch_size add add_summary ceil num_eval_examples range Summary run latest_checkpoint checkpoint_dir info Graph MakeDirs info eval_dir run vocab_file Vocabulary Graph Glob len extend finalize input_files info split inception_checkpoint_file TrainingConfig input_file_pattern MakeDirs train ModelConfig train_dir FeatureLists SequenceExample decode_jpeg Features int join arange TFRecordWriter print astype write SerializeToString close output_dir _to_sequence_example range flush len seed int Thread join print min astype shuffle Coordinator start num_threads ImageDecoder append range len update Vocabulary print sort Counter dict len word_tokenize extend lower end_word append join setdefault print ImageMetadata append int val_captions_file _process_dataset train_captions_file val_shards _create_vocab train_shards _load_and_process_metadata val_image_dir output_dir test_shards train_image_dir list l2_regularizer summarize_activation values resize_images random_crop mul resize_image_with_crop_or_pad image_summary convert_image_dtype sub distort_image parse_single_sequence_example enqueue fatal string_input_producer len FIFOQueue cast append range add_queue_runner size Glob info QueueRunner read extend float32 RandomShuffleQueue scalar_summary split batch_join slice ones reduce_max reduce_sum add reduce_mean sub append expand_dims reduce_min scalar_summary eval_dir Exists DeleteRecursively FlowersData join Server ClusterSpec ImagenetData target batch_size batch_size mul decode_jpeg sub eval_image image_size distort_image update concat transpose cast VarLenFeature parse_single_example expand_dims values num_replicas_to_aggregate len _activation_summaries value batch_size reshape concat sparse_to_dense cross_entropy_loss range name zero_fraction sub scalar_summary histogram_summary REGULARIZATION_LOSSES name get_collection apply average add_n ExponentialMovingAverage sub TOWER_NAME inference LOSSES_COLLECTION loss scalar_summary concat reduce_mean zip append expand_dims Example print _is_cmyk cmyk_to_rgb png_to_jpeg _is_png decode_jpeg int join _convert_to_example arange _process_image TFRecordWriter print astype write SerializeToString close output_directory range flush len ImageCoder Thread Coordinator start append seed list print Glob extend shuffle range len append append basename print _process_image_files _find_image_files _find_human_readable_labels _find_image_bounding_boxes labels_file readlines split print readlines append float split imagenet_metadata_file train_directory _build_bounding_box_lookup validation_directory _build_synset_lookup bounding_box_file validation_shards labels_file iter BoundingBox parse height ymin FindNumberBoundingBoxes min ymax xmin xmax GetInt getroot width append float max range GetItem get_shape assert_is_compatible_with get_shape TensorShape isinstance num_elements pop add_to_collection get_collection _get_arg_stack add update append copy isinstance _add_op append add_to_collection get_collection VARIABLES name NodeDef callable device GLOBAL_STEP get_collection append list set list ones min float32 int32 zeros next range len exp get_batch ckpt _LoadModel error write isnan eval pbtxt enumerate run _SampleSoftmax ckpt ones _LoadModel word_to_char_ids id_to_word write float32 num_samples int32 zeros range pbtxt run ckpt ones _LoadModel reshape size write float32 int32 zeros save_dir range pbtxt run join ckpt ones _LoadModel len write float32 int32 zeros save_dir range pbtxt run _SampleModel sentence input_data CharsVocabulary prefix _DumpSentenceEmbedding LM1BDataset _DumpEmb _EvalModel namedtuple tolist lower sum array read_csv list reshape map choice zeros sum range zeros list map reshape append max range len time rand_pair rand_dup_pair rand_kvsort_pair rand_rev2_pair spec print_out append float range rand_search_pair choice pad append array write flush decode task_print min range exp sigmoid tanh conv_lin sigmoid_cutoff tanh minimum maximum add_n range len append trainable_variables assign relaxed_average init_data kh noclass MkDir set_random_seed kw max_grad_norm forward_max uniform_unit_scaling_initializer max run seed restore niclass rx_step print_out curriculum_bound append initialize_all_variables cutoff range max_length train_dir nconvs height pull dropout get_checkpoint_state NeuralGPU lr set_initializer random_seed pull_incr join jobid min model_checkpoint_path mode nmaps split restore get_batch accuracy print_out append float step min print_out single_test low_batch_size float max range split batch_size zeros_like set_xticklabels text set_yticks grid add_axes imshow set_zorder set_xticks figure save append range FuncAnimation split batch_size interactive range len number_columns deepcopy isinstance Number question word_column_names word_columns append word_ids number_column_names len dummy_token max_entry_length append range len tolist max_elements range len append range len append range len append range len append range len append get_max_entry range len range len exact_match number_lookup_matrix word_exact_match float64 original_nc_names word_print_answer word_group_by_max calc_answer column_names number_columns sorted columns list unk_token ones tolist max_number_cols column_match_token max_entry_length word_columns convert_to_int_2d_and_pad entry_match_token append ordinal_question number_print_answer word_lookup_matrix range number_group_by_max string_question replace original_nc original_wc_names question_attention_mask original_wc partial_match question max_word_cols max_elements ordinal_question_one number_column_exact_match partial_column_match number_column_names is_lookup group_by_max convert_to_bool_and_pad exact_column_match question_length check_processed_cols number_exact_match word_column_names word_column_exact_match zeros is_bad_example len unk_token print entry_match_token column_match_token dummy_token append word_ids len pop remove keys list append range len append reshape range dropout sigmoid tanh bias_add matmul str join strip_accents strip sub correct_unicode sub final_normalize simple_normalize strip sub lower strip float range len concat enqueue image_summary string_input_producer resize_image_with_crop_or_pad transpose FIFOQueue cast range sparse_to_dense per_image_whitening random_crop add_queue_runner random_flip_left_right slice Glob QueueRunner FixedLengthRecordReader read decode_raw uint8 reshape float32 dequeue_many int32 RandomShuffleQueue Stop build_graph dataset argmax Summary run prepare_or_wait_for_session add Supervisor train_dir SummaryWriter build_input ResNet mean info flush train_data_path add_summary mode build_graph eval_dir Saver dataset argmax max Session Summary run eval_once restore eval_batch_count add sleep range start_queue_runners SummaryWriter build_input eval_data_path get_checkpoint_state ResNet info log_root flush model_checkpoint_path add_summary mode HParams dataset_dir set_verbosity INFO int sync_replicas num_epochs_per_decay batch_size MomentumOptimizer AdagradOptimizer GradientDescentOptimizer AdamOptimizer RMSPropOptimizer AdadeltaOptimizer FtrlOptimizer name get_model_variables append scalar_summary histogram_summary checkpoint_path latest_checkpoint get_model_variables checkpoint_exclude_scopes IsDirectory startswith info append train_dir extend get_collection TRAINABLE_VARIABLES join TFRecordReader TFExampleDecoder read_label_file has_labels join urlretrieve print extractall stat join join index split reshape join urlretrieve print extractall stat join Remove DeleteRecursively download_and_uncompress_tarball list print _get_output_filename write_label_file dict zip _clean_up_temporary_files range len append join listdir isdir int write ceil float flush len _get_dataset_filename range seed shuffle _dataset_exists _convert_dataset _get_filenames_and_classes print print _extract_images _extract_labels print join urlretrieve _download_dataset format urlretrieve readlines len split create_readable_names_for_imagenet_labels write_label_file scope scalar_summary _gather_clone_loss REGULARIZATION_LOSSES get_collection add_n _sum_clones_gradients len SUMMARIES get_collection set scope create_clones UPDATE_OPS append add_n zip global_norm isinstance name IndexedSlices info append values histogram_summary batch_norm as_list prediction_fn hasattr default_image_size pad to_float random_crop random_flip_left_right image_summary random_brightness pad random_contrast expand_dims to_float resize_image_with_crop_or_pad expand_dims image_summary random_uniform to_float resize_image_with_crop_or_pad sub div pack greater_equal slice to_int32 logical_and with_dependencies Assert shape rank equal greater_equal reshape logical_and with_dependencies extend Assert shape rank random_uniform append range equal len append _crop range split convert_to_tensor to_float to_int32 greater cond convert_to_tensor resize_bilinear squeeze shape set_shape _smallest_size_at_least expand_dims _aspect_preserving_resize set_shape random_uniform set_shape _aspect_preserving_resize subtract sum Counter test_count fn truth_count fp merge_with with_rank list range tensor_shape get_shape rotate_dimensions reshape transpose len get_shape append tensor_dim range len num_steps eval_interval_secs decoder graph_def_file model_str eval_data Eval batch_join sparse_tensor_to_dense string_input_producer batch_size reshape Glob identity image_summary int64 cast _ReadExamples deserialize_many_sparse int32 append range read TFRecordReader reader serialize_sparse reshape parse_single_example _ImageProcessing mul float32 sub set_shape cast decode_png ReplicaDeviceSetter startswith ErrorRates Decoder Build rfind VGSLImageModel find tensor_shape reshape _PadLabels2d int match group compile int match group compile Summary add_summary task ps_tasks initial_learning_rate master num_preprocess_threads learning_rate_halflife final_learning_rate optimizer_type train_data max_steps join read pack seek SEEK_END shard_size print tell size write SEEK_SET any unpack output_dir range flush open shard_size makedirs max_vocab seek SEEK_END setdefault print tell sort words write SEEK_SET min len flush enumerate join seek SEEK_END setdefault shard_size flush_coocs tell min write SEEK_SET enumerate window_size output_dir open range flush len join read items seek shard_size name sort write close unlink Example output_dir range flush len vocab write_vocab_and_sums write_shards read string_input_producer reshape concat WholeFileReader parse_single_example batch sparse_to_dense values run row_embedding print col_embedding input_base_path output_base_path write_embedding_tensor_to_disk flush output_base_path spearmanr list _get_dict OrderedDict append token range len fill float32 constant diag convert_to_tensor embedding_lookup concat unpack_sparse_features join TaskSpec file_pattern resource_dir input part slim_model document_sink batch_size GreedyParser StructuredGraphBuilder string values run list restore len map placeholder feature_size resource_dir info model_path AddEvaluation time AddSaver task_context RewriteContext split OutputPath name add join OutputPath WriteStatus save max output_path slim_model GreedyParser StructuredGraphBuilder word_embeddings report_every Eval values run list AddTraining name node map append pretrained_params OutputPath info AddEvaluation time AddSaver AddPretrainedEmbeddings output_path split projectivize_training_set OutputPath compute_lexicon RewriteContext slice reshape cond append range glob read shuffle open len append WordToId split index len SnippetGen join in_file read feature FromString write close out_file append open pack readlines SerializeToString extend len write close Example out_file open split _binary_to_text _text_to_binary min write add add_summary Summary build_graph eval_dir Saver Session restore tolist sleep train_dir eval_interval_secs SummaryWriter NextBatch get_checkpoint_state info log_root flush join Ids2Words run_eval_step model_checkpoint_path add_summary _RunningAvgLoss vocab_path beam_size abstract_key max_abstract_sentences BSDecoder Seq2SeqAttentionModel max_article_sentences _Eval Batcher set_random_seed data_path _replace DecodeLoop _Train Vocab article_key random_seed as_list zeros random_normal zeros array arange state_initializer dtype pack set_shape list init_state use_state batch_size concat floor set_shape parse_single_example str TFRecordReader string_input_producer data_dir resize_image_with_crop_or_pad cast append range Glob decode_jpeg batch join int read reshape min resize_bicubic train_val_split float32 sequence_length zeros len to_float exp to_int32 int32 zip bool round array convert_to_tensor fully_connected float32 transformer append array range int relu fully_connected reshape squeeze concat depthwise_conv2d reduce_sum tile zip append expand_dims split int relu slice concat reduce_sum pad append expand_dims range gather int range random_shuffle Saver save restore get_collection event_log_dir initialize_all_variables start_queue_runners SummaryWriter flush InteractiveSession num_iterations pretrained_model add_summary VARIABLES | # Localizing the Common Action Among a Few Videos(ECCV20) This paper strives to localize the temporal extent of an action in a long untrimmed video. Where existing work leverages many examples with their start, their ending, and/or the class of the action during training time, we propose few-shot common action localization. The start and end of an action in a long untrimmed video is determined based on just a hand-full of trimmed video examples containing the same action, without knowing their common class label. To address this task, we introduce a new 3D convolutional network architecture able to align representations from the support videos with the relevant query video segments. The network contains: (i) a mutual enhancement module to simultaneously complement the representation of the few trimmed support videos and the untrimmed query video; (ii) a progressive alignment module that iteratively fuses the support videos into the query branch; and (iii) a pairwise matching module to weigh the importance of different support videos. Evaluation of few-shot common action localization in untrimmed videos containing a single or multiple action instances demonstrates the effectiveness and general applicability of our proposal. For more details, please check our [paper](https://arxiv.org/abs/2008.05826). ### System Requirements * cuda=9.0 * python 3.6 * gcc=5.5.0 * torch=0.4(currently doesn't support torch0.4.1, for a smooth installation of NMS, see https://github.com/jwyang/faster-rcnn.pytorch/issues/235#issuecomment-409493006) ### Package Requirements ``` | 846 |
PengjieRen/RepeatNet | ['session based recommendations'] | ['RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation'] | repeat_net.py gru_rec.py repeat_non_repeat_bar.py base/recsys.py train.py base/n_step_gru.py base/decoder.py tmp.py base/utils.py base/corpus.py base/attention.py data/lastfm/split_lastfm.py base/function.py test.py data/lastfm/process_lastfm.py base/selective_gate.py att_rec.py base/encoder.py data/lastfm/data_aug_lastfm.py AttRec GruRec NoAttRepeatNet RepeatNet Attention AttDecoder AttReDecoder NoAttReDecoder GruDecoder NStepSelBiGRUEncoder NStepGRUEncoder repeat Repeat Explore expore NStepBiGRU NStepGRU NStepGRUBase RecSys SelectiveGate evaluate evaluates mrr mask recall append max len evaluate mrr test report append recall range len range range | Note: a new pytorch version is provided at https://github.com/PengjieRen/RepeatNet-pytorch. The repo will not be maintained any more. This is our implementation for the paper: Pengjie Ren, Jing Li, Zhumin Chen, Zhaochun Ren, Jun Ma and Maarten de Rijke (2019). RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation. In Proceedings of The Thirty-Third AAAI Conference on Artificial Intelligence. Please cite our AAAI'19 paper. Thanks! We would like to thank Jianqiang Sun for pointing out the reverse order issue when processing lastfm data! | 847 |
PerryXDeng/adversarial_mnist_attacks | ['adversarial attack'] | ['Towards Evaluating the Robustness of Neural Networks'] | adversarial_payload_optimizer.py adversarial_configuration.py data_preparation.py neural_network_trainer.py datasets_loader/mnist.py neural_network.py neural_network_configuration.py main payload_derivatives optimize_payload generate_payload generate_x_y find_and_save_sample_images load_datasets denormalize load_image load_sample_image vectorized_label normalize save_image regularize feed_forward predict activation accuracy sigmoid cost_derivatives cross_entropy gradient_descent rand_init_weights main gradient_descent_fakedata download_mnist load save_mnist init T LAYERS_NUM ndarray list multiply feed_forward matmul reversed float range load argmax payload_derivatives LAYERS_NUM ndarray arange print multiply xlabel ylabel copy bar title legend LAMBDA sum show denormalize reshape optimize_payload load_sample_image vectorized_label imshow normalize save_image input int eval generate_payload zeros rand hstack choice load hstack fromarray reshape save load str save_image range flatten str T LAYERS_NUM ndarray activation range LAYERS_NUM REG_CONST range T LAYERS_NUM ndarray list float multiply feed_forward matmul reversed zeros matrix range load feed_forward LAYERS_NUM ndarray predict rand range LAYERS_NUM generate_x_y rand_init_weights LAYERS_NUM ndarray regularize print feed_forward accuracy cost_derivatives NUM_EPOCHS float range LAYERS_NUM load_datasets regularize print feed_forward accuracy cost_derivatives NUM_EPOCHS float range load rand_init_weights LAYERS_NUM ndarray strip gradient_descent save print urlretrieve print download_mnist save_mnist | # MNIST Neural Network Adversarial Attacks A demonstration of gradient-based adversarial attacks on a simple 90% accuracy implementation of a MNIST hand-written digit classification neural network with one hidden layer and 32 hidden units. The attacker differentiates the neural network to make it misclassify an image that is euclideanly close to a handwritten "i" as a "j", where 0 <= i <= 9, 0 <= j <= 9, and i =\= j. Usage: python3 adversarial_payload_optimizer.py Required libraries: numpy, matplotlib Adjust optimization parameters in adversarial_configuration.py (lambda, threshold, and learning rate primarily) as you see fit. # Related Research | 848 |
PeteWe/ts_similarity_tensorflow | ['time series', 'dynamic time warping'] | ['Soft-DTW: a Differentiable Loss Function for Time-Series'] | SoftDTWTF.py CIDTF.py EDTF.py CIDTF SoftDTWTF | # Similarity measures for time series implemented in tensorflow. ### 1. Soft-Dynamic Time Warping (SDTW). - File: [SoftDTW.py](https://github.com/PeteWe/ts_similarity_tensorflow/blob/master/SoftDTWTF.py) - Notes: Inputs of shape: (n,t,f). - n: number of multivariate time series. - t: length of time series. - f: number of features. - Source: [Cuturi, Blondel (2017) - Soft-DTW: a Differentiable Loss Function for Time-Series](https://arxiv.org/pdf/1703.01541.pdf) ### 2. Complexity-Invariant Distance (CID). - File: [CIDTF.py](https://github.com/PeteWe/ts_similarity_tensorflow/blob/master/CIDTF.py) | 849 |
PhIMaL/temporal_normalizing_flows | ['density estimation'] | ['Temporal Normalizing Flows'] | setup.py src/temporal_normalizing_flows/preprocessing.py src/temporal_normalizing_flows/neural_flow.py src/temporal_normalizing_flows/latent_distributions.py src/temporal_normalizing_flows/__init__.py gaussian neural_flow prepare_data dtype ones_like permutation arange neural_flow_data namedtuple concatenate size shape meshgrid tensor empty | PhIMaL/temporal_normalizing_flows | 850 |
Phoebe-star/AlignedReID | ['person re identification'] | ['AlignedReID: Surpassing Human-Level Performance in Person Re-Identification'] | cmc.py heads/fc1024.py big_dataset_label.py nets/mobilenet_v1_1_224.py heads/direct.py nets/__init__.py heads/__init__.py heads/fc1024_normalize.py RES.py nets/resnet_utils.py top1.py triplet_loss.py nets/resnet_v1_101.py RES_2.py nets/resnet_v1.py nets/mobilenet_v1.py RES_cmc.py nets/resnet_v1_50.py heads/direct_normalize.py _cmc_core cmc batch_all_triplet_loss _get_anchor_negative_triplet_mask batch_hard_triplet_loss _get_anchor_positive_triplet_mask _pairwise_distances _get_triplet_mask head head head head mobilenet_v1_arg_scope mobilenet_v1 _reduced_kernel_size_for_small_input mobilenet_v1_base wrapped_partial endpoints mobilenet_v1_arg_scope Block conv2d_same subsample resnet_arg_scope stack_blocks_dense resnet_v1_152 resnet_v1_101 bottleneck resnet_v1_200 resnet_v1_50 resnet_v1 resnet_v1_block endpoints endpoints print cumsum argsort shape sum asarray arange isinstance print size choice shape unique xrange zeros enumerate to_float transpose maximum matmul diag_part sqrt expand_dims equal logical_and logical_not expand_dims cast eye bool equal expand_dims equal logical_not bool logical_and logical_not cast eye expand_dims equal to_float multiply maximum greater reduce_sum _pairwise_distances expand_dims _get_triplet_mask to_float multiply reduce_max _get_anchor_negative_triplet_mask maximum reduce_sum greater float32 reduce_mean cast _get_anchor_positive_triplet_mask _pairwise_distances reduce_min cond scalar fully_connected l2_normalize as_list partial update_wrapper as_list l2_regularizer truncated_normal_initializer reduce_mean divide pad constant | # AlignedReID https://arxiv.org/abs/1711.08184 data folder is cuhk03 dataset ``` https://drive.google.com/open?id=1Qgz7eXFTP5Hi7hO1ew7ZWFSkh_vdl57e ``` 使用cd 到 AlignedReID_train folder 路徑 ```bash cd AlignedReID_train ``` | 851 |
Phoebe-star/part_aligned | ['person re identification'] | ['Deeply-Learned Part-Aligned Representations for Person Re-Identification'] | heads/fc1024_normalize.py cmc.py heads/fc1024.py big_dataset_label.py RES.py nets/mobilenet_v1_1_224.py nets/resnet_v1.py heads/direct.py nets/__init__.py image_ex.py nets/mobilenet_v1.py top1.py triplet_loss.py nets/resnet_utils.py heads/__init__.py nets/resnet_v1_101.py nets/resnet_v1_50.py heads/direct_normalize.py _cmc_core cmc batch_all_triplet_loss _get_anchor_negative_triplet_mask batch_hard_triplet_loss _get_anchor_positive_triplet_mask _pairwise_distances _get_triplet_mask head head head head mobilenet_v1_arg_scope mobilenet_v1 _reduced_kernel_size_for_small_input mobilenet_v1_base wrapped_partial endpoints mobilenet_v1_arg_scope Block conv2d_same subsample resnet_arg_scope stack_blocks_dense resnet_v1_152 resnet_v1_101 bottleneck resnet_v1_200 resnet_v1_50 resnet_v1 resnet_v1_block endpoints endpoints print cumsum argsort shape sum asarray arange isinstance print size choice shape unique xrange zeros enumerate to_float transpose maximum matmul diag_part sqrt expand_dims equal logical_and logical_not expand_dims cast eye bool equal expand_dims equal logical_not bool logical_and logical_not cast eye expand_dims equal to_float multiply maximum greater reduce_sum _pairwise_distances expand_dims _get_triplet_mask to_float multiply reduce_max _get_anchor_negative_triplet_mask maximum reduce_mean _get_anchor_positive_triplet_mask _pairwise_distances reduce_min scalar fully_connected l2_normalize as_list partial update_wrapper as_list l2_regularizer truncated_normal_initializer reduce_mean divide pad constant | # part_aligned https://arxiv.org/abs/1707.07256 data folder is cuhk03 dataset https://drive.google.com/open?id=1Qgz7eXFTP5Hi7hO1ew7ZWFSkh_vdl57e resnet50 https://drive.google.com/open?id=14J1Qs8ApuJdhuFvhsf7ejijr86SLFr-p | 852 |
Ping-C/certifiedpatchdefense | ['adversarial attack'] | ['Local Gradients Smoothing: Defense against localized adversarial attacks'] | train.py attacks/pgd_attacker.py model_defs.py attacks/patch_attacker.py eval.py converter.py argparser.py unet.py config.py bound_layers.py datasets.py isfloat isint argparser BoundSequential ParallelBound BoundFlatten BoundReLU BoundConv2d ParallelBoundPool BoundLinear config_dataloader config_modelloader_and_convert2mlp get_file_close get_path config_modelloader update_dict load_config get_model_config main get_stats mnist_loaders svhn_loaders cifar_loaders main model_mlp_any save_checkpoint model_cnn_4layer_conv11 model_cnn_6layer model_cnn_10layer FeatureMask2D model_cnn_2layer Flatten model_cnn_4layer load_checkpoint_to_mlpany model_cnn_5layer model_cnn_3layer add_feature_subsample convert_conv2d_dense remove_feature_subsample model_mlp_uniform DenseConv2d model_cnn_1layer model_cnn_4layer_conv13 model_cnn_3layer_fixed main AverageMeter Logger Train convrelu conv1x1 ResNetUNet conv3x3 BasicBlock PatchAttacker PGDAttacker float int seed int isint isfloat overrides add_argument seterr literal_eval ArgumentParser manual_seed parse_args float gpu split glob sorted format print get items format isinstance print config str format deepcopy replace isinstance items print overrides_dict model_subset update_dict append range path_prefix get_file_close join makedirs add_feature_subsample load model_class isinstance print get_path keys import_module getattr load_state_dict append cuda add_feature_subsample load convert_conv2d_dense model_class format isinstance print get_path load_checkpoint_to_mlpany dense_m import_module getattr load_state_dict save_checkpoint append zeros keys load_config config_modelloader_and_convert2mlp view size sqrt dataset len list Subset DataLoader dataset range list Subset DataLoader Normalize CIFAR10 range get_stats list print Subset DataLoader Normalize SVHN numpy range config_dataloader Logger Train argmax max log cuda open config_modelloader argmin append update format get_path mean zip float deepcopy print min median array append Linear ReLU Sequential Conv2d ReLU Flatten Linear Sequential Conv2d ReLU Flatten Linear Sequential Conv2d ReLU Flatten Linear Sequential Conv2d ReLU Flatten Linear Sequential Conv2d ReLU Flatten Linear Sequential Conv2d ReLU Flatten Linear Sequential Conv2d ReLU Flatten Linear Sequential Conv2d ReLU Flatten Linear Sequential Conv2d ReLU Flatten Linear Sequential Conv2d ReLU Flatten Linear named_modules children list add_module __class__ FeatureMask2D children list children list DenseConv2d isinstance kernel_size out_channels in_channels Conv2d bias __class__ append weight children list isinstance dense_w bias dense_bias OrderedDict save weight Linear join format replace endswith print model_mlp_any shape load_state_dict append state_dict batch_size model perturb zero_grad randint where unsqueeze linspace interval_range_pool gather dataset max log is_cuda cuda view step scatter permute append range cat detach update format LongTensor size mean multinomial eval PatchAttacker interval_range enumerate time backward PGDAttacker adv_net AverageMeter min repeat_interleave reshape tqdm parameters grad_acc_steps repeat zeros train numpy std len SGD linspace save str list StepLR Adam device_count load_state_dict get_lr range inf resume load join time update_dict parameters grad_acc_steps step std | Certified Defenses for Adversarial Patches - ICLR 2020 ===================== This repository implements the _first_ certified defense method against adversarial patch attack. Our methodology extends Interval Bound Propagation ([IBP](https://arxiv.org/abs/1810.12715)) to defending against patch attack. The resulting model achieves certified accuracy that exceeds empirical robust accuracy of previous empirical defense methods, such as [Local Gradient Smoothing](https://arxiv.org/abs/1807.01216) or [Digital Watermarking](https://ieeexplore.ieee.org/document/8575371). More details of our methodology can be found in the paper below: [**Certified Defenses for Adversarial Patches**](https://openreview.net/forum?id=HyeaSkrYPH¬eId=HyeaSkrYPH) <br> _Ping-yeh Chiang*, Renkun Ni*, Ahmed Abdelkader, Chen Zhu, Christoph Studor, Tom Goldstein_<br> | 853 |
PingEnLu/Time-dependent_SIR_COVID-19 | ['time series'] | ['A Time-dependent SIR model for COVID-19 with Undetectable Infected Persons'] | TimeSIR_COVID-19.py data_spilt ridge empty range delete len print Ridge GridSearchCV fit | # A Time-dependent SIR model for COVID-19 with Undetectable Infected Persons This repository provides the codes for the paper "A Time-dependent SIR model for COVID-19 with Undetectable Infected Persons" by Ping-En Lu. We have uploaded the paper to arXiv and has been published. It is available at https://arxiv.org/abs/2003.00122. The Authors are Yi-Cheng Chen, Ping-En Lu, Cheng-Shang Chang, and Tzu-Hsuan Liu If there is an update for the paper, the latest version of the paper will be placed on this link: http://gibbs1.ee.nthu.edu.tw/A_TIME_DEPENDENT_SIR_MODEL_FOR_COVID_19.PDF ## Abstract of the paper In this paper, we conduct mathematical and numerical analyses to address the following important questions for COVID-19: (Q1) Is it possible to contain COVID-19? (Q2) If COVID-19 can be contained, when will be the peak of the epidemic, and when will it end? (Q3) How do the asymptomatic infections affect the spread of disease? (Q4) If COVID-19 cannot be contained, what is the ratio of the population that needs to be infected in order to achieve herd immunity? (Q5) How effective are the social distancing approaches? (Q6) If COVID-19 cannot be contained, what is the ratio of the population infected in the long run? For (Q1) and (Q2), we propose a time-dependent susceptible-infected-recovered (SIR) model that tracks two time series: (i) the transmission rate at time  and (ii) the recovering rate at time . Such an approach is not only more adaptive than traditional static SIR models, but also more robust than direct estimation methods. Using the data provided by the National Health Commission of the People's Republic of China (NHC) [1], we show that the one-day prediction errors for the numbers of confirmed cases are almost less than 3%. Also, the turning point, defined as the day that the transmission rate is less than the recovering rate, is predicted to be Feb. 17, 2020. After that day, the basic reproduction number, known as the  value at time , is less than 1. In that case, the total number of confirmed cases is predicted to be around 80,000 cases in China under our model. For (Q3), we extend our SIR model by considering two types of infected persons: detectable infected persons and undetectable infected persons. Whether there is an outbreak in such a model is characterized by the spectral radius of a  matrix that is closely related to the basic reproduction number . We plot the phase transition diagram of an outbreak and show that there are several countries, including South Korea, Italy, and Iran, that are on the verge of COVID-19 outbreaks on Mar. 2, 2020. For (Q4), we show that herd immunity can be achieved after at least  fraction of individuals being infected and recovered from COVID-19. For (Q5) and (Q6), we analyze the independent cascade (IC) model for disease propagation in a random network specified by a degree distribution. By relating the propagation probabilities in the IC model to the transmission rates and recovering rates in the SIR model, we show two approaches of social distancing that can lead to a reduction of . ## Usage ### Installation * Clone this repository. | 854 |
Pirazh/Vehicle_Key_Point_Orientation_Estimation | ['vehicle re identification'] | ['A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification'] | tools/confusion_meter.py tools/utilities.py tools/paths.py data/datatools/veri_dataset.py models/KP_Orientation_Net.py data/datatools/transforms.py tools/test.py main.py tools/train.py main Rotate ToTensor LRFlip Normalize Rescale VeriDataset FineRegressor KeyPointModel CoarseRegressor ConfusionMeter paths test train sample_visualizer calc_dists get_preds accuracy save_checkpoint Chronometer load KeyPointModel format resumed_ckpt filterwarnings print train CoarseRegressor test parameters DataParallel eval load_state_dict device to sum join format ckpt ConfusionMeter DataLoader save_confusion_matrix VeriDataset len ckpt elapsed DataLoader cuda Adam MSELoss VeriDataset CrossEntropyLoss format set start_epoch resume mkdir Chronometer load int resumed_ckpt print epochs copyfile save subplot sum xlabel pause draw imshow figure randint xticks numpy range view size floor float max size float range get_preds calc_dists | # Vehicle Key-Point & Orientation Estimation The repository contains the code for vehicle key-point and Orientation estimation Network proposed in the [A Dual Path Model With Adaptive Attention For Vehicle Re-Identification](http://openaccess.thecvf.com/content_ICCV_2019/papers/Khorramshahi_A_Dual-Path_Model_With_Adaptive_Attention_for_Vehicle_Re-Identification_ICCV_2019_paper.pdf) which has been accepted as an **oral presentation** in ICCV 2019. The code for re-identification network does not exist in the repository. The code for vehicle key-point and orientation estimation has been released to facilitate future research in vehicle alignment, 3d vehicle modeling and vehicle speed estimation. ## Vehicle Key-Point & Orientation Estimation Pipeline The figure below demonstrates the pipeline for prediction of 20 vehicle landmarks and classify vehicle's orientation into one of 8 classes all defined in [here](https://github.com/Zhongdao/VehicleReIDKeyPointData).  Key-point estimation is done in two stages; in stage 1 the model tries to come up with coarse estimation of key-points location and in stage 2 those coarse estimates are refined through an hourglass like structure and in a parallel branch the orientation of the vehicle is predicted as well. ## Getting Started Clone this repository with the following command: ``` | 855 |
PkuRainBow/OCNet | ['scene parsing', 'semantic segmentation'] | ['Interlaced Sparse Self-Attention for Semantic Segmentation', 'OCNet: Object Context Network for Scene Parsing'] | utils/utils.py inplace_abn_03/modules/build.py network/resnet101_baseline.py oc_module/pyramid_oc_block.py utils/criterion.py network/resnet101_asp_oc.py network/resnet101_pyramid_oc.py inplace_abn_03/modules/misc.py utils/parallel.py oc_module/base_oc_block.py utils/files.py train.py inplace_abn/__init__.py utils/resnet_block.py generate_submit.py inplace_abn_03/modules/dense.py dataset/cityscapes.py network/__init__.py inplace_abn_03/modules/__init__.py inplace_abn/bn.py inplace_abn_03/modules/residual.py config/__init__.py inplace_abn_03/modules/functions.py oc_module/asp_oc_block.py utils/operator.py utils/metric.py eval.py inplace_abn/functions.py inplace_abn_03/modules/bn.py network/resnet101_base_oc.py utils/loss.py dataset/__init__.py inplace_abn_03/modules/_ext/__init__.py predict_whole_img predict_sliding predict_whole_img_w_label id2trainId predict_multi_scale get_palette get_confusion_matrix main pad_image predict_whole_img predict_sliding id2trainId predict_multi_scale get_palette main pad_image main adjust_learning_rate lr_poly Parameters str2bool CitySegmentationTrainWpath CitySegmentationTrain CitySegmentationTest get_segmentation_dataset InPlaceABN InPlaceABNSync ABN _act_forward _count_samples _broadcast_shape InPlaceABNSync InPlaceABN _reduce _check _act_backward InPlaceABNSyncWrapper ABN InPlaceABNSync InPlaceABNWrapper InPlaceABN _pair DenseModule _act_forward _count_samples _broadcast_shape InPlaceABNSync _check_contiguous InPlaceABN _reduce _check _act_backward GlobalAvgPool2d IdentityResidualBlock _import_symbols ResNet get_resnet101_asp_oc_dsn ResNet get_resnet101_baseline ResNet get_resnet101_base_oc_dsn ResNet get_resnet101_pyramid_oc_dsn get_segmentation_model ASP_OC_Module _SelfAttentionBlock SelfAttentionBlock2D BaseOC_Module BaseOC_Context_Module PyramidSelfAttentionBlock2D _PyramidSelfAttentionBlock Pyramid_OC_Module CriterionDSN CriterionOhemDSN CriterionCrossEntropy CriterionOhemDSN_single download save_checkpoint mkdir check_sha1 CrossEntropy2d OhemCrossEntropy2d _pickle_method ConfusionMatrix Separable_transpose_convolution Separable_convolution CallbackContext allreduce AllReduce DataParallelModel _criterion_parallel_apply execute_replication_callbacks Reduce patch_replication_callback DataParallelCriterion conv3x3 Bottleneck outS decode_predictions reshape_predict_target inv_preprocess decode_labels _quick_countless down_sample_target_count _zero_corrected_countless down_sample_target pad int zoom print upsample transpose min shape cuda ceil zeros range max pad_image net zoom upsample transpose shape Upsample cuda net zoom upsample transpose shape Upsample cuda net predict_whole_img print predict_sliding copy shape zeros float flush bincount zeros astype range items list copy predict_whole_img set_fill_value diag masked_array DataParallel DataLoader get_segmentation_model save dataset argmax cuda fromarray get_segmentation_dataset list num_classes map load_state_dict restore_from predict_multi_scale putpalette sum range format parse asarray size output_path predict_whole_img_w_label mean eval use_flip flush enumerate load items join print Variable method maximum get_palette split zeros numpy gpu makedirs range Upsample ignore_label id2trainId num_steps power learning_rate lr_poly CriterionOhemDSN_single model DataParallelModel zero_grad fix_lr SGD adjust_learning_rate DataParallelCriterion ohem_single CriterionDSN seed CriterionCrossEntropy str default_timer state_dict num_steps SummaryWriter copy manual_seed float learning_rate criterion backward snapshot_dir CriterionOhemDSN add_scalar ohem train step fn append size enumerate size enumerate elu_forward slope leaky_relu_forward elu_backward slope leaky_relu_backward isinstance elu_cuda _check leaky_relu_cuda elu_inv_cuda leaky_relu_backward_cuda elu_backward_cuda _check leaky_relu_cuda dir _wrap_function getattr append callable ResNet ResNet ResNet ResNet copyfile save makedirs get join isdir print dirname abspath expanduser makedirs sha1 makedirs join isinstance _worker len start is_grad_enabled append range Lock list hasattr __data_parallel_replicate__ modules enumerate len replicate ceil int load new shape zeros numpy array range enumerate load isinstance concatenate new shape numpy append zeros argmax array range enumerate uint8 astype shape zeros numpy range size numpy view unique append ndindex append ndindex numpy _zero_corrected_countless | # OCNet: Object Context Network for Scene Parsing (pytorch) [](https://paperswithcode.com/sota/semantic-segmentation-on-coco-stuff-test?p=object-contextual-representations-for) [](https://paperswithcode.com/sota/semantic-segmentation-on-pascal-context?p=object-contextual-representations-for) [](https://paperswithcode.com/sota/semantic-segmentation-on-ade20k-val?p=object-contextual-representations-for) [](https://paperswithcode.com/sota/semantic-segmentation-on-lip-val?p=object-contextual-representations-for) [](https://paperswithcode.com/sota/semantic-segmentation-on-cityscapes?p=object-contextual-representations-for) <h2> ```diff | 856 |
PkuRainBow/OCNet.pytorch | ['scene parsing', 'semantic segmentation'] | ['Interlaced Sparse Self-Attention for Semantic Segmentation', 'OCNet: Object Context Network for Scene Parsing'] | utils/utils.py inplace_abn_03/modules/build.py network/resnet101_baseline.py oc_module/pyramid_oc_block.py utils/criterion.py network/resnet101_asp_oc.py network/resnet101_pyramid_oc.py inplace_abn_03/modules/misc.py utils/parallel.py oc_module/base_oc_block.py utils/files.py train.py inplace_abn/__init__.py utils/resnet_block.py generate_submit.py inplace_abn_03/modules/dense.py dataset/cityscapes.py network/__init__.py inplace_abn_03/modules/__init__.py inplace_abn/bn.py inplace_abn_03/modules/residual.py config/__init__.py inplace_abn_03/modules/functions.py oc_module/asp_oc_block.py utils/operator.py utils/metric.py eval.py inplace_abn/functions.py inplace_abn_03/modules/bn.py network/resnet101_base_oc.py utils/loss.py dataset/__init__.py inplace_abn_03/modules/_ext/__init__.py predict_whole_img predict_sliding predict_whole_img_w_label id2trainId predict_multi_scale get_palette get_confusion_matrix main pad_image predict_whole_img predict_sliding id2trainId predict_multi_scale get_palette main pad_image main adjust_learning_rate lr_poly Parameters str2bool CitySegmentationTrainWpath CitySegmentationTrain CitySegmentationTest get_segmentation_dataset InPlaceABN InPlaceABNSync ABN _act_forward _count_samples _broadcast_shape InPlaceABNSync InPlaceABN _reduce _check _act_backward InPlaceABNSyncWrapper ABN InPlaceABNSync InPlaceABNWrapper InPlaceABN _pair DenseModule _act_forward _count_samples _broadcast_shape InPlaceABNSync _check_contiguous InPlaceABN _reduce _check _act_backward GlobalAvgPool2d IdentityResidualBlock _import_symbols ResNet get_resnet101_asp_oc_dsn ResNet get_resnet101_baseline ResNet get_resnet101_base_oc_dsn ResNet get_resnet101_pyramid_oc_dsn get_segmentation_model ASP_OC_Module _SelfAttentionBlock SelfAttentionBlock2D BaseOC_Module BaseOC_Context_Module PyramidSelfAttentionBlock2D _PyramidSelfAttentionBlock Pyramid_OC_Module CriterionDSN CriterionOhemDSN CriterionCrossEntropy CriterionOhemDSN_single download save_checkpoint mkdir check_sha1 CrossEntropy2d OhemCrossEntropy2d _pickle_method ConfusionMatrix Separable_transpose_convolution Separable_convolution CallbackContext allreduce AllReduce DataParallelModel _criterion_parallel_apply execute_replication_callbacks Reduce patch_replication_callback DataParallelCriterion conv3x3 Bottleneck outS decode_predictions reshape_predict_target inv_preprocess decode_labels _quick_countless down_sample_target_count _zero_corrected_countless down_sample_target pad int zoom print upsample transpose min shape cuda ceil zeros range max pad_image net zoom upsample transpose shape Upsample cuda net zoom upsample transpose shape Upsample cuda net predict_whole_img print predict_sliding copy shape zeros float flush bincount zeros astype range items list copy predict_whole_img set_fill_value diag masked_array DataParallel DataLoader get_segmentation_model save dataset argmax cuda fromarray get_segmentation_dataset list num_classes map load_state_dict restore_from predict_multi_scale putpalette sum range format parse asarray size output_path predict_whole_img_w_label mean eval use_flip flush enumerate load items join print Variable method maximum get_palette split zeros numpy gpu makedirs range Upsample ignore_label id2trainId num_steps power learning_rate lr_poly CriterionOhemDSN_single model DataParallelModel zero_grad fix_lr SGD adjust_learning_rate DataParallelCriterion ohem_single CriterionDSN seed CriterionCrossEntropy str default_timer state_dict num_steps SummaryWriter copy manual_seed float learning_rate criterion backward snapshot_dir CriterionOhemDSN add_scalar ohem train step fn append size enumerate size enumerate elu_forward slope leaky_relu_forward elu_backward slope leaky_relu_backward isinstance elu_cuda _check leaky_relu_cuda elu_inv_cuda leaky_relu_backward_cuda elu_backward_cuda _check leaky_relu_cuda dir _wrap_function getattr append callable ResNet ResNet ResNet ResNet copyfile save makedirs get join isdir print dirname abspath expanduser makedirs sha1 makedirs join isinstance _worker len start is_grad_enabled append range Lock list hasattr __data_parallel_replicate__ modules enumerate len replicate ceil int load new shape zeros numpy array range enumerate load isinstance concatenate new shape numpy append zeros argmax array range enumerate uint8 astype shape zeros numpy range size numpy view unique append ndindex append ndindex numpy _zero_corrected_countless | # OCNet: Object Context Network for Scene Parsing (pytorch) [](https://paperswithcode.com/sota/semantic-segmentation-on-coco-stuff-test?p=object-contextual-representations-for) [](https://paperswithcode.com/sota/semantic-segmentation-on-pascal-context?p=object-contextual-representations-for) [](https://paperswithcode.com/sota/semantic-segmentation-on-ade20k-val?p=object-contextual-representations-for) [](https://paperswithcode.com/sota/semantic-segmentation-on-lip-val?p=object-contextual-representations-for) [](https://paperswithcode.com/sota/semantic-segmentation-on-cityscapes?p=object-contextual-representations-for) <h2> ```diff | 857 |
Plrbear/biomedical-image-segmentation | ['semantic segmentation'] | ['Multi-Level Contextual Network for Biomedical Image Segmentation'] | train.py utils/BilinearUpSampling.py dataLoader.py model.py dataloader SegModel train fscore resize_images_bilinear BilinearUpSampling2D join sorted basename imresize glob astype expand_dims sub normpath append imread array ad5 ad3 img_format model fscore print predict Adam ad1 chekp SegModel load_weights ModelCheckpoint ad2 dataloader compile fit ravel astype range len constant resize_bilinear astype image_data_format set_shape permute_dimensions int_shape | # Multi-Level Contextual Network for Biomedical Image Segmentation ## Overview Paper: https://arxiv.org/abs/1810.00327 ### Datasets [Download links](http://www.andrewjanowczyk.com/deep-learning/) * The tree of ```dataset``` dir must be like: ``` -dataset | -----masks | 858 |
PlusLabNLP/Deep-Structured-EveEveTemp | ['relation extraction'] | ['Deep Structured Neural Network for Event Temporal Relation Extraction'] | code/train_all.py code/featureFuncs.py code/dataloader.py code/local_train.py code/nn_model.py code/dataset.py code/temporal_evaluation.py code/collect_mcnermar.py code/relation_to_timegraph.py code/gurobi_inference.py code/base.py code/global_inference.py ClassificationReport EveEveRelModel collect_mcnermar get_data_loader _collate_fn EventDataset modal_features tense_features bert_features compute_ngbrs togpu_data pos_features token_idx distance_features glove_features create_pos_dict ner_features wordNet_features temporal_features togpu create_ner_dict read_glove polarity_features temporal_awareness NNClassifier main_global Evaluator Gurobi_Inference main_local NNClassifier Evaluator BiLSTM add_X_after_Y_metagraph add_relation_in_timegraph traverse_timegraph_identify_rel interval_rel_X_Y getdct add_relation_for_existing_entities_in_timegraph get_feature add_point_x_BEFORE_y search_x_in_y bar add_point_x_EQUAL_y find_point_rel Chain reverse_relation change_DURING_relation read_config add_point_x_DURING_y_z fine_relation_in_timegraph Timegraph add_point_x_AFTER_y Node get_entities_add_relation_in_timegraph read_tlinks traverse_from_x_to_y point_rel_x_y tempeval_to_timegraph_func extract_name create_timegraph_from_weight_sorted_relations evaluate_all get_arg evaluate_two_files_implicit_in_recall get_fscore input_and_evaluate total_implicit_matched get_timegraphs get_triples evaluate_two_files get_x_y_rel get_entity_rel get_directory_path total_relation_matched reverse_relation change_DURING_relation get_entities get_n get_ref_minus final_score get_common_n get_entity_val extract_name str2bool load items dict append open LongTensor pad_sequence size stack append OrderedDict open OrderedDict str rfind items items split len str list keys int compute_ngbrs from_iterable set intersection append synsets list num_ngbrs compute_ngbrs extend copy ngbr_emb keys zeros len mean eval numpy bert_model device append items print enumerate get_score time get_data_loader print data_dir glove2vocab train_epoch Evaluator EventDataset bert_fts len get_score get_data_loader print data_dir glove2vocab train_epoch Evaluator EventDataset bert_fts len print len split split read get_feature search extract_name split read strip str Chain chain next_chain add_X_after_Y_metagraph Node chain pseudo next_chain add_X_after_Y_metagraph Node chain pseudo Node next_chain add_X_after_Y_metagraph Node chain pseudo next_chain add_X_after_Y_metagraph interval_rel_X_Y str p add_point_x_BEFORE_y cp c chain reverse_relation add_point_x_DURING_y_z upper add_point_x_AFTER_y Node parent print sibling sub child split pseudo print put add_relation_in_timegraph split range len read split print cp split chain pseudo print traverse_timegraph_identify_rel print fine_relation_in_timegraph find_point_rel chain pseudo add_X_after_Y_metagraph print print add_relation_for_existing_entities_in_timegraph point_rel_x_y len range split violated_relations change_DURING_relation print nonredundant final_relations get_entities_add_relation_in_timegraph split remove_from_reduce range len get print search entries_added put get_entities_add_relation_in_timegraph entity_to_lines split sub extract_name sub search sub print reverse_relation split create_timegraph_from_weight_sorted_relations final_relations violated_relations Timegraph split split get_x_y_rel print get_entity_rel strip search interval_rel_X_Y split get_x_y_rel print search interval_rel_X_Y split split get_entities get_entities print search split len get_entities split total_relation_matched print get_common_n get_relations get_fscore total_implicit_matched final_relations get_timegraphs split get_triples len total_relation_matched print final_relations get_timegraphs split get_triples len print str round get_fscore get_directory_path evaluate_two_files_acl11 print get_arg evaluate_two_files_implicit_in_recall evaluate_two_folders final_score evaluate_two_files print final_score keys evaluate_two_files isinstance | # Basic Info Author: Rujun Han*, I-Hung Hsu*, Mu Yang Title: Codebase for CoNLL 2019 Paper: [Deep Structured Neural Network for Event Temporal Relation Extraction](https://arxiv.org/pdf/1909.10094.pdf) Data processinng. We have preprocessed MATRES(notice that the Matres dataset we use are their initial released version, hence, contains less data), TB-Dense and TCR raw data using internal NLP tools at the Information Sciences Institute. These .pickle files are saved in data fold. ## Additional Note: - If you are curious about the data preprocessing, we recommend you to see this | 859 |
Podidiving/lgsc-for-fas-pytorch | ['face anti spoofing', 'anomaly detection'] | ['Learning Generalized Spoof Cues for Face Anti-spoofing'] | src/models/decoder.py src/models/resnet.py src/infer.py src/models/scan.py src/train.py src/loss.py src/metrics.py src/pl_model.py src/utils.py src/datasets/datasets.py src/datasets/__init__.py parse_args prepare_infer_dataloader infer_model load_model_from_checkpoint TripletSemiHardLoss TripletLoss pairwise_distance_torch get_npcer get_metrics get_apcer get_threshold get_acer eval_from_scores LightningModel GridMaker get_test_augmentations get_train_augmentations Dataset Decoder conv1x1 conv3x3 ResNet18Classifier ResNet18Encoder BasicBlock make_layer SCAN infer_df get_test_augmentations DataLoader with_labels read_csv root Dataset image_size face_detector to eval load_from_checkpoint load add_argument configs ArgumentParser print eval_from_scores Tensor numpy array mul ones reshape transpose clone to sum max diag reshape transpose min logical_not where shape eq repeat pairwise_distance_torch to sum max get_npcer sum get_apcer get_acer append float range accuracy_score get_threshold conv1x1 norm_layer block Sequential append BatchNorm2d range expansion | # LGSC-for-FAS pytorch ## WORK IN PROGRESS This repository is reimplementation of ["LGSC-for-FAS"](https://github.com/VIS-VAR/LGSC-for-FAS) repository, which is implementation of ["Learning Generalized Spoof Cues for FaceAnti-spoofing (LGSC)"](https://arxiv.org/abs/2005.03922) Code of this repository uses ["pytorch"](https://github.com/pytorch/pytorch) and ["pytorch_lightning"](https://github.com/PyTorchLightning/pytorch-lightning) ### Train There are 2 ways: 1. Configure your own dataset, redefine `val_dataloader` & `train_dataloader` functions in `pl_model.py` 2. Specify in `configs/train_config.yml` next variables: `train_df` - path to csv with train info. Each object should have `target` | 860 |
Poseidon0711/Art-Generation-with-Neural-Style-Transfer | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | nst_utils.py generate_noise_image reshape_and_normalize_image save_image CONFIG load_vgg_model reshape _conv2d_relu Variable zeros _avgpool loadmat astype reshape MEANS shape MEANS imsave astype | # Art-Generation-with-Neural-Style-Transfer We will implement Neural Style Transfer. This algorithm was created by Gatys et al. (2015) (https://arxiv.org/abs/1508.06576). In this project, we will: 1.) Implement the neural style transfer algorithm 2.) Generate novel artistic images using your algorithm Most of the algorithms we've studied optimize a cost function to get a set of parameter values. In Neural Style Transfer, we'll optimize a cost function to get pixel values! | 861 |
PouyaREZ/AirBnbPricePrediction | ['sentiment analysis'] | ['Airbnb Price Prediction Using Machine Learning and Sentiment Analysis'] | Main/baselines.py Main/cv.py Main/sentiment_analysis.py Main/run_models.py Main/data_cleanup.py Main/Price_map_creator.py Main/feature_selection.py Main/data_preprocessing_reviews.py kmeans TreebasedModel get_gaussian_process_regressor print_evaluation_metrics2 simple_neural_network get_mlp_regressor print_evaluation_metrics get_ensemble_models LinearModel clean_host_since clean_number_removal clean_comments cleaned_state collect_amenities clean_response_rate reviews_per_month_cleanup clean_listings_count generic_amenities clean_number generic_verification collect_host_verifications clean_price create_state_set clean_superhost normalize split clean_pvals linear_model_SGD LinearModelLasso kmeans TreebasedModel get_gaussian_process_regressor svm print_evaluation_metrics2 LinearModelRidge simple_neural_network get_mlp_regressor print_evaluation_metrics get_ensemble_models LinearModel calculate_sentiment GaussianProcessRegressor MLPRegressor RandomForestRegressor BaggingRegressor ExtraTreesRegressor GradientBoostingRegressor AdaBoostRegressor print median_absolute_error xlabel ylabel mean_absolute_error title scatter savefig r2_score mean_squared_error predict print median_absolute_error xlabel ylabel mean_absolute_error title scatter savefig r2_score mean_squared_error predict print r2_score mean_squared_error predict fit print Sequential add print_evaluation_metrics2 Dense print_evaluation_metrics compile fit squeeze print_evaluation_metrics print_evaluation_metrics2 zip array get_ensemble_models fit LinearModel print cluster_centers_ mean std range predict fit add split split replace isnan isnan isnan isnan isnan isinstance add split split MinMaxScaler DataFrame astype fit_transform train_test_split isnan SVR print_evaluation_metrics2 ravel print_evaluation_metrics fit print_evaluation_metrics2 ravel print_evaluation_metrics Ridge coef_ print predict print_evaluation_metrics2 r2_score mean_squared_error print_evaluation_metrics values fit print_evaluation_metrics2 fit print_evaluation_metrics Lasso Adam deepcopy Ridge values median_absolute_error mean_absolute_error r2_score append mean_squared_error SGDRegressor print_evaluation_metrics2 ravel print_evaluation_metrics fit TextBlob | ########################################### Airbnb Price Prediction Using MachineLearning and Sentiment Analysis Authors: Pouya Rezazadeh Kalehbasti ([email protected]) Liubov Nikolenko ([email protected]) Hoormazd Rezaei ([email protected]) **Link to source paper for citation: https://arxiv.org/abs/1907.12665** ########################################### In order to run the code make sure you pre-instal all the dependecies such as TextBlob and sklearn | 862 |
Pranacahya/FP-GameCerdas | ['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/trainers/bc/online_trainer.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/trainers/tensorflow_to_barracuda.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/tests/trainers/test_learn.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 ml-agents/tests/trainers/test_demo_loader.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/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/mlagents/trainers/demo_loader.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/mlagents/envs/communicator_objects/demonstration_meta_proto_pb2.py ml-agents/tests/trainers/test_buffer.py ml-agents/mlagents/trainers/trainer.py ml-agents/mlagents/envs/communicator.py ml-agents/tests/envs/test_rpc_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/trainers/barracuda.py ml-agents/mlagents/envs/communicator_objects/command_proto_pb2.py ml-agents/mlagents/trainers/bc/offline_trainer.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 ml-agents/tests/trainers/test_barracuda_converter.py UnityGymException ActionFlattener UnityEnv create_mock_vector_braininfo test_gym_wrapper test_multi_agent test_branched_flatten setup_mock_unityenvironment create_mock_brainparams BrainInfo BrainParameters Communicator UnityEnvironment UnityWorkerInUseException UnityException UnityTimeOutException UnityEnvironmentException UnityActionException RpcCommunicator UnityToExternalServicerImplementation SocketCommunicator UnityToExternalServicer UnityToExternalStub add_UnityToExternalServicer_to_server BarracudaWriter compress Build sort lstm write fuse_batchnorm_weights trim gru Model summary Struct parse_args to_json rnn BufferException Buffer Curriculum make_demo_buffer load_demonstration demo_to_buffer CurriculumError MetaCurriculumError TrainerError run_training prepare_for_docker_run init_environment try_create_meta_curriculum main load_config MetaCurriculum LearningModel Policy UnityPolicyException get_layer_shape pool_to_HW flatten process_layer process_model basic_lstm get_attr ModelBuilderContext order_by get_epsilon get_tensor_dtype replace_strings_in_list get_tensor_dims by_op remove_duplicates_from_list by_name convert strides_to_HW get_tensor_data gru UnityTrainerException Trainer TrainerController BehavioralCloningModel OfflineBCTrainer OnlineBCTrainer BCPolicy BCTrainer PPOModel PPOPolicy PPOTrainer get_gae discount_rewards MockCommunicator test_initialization test_reset test_close test_step test_handles_bad_filename test_rpc_communicator_checks_port_on_create test_rpc_communicator_create_multiple_workers test_rpc_communicator_close test_barracuda_converter test_dc_bc_model test_cc_bc_model test_visual_cc_bc_model test_bc_policy_evaluate dummy_config test_visual_dc_bc_model assert_array test_buffer location default_reset_parameters test_init_curriculum_bad_curriculum_raises_error test_init_curriculum_happy_path test_increment_lesson test_get_config test_load_demo basic_options test_docker_target_path test_run_training test_init_meta_curriculum_happy_path test_increment_lessons_with_reward_buff_sizes default_reset_parameters MetaCurriculumTest test_increment_lessons measure_vals reward_buff_sizes test_set_all_curriculums_to_lesson_num test_get_config test_set_lesson_nums test_init_meta_curriculum_bad_curriculum_folder_raises_error more_reset_parameters test_rl_functions test_ppo_model_dc_vector_curio test_ppo_model_dc_vector_rnn test_ppo_model_cc_vector_rnn test_ppo_policy_evaluate test_ppo_model_cc_visual dummy_config test_ppo_model_dc_vector test_ppo_model_dc_visual test_ppo_model_cc_visual_curio test_ppo_model_dc_visual_curio test_ppo_model_cc_vector_curio test_ppo_model_cc_vector test_initialize_online_bc_trainer basic_trainer_controller assert_bc_trainer_constructed test_initialize_trainer_parameters_uses_defaults dummy_bad_config test_take_step_adds_experiences_to_trainer_and_trains test_initialize_trainer_parameters_override_defaults test_initialize_invalid_trainer_raises_exception test_start_learning_trains_until_max_steps_then_saves dummy_config dummy_offline_bc_config_with_override test_initialization_seed test_initialize_ppo_trainer test_start_learning_updates_meta_curriculum_lesson_number assert_ppo_trainer_constructed test_take_step_resets_env_on_global_done test_start_learning_trains_forever_if_no_train_model dummy_offline_bc_config trainer_controller_with_take_step_mocks trainer_controller_with_start_learning_mocks dummy_online_bc_config create_mock_vector_braininfo sample UnityEnv setup_mock_unityenvironment step create_mock_brainparams create_mock_vector_braininfo UnityEnv setup_mock_unityenvironment step create_mock_brainparams setup_mock_unityenvironment create_mock_vector_braininfo create_mock_brainparams UnityEnv Mock Mock array range method_handlers_generic_handler add_generic_rpc_handlers join isdir print replaceFilenameExtension add_argument exit verbose source_file ArgumentParser target_file sqrt topologicalSort list hasattr layers addEdge Graph print inputs set len list hasattr layers print filter match trim_model compile data layers print tensors float16 replace layers dumps data dtype layers isinstance print name tensors inputs outputs shape zip array_without_brackets to_json globals Build tanh mad tanh mul Build concat add sigmoid sub mad _ tanh mul Build concat add sigmoid mad Buffer reset_local_buffers number_visual_observations append_update_buffer append range enumerate make_demo_buffer load_demonstration number_steps read suffix BrainParametersProto from_agent_proto DemonstrationMetaProto ParseFromString AgentInfoProto append from_proto _DecodeVarint32 start_learning int str format external_brain_names TrainerController put init_environment try_create_meta_curriculum load_config MetaCurriculum keys _resetParameters chmod format basename isdir glob copyfile copytree prepare_for_docker_run replace int Process getLogger print run_training start Queue info append randint docopt range endswith len HasField hasattr get_attr tensor_shape ndarray isinstance shape int_val bool_val float_val ListFields name ndarray isinstance str tensor_content ndarray product isinstance get_tensor_dtype print get_tensor_dims unpack int_val bool_val array float_val enter append add set name find_tensor_by_name split name lstm find_tensor_by_name find_forget_bias split get_layer_shape id Struct tensor hasattr name patch_data input_shapes out_shapes input get_attr append replace_strings_in_list tensors astype op zip enumerate print float32 patch_data_fn model_tensors map_ignored_layer_to_its_input co_argcount len items get_tensors hasattr name print process_layer eval ModelBuilderContext layers verbose Struct process_model open compress node GraphDef Model dims_to_barracuda_shape insert get_tensor_dims inputs MessageToJson ParseFromString cleanup_layers read memories print sort write trim summary 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 close RpcCommunicator close RpcCommunicator close RpcCommunicator join remove _get_candidate_names convert _get_default_tempdir dirname abspath isfile next BCPolicy evaluate close reset MockCommunicator reset_default_graph UnityEnvironment reset_default_graph reset_default_graph reset_default_graph reset_default_graph flatten list range len get_batch Buffer assert_array append_update_buffer make_mini_batch append reset_agent array range Curriculum Curriculum Curriculum make_demo_buffer load_demonstration dirname abspath MagicMock basic_options MagicMock MetaCurriculum assert_has_calls MetaCurriculumTest increment_lessons assert_called_with MetaCurriculumTest increment_lessons assert_called_with assert_not_called MetaCurriculumTest set_all_curriculums_to_lesson_num MetaCurriculumTest dict update MetaCurriculumTest evaluate close reset MockCommunicator PPOPolicy reset_default_graph UnityEnvironment reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph reset_default_graph assert_array_almost_equal array discount_rewards dummy_offline_bc_config TrainerController assert_called_with BrainInfoMock basic_trainer_controller assert_bc_trainer_constructed dummy_offline_bc_config summaries_dir model_path keep_checkpoints BrainInfoMock basic_trainer_controller assert_bc_trainer_constructed summaries_dir model_path keep_checkpoints dummy_offline_bc_config_with_override BrainInfoMock basic_trainer_controller assert_bc_trainer_constructed summaries_dir model_path keep_checkpoints dummy_online_bc_config BrainInfoMock basic_trainer_controller assert_ppo_trainer_constructed summaries_dir dummy_config model_path keep_checkpoints initialize_trainers BrainInfoMock dummy_bad_config basic_trainer_controller MagicMock basic_trainer_controller start_learning assert_called_once MagicMock assert_not_called dummy_config trainer_controller_with_start_learning_mocks assert_called_once_with start_learning assert_called_once MagicMock dummy_config trainer_controller_with_start_learning_mocks assert_called_once_with start_learning MagicMock dummy_config trainer_controller_with_start_learning_mocks assert_called_once_with lesson MagicMock basic_trainer_controller take_step assert_called_once MagicMock trainer_controller_with_take_step_mocks assert_called_once MagicMock take_step assert_not_called trainer_controller_with_take_step_mocks assert_called_once_with | <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 | 863 |
Pratik08/vis-dss | ['video summarization', 'active learning'] | ['Vis-DSS: An Open-Source toolkit for Visual Data Selection and Summarization'] | src/utils/dlib/external/pybind11/tools/mkdoc.py cpplint.py src/utils/dlib/external/pybind11/tools/libsize.py ParseNolintSuppressions CheckVlogArguments IsDecltype CheckSectionSpacing PathSplitToList _ExternCInfo FindNextMultiLineCommentEnd ReplaceAll CheckForFunctionLengths _SetOutputFormat CheckCommaSpacing CheckPrintf _VerboseLevel CheckBraces RemoveMultiLineComments ResetNolintSuppressions CheckForNonStandardConstructs _SetVerboseLevel PrintUsage _IsType CheckIncludeLine CheckCasts _CppLintState _IsSourceExtension Search CheckInvalidIncrement ShouldCheckNamespaceIndentation RemoveMultiLineCommentsFromRange CheckRedundantVirtual CleansedLines CheckForBadCharacters UpdateIncludeState CheckEmptyBlockBody FindNextMultiLineCommentStart IsBlockInNameSpace Match ExpectingFunctionArgs _NamespaceInfo CheckMakePairUsesDeduction CheckCheck IsBlankLine _SetFilters FlagCxx11Features ProcessLine _FunctionState CheckPosixThreading GetLineWidth _AddFilters IsCppString GetHeaderGuardCPPVariable _IncludeState CheckSpacing CheckBracesSpacing _ClassInfo IsDerivedFunction CheckGlobalStatic CheckForCopyright IsErrorSuppressedByNolint ProcessFileData CheckForMultilineCommentsAndStrings CloseExpression _PreprocessorInfo CheckRedundantOverrideOrFinal NestingState IsHeaderExtension _OutputFormat CheckForIncludeWhatYouUse CheckForNamespaceIndentation CheckSpacingForFunctionCall IsForwardClassDeclaration CheckParenthesisSpacing FindEndOfExpressionInLine ProcessHppHeadersOption IsMacroDefinition ProcessGlobalSuppresions CheckOperatorSpacing _SetCountingStyle ProcessFile _IncludeError CleanseRawStrings CheckAltTokens CheckForNewlineAtEOF ParseArguments CheckForNonConstReference PrintCategories _Filters main _RestoreFilters FilesBelongToSameModule CheckCStyleCast FileInfo CheckTrailingSemicolon _Quiet CheckItemIndentationInNamespace _BlockInfo ProcessConfigOverrides CheckForHeaderGuard FlagCxx14Features ReverseCloseExpression CleanseComments _DropCommonSuffixes _ClassifyInclude CheckStyle CheckHeaderFileIncluded IsOutOfLineMethodDefinition FindStartOfExpressionInLine _ShouldPrintError _SetQuiet CheckComment Error GetIndentLevel IsInitializerList _GetTextInside CheckLanguage FindCheckMacro _BackupFilters GetPreviousNonBlankLine extract ExtractionThread d sanitize_name process_comment update split set error group Search add search clear compile compile compile SetOutputFormat SetCountingStyle SetFilters AddFilters BackupFilters RestoreFilters _Filters startswith IsErrorSuppressedByNolint _ShouldPrintError write IncrementErrorCount replace append Match group find startswith endswith range error FindNextMultiLineCommentEnd RemoveMultiLineCommentsFromRange FindNextMultiLineCommentStart rstrip find pop xrange append len FindEndOfExpressionInLine pop append len FindStartOfExpressionInLine min search I xrange len Match append reverse split replace FileInfo FixupPathFromRoot sub RepositoryName ParseNolintSuppressions lines_without_raw_strings error len Match startswith xrange GetHeaderGuardCPPVariable enumerate Search split FileInfo error include_list RepositoryName Search BaseName error enumerate error len error replace count error Search error Search error match Search InnermostClass replace error group escape bool Match Search split error group Search elided ShouldCheckNamespaceIndentation CheckItemIndentationInNamespace Check error lines Count End group Begin xrange NumLines Match Search error match group find elided rfind error len group lstrip CheckComment Match lines_without_raw_strings error group CloseExpression ReverseCloseExpression Match Search len error group Search error Search lines_without_raw_strings isinstance group escape match stack starting_linenum xrange Match Search len error min group xrange NumLines Search Match CloseExpression len Search ReverseCloseExpression error group starting_linenum Match range Search GetIndentLevel error end CloseExpression bool Match Search find ParseNolintSuppressions rfind error group ReverseCloseExpression Search Match raw_lines CloseExpression len join list error extend CloseExpression append Match Search find Match find elided error strip group FindCheckMacro FindEndOfExpressionInLine xrange Match CloseExpression error Match finditer normalize isinstance InnermostClass GetLineWidth CheckSpacingForFunctionCall CheckCheck CheckTrailingSemicolon error CheckCommaSpacing CheckAltTokens CheckBraces CheckSpacing CheckBracesSpacing CheckParenthesisSpacing CheckOperatorSpacing CheckSectionSpacing CheckEmptyBlockBody IsHeaderExtension GetHeaderGuardCPPVariable lines_without_raw_strings _DropCommonSuffixes RepositoryName match split CheckNextIncludeOrder CanonicalizeAlphabeticalOrder FileInfo error search group SetLastHeader _ClassifyInclude append Match FindHeader pop end search set itervalues append M rstrip replace error _GetTextInside CheckIncludeLine search group CheckCasts lstrip CheckGlobalStatic IsHeaderExtension startswith CheckPrintf Match ResetSection Search split error Match Search error group Search group xrange Match max CloseExpression len Match max xrange xrange Match group Search IsOutOfLineMethodDefinition IsInitializerList rfind error group ReverseCloseExpression lstrip IsDerivedFunction xrange findall Match max Search ReplaceAll CheckCStyleCast error group Match CloseExpression ExpectingFunctionArgs Search len error xrange Match max Search replace FileInfo endswith Search BaseName setdefault group search CleanseComments open FilesBelongToSameModule error search dict sub xrange NumLines FullName keys error search error min group xrange NumLines Search Match CloseExpression len error rfind IsMacroDefinition IsForwardClassDeclaration error Match CheckPosixThreading ParseNolintSuppressions CheckRedundantVirtual CheckVlogArguments CheckMakePairUsesDeduction CheckLanguage CheckInvalidIncrement InAsmBlock CheckForNonConstReference check_fn Update CheckRedundantOverrideOrFinal CheckForNonStandardConstructs CheckStyle raw_lines CheckForMultilineCommentsAndStrings CheckForNamespaceIndentation CheckForFunctionLengths error Match group Search error Match group NestingState IsHeaderExtension xrange CheckCompletedBlocks _IsSourceExtension CheckForIncludeWhatYouUse CheckHeaderFileIncluded CleansedLines CheckForBadCharacters ProcessGlobalSuppresions CheckForNewlineAtEOF FlagCxx11Features RemoveMultiLineComments ResetNolintSuppressions ProcessLine _FunctionState _IncludeState CheckForCopyright CheckForHeaderGuard NumLines join _AddFilters reversed abspath split Error rstrip _RestoreFilters endswith len write ProcessFileData error_count _BackupFilters append _SetVerboseLevel range split write exit join write exit _VerboseLevel int getopt _SetOutputFormat _Quiet set _SetVerboseLevel PrintCategories _SetFilters _SetQuiet ProcessHppHeadersOption _OutputFormat PrintUsage _SetCountingStyle split getreader ParseArguments ResetErrorCounts stderr exit verbose_level PrintErrorCounts StreamReaderWriter ProcessFile getwriter items join sub replace items rstrip replace endswith strip min lstrip split splitlines sub startswith TextWrapper fill float len d sanitize_name process_comment append get_children spelling | # Vis-DSS: An Open-Source toolkit for Visual Data Selection and Summarization Rishabh Iyer, Pratik Dubal, Kunal Dargan, Suraj Kothiwade, Rohan Mahadev, Vishal Kaushal, Vis-DSS: An Open-Source toolkit for Visual Data Selection and Summarization (https://arxiv.org/pdf/1809.08846.pdf) ## License Vis-DSS is Licensed under the GNU GENERAL PUBLIC LICENSE. See LICENSE for more details. Copyright (C) Rishabh Iyer, Pratik Dubal, Kunal Dargan, Suraj Kothiwade, Rohan Mahadev, Vishal Kaushal ## Features and Functionalities 1) Video Summarization - `SimpleVideoSummarizer` (using Color Histogram features) - `DeepSimVideoSummarizer` using Features from a Deep Model and Similarity based functions - `DeepCoverVideoSummarizer` using Features from a Deep Model and Coverage Based Functions | 864 |
PratirupG/Handwriting-Recognition | ['optical character recognition', 'scene text recognition'] | ['An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition'] | train.py dataloader.py CONFIG.py all_models.py MODEL_ARCHITECTURE config dataLoader TRAIN_MODEL save_weights abs CNN_BiLSTM pad_sequences append format ascii_letters digits compile enumerate int print CNN_BiGRU fit prepareData ModelCheckpoint to_json array len | # Handwriting-Recognition Handwriting-Recognition also known as offline Handwriting-Recognition the objective of this project is to extract all the words from a handwritten paragraph using Deep learing. The model I used is CNNN-LSTM/GRU with CTC loss function. Coming Soon (Word Segmentation and Line Segmentation with final inference model) The model architecture I have taken from this paper https://arxiv.org/pdf/1507.05717.pdf. Dataset Used:<br> IAM Dataset(http://www.fki.inf.unibe.ch/databases/iam-handwriting-database)</br> CVL Dataset(https://cvl.tuwien.ac.at/research/cvl-databases/an-off-line-database-for-writer-retrieval-writer-identification-and-word-spotting/) Prediction Results:<br><br>  | 865 |
Priesemann-Group/mrestimator | ['time series'] | ['MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity'] | mrestimator/simulate.py docs/conf.py mrestimator/test_suite/test_mr_estimator.py mrestimator/test_suite/test_coefficients.py examples/paper/coefficients.py mrestimator/input_output.py mrestimator/wrapper.py mrestimator/utility.py mrestimator/_version.py mrestimator/test_suite/__init__.py setup.py mrestimator/fit.py examples/paper/workflow.py examples/paper/experimental.py mrestimator/coefficients.py mrestimator/__init__.py examples/getting_started/example.py examples/getting_started/create_example_data.py bin_spike_times_unitless sum_2d_ax0 CoefficientResult sm_precompute ts_precompute sm_method_naive sum_2d sum_2d_ax1 sm_method coefficients sum_1d ts_method f_linear f_complex fitpars_check FitResult fit default_fitpars f_exponential_offset default_fitbnds f_exponential fitfunc_check input_handler OutputHandler overview simulate_subsampling simulate_branching CustomExceptionFormatter initialize math_from_doc set_targetdir set_logfile _at_index _c_fits_consistent _c_rk_smaller_one tqdm _printeger _intersecting_index _prerror _c_rk_greater_zero _set_permissions _exception_test _enable_detailed_logging full_analysis calc_corr_arr_stationary calc_corr_mat_separate test_similarity_abs calc_corr_arr_stationary_new TestCorrCoeff test_similarity TestCCKnownMean compare_mre_methods fitfunction_complex fitfunction_exp_with_offset TestMREstimator calc_popt ceil int floor zeros prange ftype prange ftype zeros prange zeros prange ones prange sum_2d_ax1 empty len zeros prange sum_1d len ones prange sum_2d_ax1 zeros empty len sum_2d_ax0 mean zeros var enumerate CoefficientResult arange sm_precompute warning seed str locals all shape _log_locals sm_method append exception range format debug choice mean sqrt nonzero info float ts_method int var ts_precompute reshape any zeros array len debug fitfunc_check parameters array len debug transpose array fitfunc_check int list format asfarray reshape fitfunc_check parameters exception len exception CoefficientResult arange warning nanpercentile description _prerror seed str locals exp all math_from_doc mre transpose dt stderrs numboot _log_locals tau taustderr sum exception range dtunit nanvar format asarray debug fitpars_check FitResult fitloop copy mean sqrt nan info full triallen default_fitbnds __name__ fitfunc_check mrestderr join int isinstance tqdm any _intersecting_index array steps ssres len warning vstack list ndarray all getcwd shape append expanduser exception update format asarray glob debug astype set stack info enumerate isinstance loadtxt reshape dict len texts subplots arange trialactivities trialvariances axis OutputHandler set_visible fits spines legendHandles math_from_doc set_title ones set_xlabel numtrials set_rasterization_zorder set_multialignment set_linewidth fitfunc legend append range dtunit get format tight_layout mean sqrt set_tick_params enumerate int var suptitle text subplots_adjust set_ylabel add_ts zeros fill_between seed int ones_like asarray format exception debug size _printeger fmax warning info zeros full range poisson seed asarray format debug exception int chmod str debug abspath expanduser format debug getuser abspath expanduser _set_permissions makedirs coefficients exception ttest_1samp coefficients exception ttest_1samp debug format tau index intersect1d size asarray full float ceil float replace __doc__ find __name__ len dict format addHandler debug baseFilename setLevel str debug close baseFilename level abspath getLevelName expanduser _set_permissions setLevel makedirs RotatingFileHandler setFormatter CustomExceptionFormatter set_targetdir addHandler StreamHandler captureWarnings info DEBUG _set_permissions setLevel INFO input_handler warning abspath _set_permissions setLevel RotatingFileHandler str locals addHandler _log_locals getLevelName expanduser append exception setFormatter format handlers debug close upper mean removeHandler float FileHandler CustomExceptionFormatter isinstance fit coefficients makedirs print format max fabs print format max fabs mean enumerate zeros_like len len mean zeros sum enumerate var len mean zeros enumerate setLevel print curve_fit fitfunc zip append sum array range print coefficients correlation_coefficients test_similarity | # Mr. Estimator [](https://pypi.python.org/pypi/mrestimator/) [](https://mrestimator.readthedocs.io/en/latest/) [](https://opensource.org/licenses/BSD-3-Clause) [](https://pypi.python.org/pypi/mrestimator/) Welcome to the Toolbox for the Multistep Regression Estimator ("Mister Estimator"). If you find bugs, encounter unexpected behaviour or want to comment, please let us know via mail or open an issue on github. Any input is greatly appreciated. - [Documentation](https://mrestimator.readthedocs.io/en/latest/) - [Getting Started](https://mrestimator.readthedocs.io/en/latest/doc/gettingstarted.html) - [Python Package index](https://pypi.org/project/mrestimator) | 866 |
PrincetonLIPS/reversible-inductive-construction | ['denoising'] | ['Discrete Object Generation with Reversible Inductive Construction'] | code/genric/laman/representation/_action.py code/genric/torch_ext/segment_pool.py code/lib/pybind11/tests/test_copy_move.py code/lib/pybind11/tests/test_docstring_options.py code/genric/molecule_models/export_embeddings.py code/genric/molecule_representation/_implementation_python.py code/genric/torch_ext/segment_ops.py code/setup.py code/genric/corruption_dataset/_laman_dataset.py code/genric/molecule_models/modules.py code/lib/pybind11/tests/test_operator_overloading.py code/tests/test_model/test_summary.py code/genric/Chem.py code/tests/test_torch_multi_logit.py code/genric/laman/_utils.py code/genric/torch_ext/segment_indirect.py code/lib/pybind11/tests/test_eval.py code/lib/pybind11/docs/benchmark.py code/lib/pybind11/tests/test_numpy_vectorize.py code/lib/pybind11/tests/test_stl.py code/genric/laman/summary.py code/tests/test_torch_segment_indirect.py code/lib/pybind11/tests/test_iostream.py code/genric/chemutils.py code/lib/pybind11/docs/conf.py code/lib/pybind11/tests/test_smart_ptr.py code/genric/laman/representation/_aggregation.py code/lib/pybind11/tests/test_multiple_inheritance.py code/genric/model/readout.py code/lib/pybind11/tests/test_embed/test_interpreter.py code/genric/laman/_data.py code/genric/corruption_dataset/_dataset.py code/genric/molecule_models/summary.py code/genric/laman/representation/_representation.py code/lib/pybind11/tests/test_pickling.py code/genric/deploy/embed_dataset.py code/lib/pybind11/tests/test_constants_and_functions.py code/lib/pybind11/tests/test_cmake_build/test.py code/tests/test_laman_edit.py code/genric/molecule_edit.py code/tests/test_molecule_representation_incidence.py code/genric/deploy/chain.py code/lib/pybind11/tests/test_modules.py code/genric/corruption_dataset/_path_dataset.py code/genric/corruption_dataset/__main__.py code/genric/model/__init__.py code/lib/pybind11/tests/test_class.py code/lib/pybind11/tests/test_eval_call.py code/genric/torch_ext/_repeat_interleave.py code/genric/model/summary.py code/genric/laman/representation/_data.py code/genric/model/classification.py code/genric/laman/deploy.py code/genric/laman/graph.py code/genric/molecule_representation/_representation.py code/genric/torch_ext/_util.py code/lib/pybind11/tests/test_opaque_types.py code/genric/molecule_models/_train_utils.py code/lib/pybind11/tests/test_kwargs_and_defaults.py code/tests/test_vocabulary.py code/genric/model/message_passing.py code/lib/pybind11/tests/test_call_policies.py code/genric/laman/data_gen.py code/lib/pybind11/tests/test_chrono.py code/genric/model/_autograd_range.py code/tests/test_torch_segment_pool.py code/tests/test_torch_extensions.py code/genric/torch_ext/index.py code/genric/laman/laman_edit.py code/lib/pybind11/tests/test_exceptions.py code/tests/test_torch_index.py code/lib/pybind11/tests/test_local_bindings.py code/tests/test_chem.py code/genric/molecule_models/action_representation.py code/lib/pybind11/tests/test_builtin_casters.py code/genric/laman/joint_network.py code/genric/molecule_models/_train_harness.py code/genric/torch_ext/__init__.py code/tests/test_molecule_representation.py code/genric/deploy/_data.py code/lib/pybind11/tests/test_enum.py code/lib/pybind11/tests/test_pytypes.py code/lib/pybind11/tests/test_buffers.py code/genric/corruption_dataset/__init__.py code/genric/torch_ext/multi_logit.py code/lib/pybind11/tests/test_callbacks.py code/tests/test_action_representation.py code/lib/pybind11/tests/test_sequences_and_iterators.py code/tests/test_laman_representation.py code/lib/pybind11/tools/mkdoc.py code/genric/action.py code/genric/molecule_representation/_aggregration.py code/lib/pybind11/tests/test_numpy_dtypes.py code/genric/corruption_dataset/_sample.py code/lib/pybind11/pybind11/__init__.py code/lib/pybind11/tests/test_factory_constructors.py code/genric/molecule_models/joint_network.py code/lib/pybind11/tests/test_numpy_array.py code/lib/pybind11/setup.py code/lib/pybind11/tests/test_eigen.py code/lib/pybind11/tests/test_stl_binders.py code/lib/pybind11/tests/test_virtual_functions.py code/lib/pybind11/pybind11/_version.py code/genric/vocabulary.py code/genric/molecule_models/train_joint.py code/lib/pybind11/pybind11/__main__.py code/genric/laman/decomp.py code/genric/corruption_dataset/_cached_dataset.py code/tests/test_molecule_edit.py code/genric/laman/train_joint.py code/lib/pybind11/tests/test_methods_and_attributes.py code/genric/molecule_models/_distributed_utils.py code/lib/pybind11/tools/libsize.py code/genric/model/nn.py code/genric/laman/representation/__init__.py code/genric/data_utils.py code/genric/molecule_representation/_types.py code/genric/molecule_representation/__init__.py code/genric/laman/action.py code/lib/pybind11/tests/conftest.py download_and_patch_rdkit CMakeExtension CMakeBuild Stop Delete ActionType Continue Switch InsertAtomFusion Insert InsertBondFusion Action get_mol_2D get_atom_leaves atom_equal decode_stereo copy_atom sanitize atom_pair_equal_bond get_leaves clean_sulfur_hs set_atommap ring_bond_equal get_clique_mol get_ring_leaves get_smiles_2D get_smiles get_mol MoleculeDataset get_vocab transfer_bonds insert_random_node compute_insert_bond enumerate_deletion_actions compute_deletion legal_at_bond comp_mols get_deletion_target bond_match atom_match insert_at_atom my_explicit_valence compute_action insert_at_bond get_insertion_target find_vocab_index my_implicit_valence compute_insert generate_random_bond_insert delete_random_leaf generate_random_atom_insert legal_at_atom compute_insert_atom Vocabulary BondTuple AtomTuple make_dataset _collate CachedCorruptionDataset _transform_serialize main CorruptionDataset BaseDataset SplitCorruptionDataset _open_maybe_compressed LamanCorruptionDataset PathCorruptionDataset PathToSingleDataset CachedPathCorruptionDataset _collate _transform_serialize main RoundRobinSampler PartitionDatasetSampler RepeatDataset main save_result GibbsSampler make_model embed_dataset_files _dataset_transform embed_dataset main SampleResult Delete Stop rev_HII Continue HI HII Insert Action rev_HI main _open_maybe_compressed RH generate_dataset get_DoD to_bipartite dod_vs_size_exp get_model_output run_single_transition get_initial_graph main run_chain _decode_action_try Graph LamanEmbeddingNetwork JointClassificationNetwork JointClassificationNetworkConfig readout_rev_h1_features minmax LamanGraphEmbedding LamanGraphReadout readout_h1_features readout_rev_h2_features readout_h2_features generate_random_rev_HII compute_rev_HII compute_rev_HI compute_HII renumber compute_delete get_new_node compute_action is_laman generate_random_HI compute_HI insert_random_node get_nodes_of_degree generate_random_rev_HI generate_random_HII delete_random_node compute_insert apply_random_corruptions MeanSummary LamanClassificationSummary LamanJointHarness _collate make_dataloader main _transform LamanSamplerConfig cast_numpy_rec encode_action get_action_offsets decode_action _make_action_scopes _combine_incidence_sparse _compute_scopes combine_graph_reps _offsets_from_counts LamanRep StructureRep LamanActionScopes ScopedIndex get_edge_incidence_list get_vertex_features get_vertex_incidence_list get_reverse_h1_location graph_to_rep get_reverse_h2_location get_edge_features get_edge_incidence_size minmax _get_graph_edges multi_classification_prediction multi_classification_coarse_to_fine_loss multi_classification_loss MultiClassificationNetwork MessageAggregationNetwork aggregate_by_incidence make_action_mpn GeneralizedEmbeddingNetwork aggregate_by_incidence_impl AvgAndMaxPool FunctionalModule Sequential PassthroughModule SumModule MoleculeEmbedding MoleculeEmbeddingNetwork AtomOutputNetwork MoleculeReadout ConditionalAccuracySummary ClassificationSummary autograd_range VocabInsertEncoder _CanonicalAtomInsertEncoder _integer_to_action_split_insert integer_to_insert_atom_location _integer_to_action_split_delete integer_to_insert_atom_vocab action_to_integer_split action_to_integer integer_to_action_split _action_to_integer_split_delete _action_to_integer_split_insert compute_action_lengths_split integer_to_action compute_canonical_atom_insert_locations compute_action_lengths log_model_embeddings make_atom_action_labels SplitClassificationNetwork JointClassificationNetwork JointClassificationNetworkConfiguration get_scopes_from_graph SingleDeviceDistributedParallel PartialLogitNetwork FullyConnectedUnit _print_summary JointModelSummary SplitModelSummary initialize_dataset initialize train_distributed train _load_dataset train_boostrap_distributed DistributedTrainingInfo main initialize_distributed _transform get_distributed_config train_distributed train_boostrap_distributed DistributedTrainingInfo initialize_distributed get_distributed_config model_to_tensorboard_histogram _log_if_rank_zero LogLossTimeHook _aggregate_distributed_batchlike PrintAccuracyHook TrainingHarness LogOptimizerHook compute_and_aggregate_predictions LogModelWeightsHook init_optimizer replace_sparse_tensor load_path_dataset is_distributed load_cuda_async is_leader get_save_dir get_learning_rate_decay parse_arguments cast_numpy_to_torch _make_sparse_or_none collate _combine_ring_leaf _combine_bond_incidence _combine_ring_bond_idx _combine_incidence_sparse _combine_atom_leaf _combine_atom_incidence dict_to_graph_data _compute_scopes _cast_tensors combine_mol_graph _combine_feature_if_exists _offsets_from_counts _feature_exists fill_bond_features fill_atom_bond_list_segment bond_features atom_features fill_atom_bond_list_sparse fill_bond_incidence_list fill_atom_bond_list get_edge_incidence_size fill_bond_incidence_list_sparse fill_bond_incidence_list_segment fill_atom_features onek_encoding_unk _normalize_adjacency_values atom_leaves_index ring_leaves_index atom_bond_list mol2graph_single bond_incidence_list ring_info atom_bond_list_segment bond_incidence_list_segment LeafInfo AtomInfo BondInfo GraphInfo ScopedTuple RingInfo segment_cartesian_product_loop segment_triu_indices_loop segment_multi_softmax_cross_entropy_loop normalize_values_scopes select_label_multi_segment_python segment_multi_softmax_cross_entropy segment_multi_argmax _segment_across_offsets select_label_multi_segment_loop segment_multi_softmax_coarse_fine segment_multi_argmax_loop segment_index_add_python segment_argmax_native segment_argmax_backward segment_op_python _min_value SegmentArgmaxNative SegmentArgmaxPython segment_logsumexp_python segment_logsumexp_native SegmentLogsumexpNative segment_argmax_python segment_logsumexp_backward_python segment_argmax_loop SegmentLogsumexpPython SegmentMaxPool1DLoop segment_max_pool1d_native segment_max_pool1d_backward SegmentAvgPool1DNative SegmentAvgPool1DLoop segment_avg_pool1d_backward segment_avg_pool1d_loop segment_avg_pool1d_native segment_max_pool1d_loop SegmentMaxPool1DNative _ensure_repeats repeat_interleave repeat_interleave_out_python repeat_interleave_out_native use_native_extension InstallHeaders generate_dummy_code_pybind11 generate_dummy_code_boost generate_doxygen_xml setup get_include main print_includes suppress _strip_and_dedent Output doc _sanitize_general pytest_namespace _test_import_pybind11 gc_collect _make_explanation Capture msg _sanitize_docstring _split_and_sort _sanitize_message pytest_assertrepr_compare SanitizedString Unordered capture test_pointer_to_member_fn test_from_python test_inherited_protocol test_to_python test_bool_caster test_bytes_to_string test_reference_wrapper test_complex_cast test_builtins_cast_return_none test_single_char_arguments test_numpy_bool test_void_caster test_unicode_conversion test_simple_string test_tuple test_string_view test_integer_casting test_none_deferred test_callbacks test_keyword_args_and_generalized_unpacking test_movable_object test_function_signatures test_bound_method_callback test_cpp_function_roundtrip test_lambda_closure_cleanup test_call_guard test_keep_alive_return_value test_keep_alive_argument test_keep_alive_constructor test_return_none test_alive_gc_multi_derived test_alive_gc test_alive_gc_derived test_keep_alive_single test_chrono_duration_roundtrip test_chrono_system_clock test_chrono_steady_clock_roundtrip test_chrono_duration_subtraction_equivalence test_floating_point_duration test_chrono_system_clock_roundtrip test_chrono_steady_clock test_repr test_bind_protected_functions test_implicit_conversion_life_support test_mismatched_holder test_operator_new_delete test_isinstance test_instance test_docstrings test_override_static test_brace_initialization test_inheritance test_reentrant_implicit_conversion_failure test_class_refcount test_qualname test_automatic_upcasting test_constants test_exception_specifiers test_bytes test_function_overloading test_private_op_new test_move_fallback test_lacking_copy_ctor test_move_and_copy_load_optional test_lacking_move_ctor test_move_and_copy_casts test_move_and_copy_loads test_docstring_options test_eigen_ref_life_support test_dense_signature test_dense test_issue738 test_eigen_ref_mutators test_numpy_ref_mutators test_eigen_return_references assign_both test_nocopy_wrapper test_partially_fixed test_named_arguments array_copy_but_one test_eigen_ref_to_python test_custom_operator_new test_issue1105 test_fixed test_both_ref_mutators test_cpp_casting test_special_matrix_objects assert_equal_ref assert_sparse_equal_ref test_negative_stride_from_python assert_keeps_alive test_eigen_keepalive test_nonunit_stride_from_python test_sparse test_pass_readonly_array test_nonunit_stride_to_python test_sparse_signature test_mutator_descriptors test_scoped_enum test_binary_operators test_unscoped_enum test_implicit_conversion test_enum_to_int test_evals test_python_call_in_catch test_cross_module_exceptions test_nested_throws test_error_already_set test_exception_matches test_custom test_std_exception test_reallocations test_invalid_self test_init_factory_dual test_init_factory_casting test_init_factory_alias test_init_factory_signature strip_comments test_init_factory_basic create_and_destroy test_multiple_inheritance test_no_placement_new test_err test_dual test_redirect_err test_guard_capture test_multi_captured test_series_captured test_captured_large_string test_redirect test_flush test_redirect_both test_captured test_not_captured test_args_refcount test_named_arguments test_function_signatures test_arg_and_kwargs test_mixed_args_and_kwargs test_stl_bind_local test_stl_bind_global test_load_external test_nonlocal_failure test_local_bindings test_cross_module_calls test_duplicate_local test_mixed_local_global test_internal_locals_differ test_stl_caster_vs_stl_bind test_custom_caster_destruction test_properties test_static_properties test_static_cls test_metaclass_override test_accepts_none test_property_return_value_policies test_dynamic_attributes test_no_mixed_overloads test_cyclic_gc test_methods_and_attributes test_str_issue test_copy_method test_unregistered_base_implementations test_noconvert_args test_bad_arg_default test_property_rvalue_policy test_duplicate_registration test_reference_internal test_pydoc test_nested_modules test_importing test_multiple_inheritance_python test_mi_dynamic_attributes test_multiple_inheritance_mix1 test_mi_base_return test_multiple_inheritance_python_many_bases test_multiple_inheritance_cpp test_multiple_inheritance_virtbase test_mi_static_properties test_mi_unaligned_base test_multiple_inheritance_mix2 test_diamond_inheritance test_make_c_f_array test_make_empty_shaped_array test_array_resize test_array_create_and_resize test_array_attributes test_cast_numpy_int64_to_uint64 test_at test_wrap test_mutate_data arr test_at_fail test_numpy_view test_initializer_list test_overload_resolution test_greedy_string_overload test_index_offset test_bounds_check test_constructors test_array_unchecked_dyn_dims test_array_unchecked_fixed_dims test_dim_check_fail test_isinstance test_data test_mutate_readonly test_array_failure partial_dtype_fmt test_dtype test_scalar_conversion test_enum_array test_string_array partial_nested_fmt packed_dtype_fmt simple_dtype_fmt partial_ld_offset test_register_dtype packed_dtype assert_equal test_complex_array test_array_array dt_fmt test_array_constructors test_signature test_format_descriptors simple_dtype test_recarray test_compare_buffer_info test_vectorize test_type_selection test_passthrough_arguments test_method_vectorization test_array_collapse test_trivial_broadcasting test_docs test_pointers test_string_list test_operator_overloading test_nested test_operators_notimplemented test_roundtrip_with_dict test_roundtrip test_hash test_constructors test_set test_list test_bytes test_str test_implicit_casting test_print test_capsule test_accessors test_dict test_iterator_rvp test_sequence test_map_iterator allclose test_python_iterator_in_cpp test_iterator_passthrough isclose test_generalized_iterators test_shared_ptr_and_references test_smart_ptr test_shared_ptr_gc test_move_only_holder test_move_only_holder_with_addressof_operator test_unique_nodelete test_smart_ptr_from_default test_large_holder test_shared_ptr_from_this_and_references test_holder_with_addressof_operator test_smart_ptr_refcounting test_variant test_stl_ownership test_valarray test_recursive_casting test_vec_of_reference_wrapper test_missing_header_message test_set test_exp_optional test_stl_pass_by_pointer test_array test_vector test_map test_move_out_container test_optional test_map_string_double test_vector_int test_vector_custom test_vector_bool test_noncopyable_containers test_vector_buffer_numpy test_map_delitem test_vector_buffer test_map_string_double_const test_override test_alias_delay_initialization2 test_inherited_virtuals test_dispatch_issue test_override_ref test_move_support test_issue_1454 test_alias_delay_initialization1 DerivedWidget extract ExtractionThread d sanitize_name process_comment test_action_to_integer_roundtrip_insert_atom roundtrip_action test_action_to_integer_roundtrip_delete test_action_canonical_actions action_mol_to_integer test_action_canonical_actions_nitrogen test_action_canonical_roundtrip test_smiles_bond_properties test_smiles_pickle test_get_leaves test_smiles_roundtrip test_smiles_atom_features test_deterministic_generation test_renumber test_is_laman test_generate_dataset test_laman_after_corruption test_inverses test_deterministic_corruption test_repeat_action test_single_representation test_edge_incidence_list test_aggregation _get_data test_delete_inverse test_delete_inverse_bond test_insert_bond_inverse test_multi_delete_deterministic_and_inverses test_multi_insert_deterministic_and_inverses test_deterministic_deletion test_atom_insert_deterministic test_bond_fusion_deterministic test_delete_inverse_kekulize test_insert_atom_inverse test_deletion test_atom_incidence_sparse test_combine_graphs_leaf_rings_singleton_sequence test_bond_embedding test_mol2graph_single_rings get_data test_combine_graphs_bond_rings test_mol2graph_single_rings_leaves test_fill_bond_features test_fill_atom_features test_bond_incidence_sparse test_atom_bond_list_segment test_molecule_representation_stereo test_atom_embedding test_mol2graph_single test_combine_graphs test_bond_incidence_segment_reference test_atom_bond_incidence_segment_reference mol_incidence_reference test_segment_logsumexp_python test_repeat_out_python test_segment_logsumexp_python_grad test_segment_argmax test_segment_argmax_backward test_segment_logsumexp_native test_segment_logsumexp_native_grad test_repeat_python _scopes_from_lengths test_segment_cartesian_product _scopes_from_lengths test_multi_logit_argmax _make_data test_select_label_multi_segmented_python test_multi_logit_softmax_cross_entropy test_multi_logit_softmax_cross_entropy_grad test_segment_index_add _scopes_from_lengths _scopes_from_lengths test_segment_max_pool test_segment_max_pool_grad test_segment_avg_pool_grad test_segment_avg_pool test_vocabulary_legal_at_bond test_vocabulary_legal_at_atom test_summary_marginal_labels_correct test_summary_conditional_accuracy test_summary_kappa_correct test_summary_accuracy_correct test_summary_accuracy_correct_multi print join call check_call SetAtomMapNum GetAtoms Kekulize MolFromSmiles Kekulize MolToSmiles MolFromSmiles MolFromSmiles list CHI_UNSPECIFIED EnumerateStereoisomers MolToSmiles SetChiralTag append GetMol isinstance copy_edit_mol get_mol get_smiles RWMol SetAtomMapNum SetFormalCharge GetFormalCharge Atom GetSymbol GetAtomMapNum SetNumExplicitHs GetAtoms GetMol MolFromSmiles sanitize MolFragmentToSmiles GetIdx tuple extend set append GetBonds union range len get_atom_leaves get_ring_leaves append get_smiles SanitizeMol GetBonds int GetBondTypeAsDouble append GetImplicitValence my_implicit_valence append debug get_mol comp_mols enumerate GetIdx InsertBondFusion get_vocab GetBonds GetAtomWithIdx GetAtoms InsertAtomFusion legal_at_bond compute_insert_bond get_clique_mol find_vocab_index legal_at_atom get_smiles comp_mols compute_insert_atom get_leaves list remove RemoveAtom GetBonds GetAtomWithIdx SetIsAromatic sort sanitize copy get_leaves GetOtherAtomIdx leaf_idx get_insertion_target append GetIsAromatic RWMol get_leaves compute_deletion enumerate_deletion_actions choice Delete isinstance get_leaves compute_deletion GetNumAtoms comp_mols enumerate atom_idx GetIdx RemoveAtom get_deletion_target transfer_bonds GetNumAtoms GetAtomWithIdx CombineMols sanitize get_mol vocab_atom_idx RWMol int choice find_vocab_index legal_at_atom get_smiles generate_random_atom_insert compute_insert_atom GetIdx GetBeginAtomIdx GetBondType AddBond GetBonds GetEndAtomIdx compute_bond_in_order int RWMol GetBondWithIdx GetNumBonds CombineMols GetBondTypeAsDouble GetOtherAtom choice get_mol legal_at_bond int GetIdx RemoveAtom GetBondWithIdx GetNumBonds bond_idx transfer_bonds CombineMols vocab_bond_idx GetBondTypeAsDouble RemoveBond sanitize get_mol get_deletion_target RWMol compute_insert_bond generate_random_bond_insert InsertAtomFusion isinstance InsertBondFusion compute_insert range generate_random_insert Stop isinstance Insert frombuffer HIGHEST_PROTOCOL dumps to_array make_dataset DataLoader ArgumentParser savez_compressed getmembers make_postfix from_iterable getattr set_postfix append parse_args epoch concatenate stack enumerate add_argument output set_epoch tqdm array endswith stack concatenate limit_elements islice empty PathCorruptionDataset path len get join format print _replace dirname abspath range makedirs CRITICAL GibbsSampler device expected_corruption_steps setLevel logger save_result load_state_dict to run_chain JointClassificationNetwork VocabInsertEncoder eval model_path vars JointClassificationNetworkConfiguration load num_insert_bond_locations get_num_atom_insert_locations concatenate load_cuda_async tqdm append numpy VocabInsertEncoder JointVaeNetwork MoleculeRecognitionNetworkConfig JointClassificationNetworkConfiguration num_insert_bond_locations get_num_atom_insert_locations load BaseDataset make_model savez embed_dataset eval DataLoader load_state_dict cuda embed_dataset_files data_path output_path add_edge compute_HII Graph generate_random_HI compute_HI uniform generate_random_HII range size_dist p_dist append trange RH seed RandomState random generate_dataset num_samples add_edge Graph tuple extend choice add_nodes_from edges enumerate add_node add_edge list maximum_matching DiGraph strongly_connected_components set difference to_bipartite edges keys len show plot print xlabel pause ylabel savefig legend append RH range cast_numpy_rec combine_graph_reps multi_classification_prediction model Stop get_model_output isinstance compute_action write random append expected_corruption_steps max_denoising_steps range apply_random_corruptions num_steps RandomState run_single_transition set_grad_enabled append trange JointClassificationNetworkConfig use_revisit initial_data_path num_transitions get_initial_graph LamanSamplerConfig mean view append degree rev_HI deepcopy add_edge get_new_node deepcopy add_edge rev_HII get_new_node edge remove_edge HI HII Delete nodes choice range nodes choice deepcopy list add_edge remove_node nodes enumerate deepcopy list renumber HI node remove_node deepcopy list add_edge renumber neighbors_to_connect node remove_node HII range choice get_nodes_of_degree deepcopy list add_edge is_laman remove_node choice get_nodes_of_degree append range remove_edge len rev_HI rev_HII isinstance generate_random_rev_HII compute_delete generate_random_delete generate_random_rev_HI get_nodes_of_degree range insert_random_node geometric delete_random_node lattice edges action_type encode_action int graph_to_rep stack cast_numpy_rec combine_graph_reps LongTensor save cuda LamanClassificationSummary init_optimizer range state_dict get format LamanJointHarness get_save_dir LamanCorruptionDataset join extend train_epoch make_dataloader step ndarray hasattr Number isinstance Iterable Mapping number_of_nodes Stop rev_HII number_of_nodes get_reverse_h1_location isinstance sorted HI node neighbors_to_connect edge degree neighbors HII searchsorted minmax _get_graph_edges array rev_HI sorted get_action_offsets number_of_nodes get_reverse_h1_location neighbors degree number_of_edges _get_graph_edges array zip append zeros expand_dims array concatenate concatenate _combine_incidence_sparse _compute_scopes array _offsets_from_counts OrderedDict items get_edge_incidence_size sqrt number_of_edges _get_graph_edges _fill_bond_incidence empty int neighbors nodes sqrt minmax number_of_edges _get_graph_edges empty enumerate expand_dims max arange pi minmax empty edges enumerate array degree neighbors degree empty array enumerate get_vertex_features get_edge_incidence_list get_vertex_incidence_list get_reverse_h1_location get_reverse_h2_location get_edge_features _get_graph_edges segment_multi_softmax_cross_entropy segment_multi_softmax_coarse_fine int exp multinomial segment_multi_argmax append range cat list new_zeros index_select unsqueeze mul_ index_add_ Tensor Sequence isinstance _pop_range _push_range get get_smiles empty GetNumAtoms range get_leaves array len get_leaves len Stop zeros_like cumsum vocab_bond_idx vocab_atom_idx atom_idx len searchsorted InsertBondFusion vocab_idx Delete bond_idx leaf_idx get_insert_bond_index get_insert_atom_index isinstance InsertAtomFusion array num_insert_bond_locations get_num_atom_insert_locations atom_idx Stop bond_idx isinstance zeros_like cumsum vocab_bond_idx vocab_idx searchsorted get_num_atom_insert_locations InsertAtomFusion vocab_atom_idx get_insert_bond_index InsertBondFusion get_insert_atom_index array num_insert_bond_locations len Delete isinstance Switch leaf_idx array get_insert_bond_location divmod get_insert_atom_location num_insert_bond_locations get_num_atom_insert_locations get_insert_bond_location divmod get_insert_atom_location num_insert_bond_locations get_num_atom_insert_locations get_insert_atom_location get_num_atom_insert_locations arange concatenate _atom_offsets len repeat canonical _atom_canonical_offsets diff make_atom_action_labels add_embedding items format print tolist conditional_accuracy StringIO marginal_statistics mol2graph_single sorted format print PathToSingleDataset CachedPathCorruptionDataset CachedCorruptionDataset CorruptionDataset listdir get VocabInsertEncoder partial Vocabulary DistributedSampler RandomSampler _load_dataset DataLoader world_size cuda load_state_dict world_size JointClassificationNetwork get SummaryWriter format disable_log get_save_dir eval JointClassificationNetworkConfiguration SingleDeviceDistributedParallel load join print train local_rank num_insert_bond_locations get_num_atom_insert_locations get spawn set_device local_rank init_process_group get initialize_distributed train get_distributed_config get initialize_dataset initialize hasattr TrainingHarness save_model_fn extend train_epoch is_leader set_epoch init_optimizer step range JointModelSummary train train_boostrap_distributed parse_arguments partial print list chunk get_world_size new_empty all_gather append _aggregate_distributed_batchlike wait add_histogram replace named_parameters hasattr partial isinstance Sequence Tensor Mapping from_numpy long items tensor stack combine_mol_graph get join abspath range makedirs get parameters Adamax LambdaLR PathCorruptionDataset sorted format print CachedPathCorruptionDataset listdir parse_args set_defaults add_argument ArgumentParser append OrderedDict isinstance partial concatenate OrderedDict dict_to_graph_data _compute_scopes _cast_tensors append _combine_feature_if_exists array _offsets_from_counts _feature_exists get LeafInfo AtomInfo BondInfo ScopedTuple RingInfo onek_encoding_unk int GetStereo GetBondType GetAtoms enumerate atom_features GetBeginAtom GetBonds bond_features atom_features GetEndAtom enumerate GetIdx int GetAtoms GetBonds enumerate GetBeginAtom GetBonds GetEndAtom _fill_bond_incidence enumerate GetIdx int GetAtoms sqrt GetBonds enumerate len GetIdx int len GetAtoms GetBonds enumerate GetDegree GetBeginAtom GetBonds sqrt GetEndAtom _fill_bond_incidence enumerate GetBeginAtom GetBonds GetEndAtom _fill_bond_incidence enumerate append int GetBonds enumerate fill_atom_bond_list_sparse empty GetNumBonds _normalize_adjacency_values fill_atom_bond_list_segment empty GetNumBonds get_edge_incidence_size empty _normalize_adjacency_values fill_bond_incidence_list_sparse get_edge_incidence_size empty fill_bond_incidence_list_segment reciprocal arange concatenate astype float32 sqrt stack repeat get_ring_leaves array len fill_bond_features GetNumBonds atom_leaves_index ring_leaves_index atom_bond_list bond_incidence_list fill_atom_features zeros ring_info GetNumAtoms append len cat expand flatten zip append cat dtype view iter device next prod range len unsqueeze cat empty long range cross_entropy empty range cat cumsum zeros_like min add_ select new_tensor stack clamp_ unsqueeze _segment_across_offsets index_select append sum long enumerate stack select_label_multi_segment logsumexp squeeze select_label_multi_segment logsumexp mean stack append max range cat add_ select segment_argmax stack _segment_across_offsets zip append max list repeat_interleave new_zeros index_select index_add_ long narrow empty op range is_floating_point dtype _min_value new_empty range max narrow repeat_interleave long exp_ unsqueeze sparse_coo_tensor to_dense list unsqueeze new_empty select scatter_ unsqueeze new_zeros _ensure_repeats flatten repeat_interleave _ensure_repeats print_message int get print randint range randint range join format write call mkdir confdir connect parse_config_files finalize_options Distribution get_command_obj append join print includes print_help print_includes strip sub replace _sanitize_general __doc__ str sub _sanitize_general hasattr collect skipif get rows Matrix astype cols float32 range get gc_collect Matrix array SquareMatrix cls hasattr refwrap_list A cant_convert B require_implicit object test_callback4 test_callback5 CppBoundMethodTest MyClass payload_cstats test_cleanup detail_reg_inst detail_reg_inst add_patient refcount Child addChildKeepAlive append ParentGC detail_reg_inst Derived Child addChildKeepAlive append detail_reg_inst Derived Child addChildKeepAlive append detail_reg_inst detail_reg_inst detail_reg_inst hasattr abs today test_chrono1 abs today test_chrono2 test_chrono3 today today test_chrono4 test_chrono5 timedelta test_chrono6 test_chrono7 test_chrono_float_diff get NoConstructor new_instance Pet Hamster Rabbit Dog make make2 str size_alias size_noalias ProtectedA C ProtectedB NoBraceInitialization BraceInitialization getrefcount gc_collect C move_and_copy_cstats move_and_copy_cstats move_and_copy_cstats get_moveissue1 get_moveissue2 assert_array_equal assert_equal_ref toarray fixed_copy_r assert_equal_ref fixed_r fixed_copy_c fixed_c dense_copy_r assert_equal_ref dense_copy_c dense_r dense_c partial_copy_four_cm_c partial_copy_four_cm_r partial_copy_four_rm_r assert_array_equal array partial_copy_four_rm_c fixed_mutator_c reshape transpose fixed_mutator_r fixed_mutator_a array fixed_copy_r fixed_r fixed_r_const assert_array_equal full double_mat_cm double_threer double_row double_complex reshape double_col double_mat_rm double_threec assert_array_equal enumerate double_mat_cm double_row double_complex reshape double_col double_mat_rm assert_array_equal enumerate range array enumerate chol array copy_ref_const assign_both ref view array_copy_but_one ones copy_ref ref_const corners_const view_ptr copy_view get copy_block block block_const ref_const_safe get_ptr block_safe copy_get ref_safe ReturnTester assert_array_equal corners get alive cl method get ReturnTester assert_keeps_alive add1 add_any add_cm add_rm add2 array get_rm_ref reset_refs get_cm_ref get_rm_const_ref get_cm_const_ref array incr_matrix_any even_cols incr_matrix reset_refs reshape get_cm_ref even_rows array array full array range ones array sparse_c sparse_copy_r assert_sparse_equal_ref sparse_copy_c sparse_r assert_allclose a diagonal CustomOperatorNew __members__ ETwo Two ESecondMode test_function EFirstMode Write Read EFirstMode test_enum_to_long_long test_enum_to_uint Read join dirname exception_matches TestFactory3 move pointer alive unique_ptr TestFactory2 TestFactory1 detail_reg_inst shared_ptr pointer alive TestFactory4 TF5 TestFactory3 detail_reg_inst TF4 shared_ptr pointer alive MyTest TestFactory6 base detail_reg_inst alias PythFactory7 mixed pointer alive TestFactory7 base detail_reg_inst alias shared_ptr invalid_base str search MITest print gc_collect NoisyAlloc gc_collect captured_output_default captured_err readouterr captured_output readouterr captured_output_default len guard_output readouterr readouterr readouterr StringIO readouterr StringIO readouterr StringIO readouterr captured_dual readouterr StringIO readouterr StringIO readouterr StringIO dict mixed_plus_args_kwargs mixed_plus_kwargs mixed_plus_args mixed_plus_args_kwargs_defaults refcount arg_refcount_h LocalType append LocalType NonLocal2 NonLocalType register_mixed_local register_mixed_global_local register_mixed_local_global register_mixed_global get_mixed_lg append MixedLocalGlobal MixedGlobalLocal get_mixed_gl VectorInt LocalVec Cat MixGL append LocalType Dog get add8 add1 add7 add5 ExampleMandA add4 add6 add9 add3 add10 add2 add2d add2b ExampleMandA add2 add2c TestProperties TestProperties TestProperties TestPropRVP getattr static_rvalue TestPropRVP rvalue get cls DynamicClass get DynamicClass ArgInspector NoneTester RegisteredDerived increase_value do_nothing destruction_tester_cstats custom_caster_destroy_const custom_caster_destroy custom_caster_no_destroy A B MIType MITypePy MITypePy MI1 MI8 MI3 MI6 MI8b MI7 MI4 MI5 MI2 MIMany117 MIMany19 MIMany14 MIMany916 range MITypePy i801b1_d i801b2_c i801b1_c I801C I801D i801b2_d detail_reg_inst i801e_c i801e_b2 i801d_b2 i801d_b1 detail_reg_inst i801c_b1 i801c_b2 D array view make_empty_shaped_array assert_references transpose random wrap diagonal array function_taking_uint64 uint64 assert_array_equal converting_constructors values default_constructors transpose proxy_add2 array ndarray proxy_add2_dyn array ndarray array_reshape2 array_resize3 array transpose create_and_resize dtype dtype partial_ld_offset dtype partial_ld_offset dtype itemsize array dtype str itemsize char alignment max dtype dtype create_rec_nested assert_equal func create_rec_partial create_rec_partial_nested arange reshape assert_array_equal range test_array_ctors create_string_array dtype create_array_array dtype create_enum_array dtype create_complex_array enumerate array array VectorizeTestClass array vectorized_func StringList push_back pop_back ClassWithSTLVecProperty enumerate alive return_unique_ptr get Vector Vector2 gc_collect as_base NestB NestC NestA cls setExtra1 setExtra2 dumps loads getattr cls HIGHEST_PROTOCOL dumps loads getattr get_set get_dict str_format gc_collect accessor_assignment accessor_api TestObject cast_functions get_implicit_casting range nonzero nonzero_keys get reversed iter Sequence range iter items range StringMap range get MyObject3 Object print MyObject1 MyObject2 cstats_ref zip enumerate get MyObject4 get MyObject5 get holder_ref A ref holder_copy copy SharedPtrRef get holder_ref ref B holder_copy bad_wp copy SharedFromThisRef get make TypeWithMoveOnlyHolder get make print_object_2 print_object_4 print_object_3 TypeForHolderWithAddressOf print_object_1 get make print_object TypeForMoveOnlyHolderWithAddressOf HeldByDefaultHolder get ElementA ElementList gc_collect add range enumerate append cast_vector cast_array cast_valarray cast_map cast_set add cast_unique_ptr_vector move_list MoveOutContainer test_no_assign half_or_none raises double_or_zero test_no_assign_exp double_or_zero_exp raises half_or_none_exp get Placeholder append insert VectorInt memoryview VectorUChar bytearray asarray VectorInt get_vectorstruct VectorStruct zeros array append VectorBool range append VectorVectorEl VectorEl El UnorderedMapStringDouble MapStringDouble MapStringDoubleConst UnorderedMapStringDoubleConst str items get_mnc get_dnc get_umnc get_vnc range enumerate UnorderedMapStringDouble MapStringDouble get ExtendedExampleVirt2 ExtendedExampleVirt ExampleVirt get NonCopyable Movable NCVirtExt2 NCVirtExt PyClass2 OverrideTest A_ref BT CR DT CCR CCT AT DR AR CT test_gil test_gil_from_thread items join sub replace items rstrip replace endswith strip min lstrip split splitlines sub startswith TextWrapper fill float len d sanitize_name process_comment append get_children spelling get_leaves len VocabInsertEncoder action_mol_to_integer integer_to_action roundtrip_action GetAtomWithIdx Vocabulary generate_random_atom_insert get_mol get_mol roundtrip_action Vocabulary compute_canonical_atom_insert_locations get_mol compute_canonical_atom_insert_locations get_mol VocabInsertEncoder GetAtomWithIdx Vocabulary generate_random_atom_insert action_mol_to_integer integer_to_action get_mol MolFromSmiles MolFromSmiles GetBondWithIdx MolFromSmiles GetAtomWithIdx MolFromSmiles loads dumps get_leaves MolFromSmiles print add_edge time RH RH RH apply_random_corruptions RH apply_random_corruptions deepcopy compute_action insert_random_node delete_random_node append RH range deepcopy time compute_action print insert_random_node delete_random_node RH range generate_dataset zip list nodes delete_random_node RH range enumerate get_edge_incidence_list graph_to_rep list combine_graph_reps map graph_to_rep delete_random_leaf RandomState get_mol range delete_random_leaf get_mol zip get_vocab RandomState insert_at_bond get_mol compute_insert_bond GetBondWithIdx insert_at_atom get_vocab RandomState GetAtomWithIdx get_mol compute_insert_atom insert_at_atom get_vocab RandomState GetAtomWithIdx sanitize compute_deletion get_mol get_vocab RandomState sanitize insert_at_bond get_mol compute_deletion GetBondWithIdx get_vocab RandomState delete_random_leaf insert_random_node get_mol Delete get_vocab compute_deletion compute_insert get_mol Delete get_vocab compute_deletion compute_insert get_mol get_vocab RandomState delete_random_leaf compute_deletion compute_insert get_mol enumerate get_vocab RandomState delete_random_leaf insert_random_node compute_insert get_mol enumerate GetAtomWithIdx get_mol atom_features bond_features GetBondWithIdx get_mol fill_atom_features zeros get_mol GetNumAtoms fill_bond_features zeros get_mol GetNumBonds join dirname get_data mol2graph get_data list get_mol values get_data list values get_mol mol2graph_single combine_mol_graph get_mol mol2graph_single get_mol mol2graph_single combine_mol_graph get_mol mol2graph_single RandomState GetNumBonds randn GetNumAtoms ones reshape fill_atom_bond_list_sparse dot fill_atom_bond_list coo_matrix shape zeros sum get_mol ones_like RandomState GetNumBonds randn GetNumAtoms ones reshape fill_bond_incidence_list_sparse get_edge_incidence_size dot shape coo_matrix zeros sum get_mol fill_bond_incidence_list fill_atom_bond_list_segment empty get_mol GetNumBonds GetIdx GetBeginAtom GetBonds GetAtoms append GetEndAtom GetNumAtoms range enumerate len mol_incidence_reference get_mol range atom_bond_list_segment mol_incidence_reference get_mol range bond_incidence_list_segment reshape tensor repeat repeat_interleave reshape shape repeat new_empty repeat_interleave_out_native tensor concatenate randn segment_logsumexp_python stack tensor array concatenate randn gradcheck stack segment_logsumexp_python concatenate randn astype int64 segment_logsumexp_native tensor array concatenate randn astype gradcheck int64 segment_logsumexp_native segment_argmax_native concatenate randn astype int64 segment_argmax_python tensor numpy array segment_argmax_native concatenate randn astype gradcheck int64 cumsum array zeros_like cartesian_prod _scopes_from_lengths RandomState arange randn segment_cartesian_product tensor cat _scopes_from_lengths RandomState normalize_values_scopes randn reshape tensor _make_data select_label_multi_segment_python select_label_multi_segment_loop _make_data segment_multi_softmax_cross_entropy_loop segment_multi_softmax_cross_entropy _make_data tuple gradcheck _make_data sum segment_multi_argmax segment_multi_argmax_loop _scopes_from_lengths RandomState numpy randn RandomState reshape segment_avg_pool1d_loop segment_avg_pool1d_native tensor long RandomState reshape gradcheck tensor long segment_max_pool1d_native RandomState reshape tensor segment_max_pool1d_loop long RandomState reshape gradcheck tensor long GetAtoms Vocabulary GetBonds Vocabulary record_statistics RandomState ClassificationSummary binomial tensor record_statistics RandomState ClassificationSummary mean binomial tensor record_statistics RandomState ClassificationSummary binomial tensor record_statistics RandomState ClassificationSummary mean binomial tensor record_statistics RandomState ConditionalAccuracySummary binomial tensor | # Discrete Object Generation with Reversible Inductive Construction <img src="aux_data/method_figure.png" width="700"> This repository contains code and pre-trained models for the generative modeling framework described in https://arxiv.org/abs/1907.08268. ## Code Please see the README in `code/` for details. ## Citation To cite this work, please use ``` @incollection{Seff2019GenRIC, title={Discrete Object Generation with Reversible Inductive Construction}, | 867 |
ProbabilisticNumerics/probabilistic_line_search | ['stochastic optimization'] | ['Probabilistic Line Searches for Stochastic Optimization', 'Probabilistic Line Searches for Stochastic Optimization'] | test/test_gaussian_process.py probls/gaussian_process.py examples/run_probls_cifar10.py probls/tensorflow_interface/interface_sgd.py examples/models/__init__.py examples/run_probls_mnist.py probls/utils.py examples/cifar10.py examples/models/mnist_2conv_2dense.py probls/tensorflow_interface/gradient_moment.py test/test_utils.py probls/tensorflow_interface/__init__.py test/demo_gaussian_process.py probls/line_search.py test/test_gradient_moment.py test/demo_interface_sgd.py probls/__init__.py examples/models/mnist_mlp.py examples/run_probls_mnist_interactive.py examples/models/cifar10_2conv_3dense.py _progress inputs _generate_image_and_label_batch distorted_inputs read_cifar10 set_up_model max_pool_3x3 conv2d bias_variable weight_variable set_up_model max_pool_2x2 conv2d bias_variable weight_variable set_up_model bias_variable weight_variable quadratic_polynomial_solve ProbLSGaussianProcess ProbLSOptimizer unbounded_bivariate_normal_integral bounded_bivariate_normal_integral _cdf _Conv2DGradMom _check_and_sort_ops _GradMom grads_and_grad_moms _MatMulGradMom _AddGradMom ProbLSOptimizerSGDInterface conv2d bias_variable max_pool_2x2 weight_variable TestNoiseFree TestSolveQuadraticPolynomial TestKernelFunctions max_pool_2x2 TestGradientMomentFullyConnected conv2d bias_variable weight_variable TestUnboundedIntegral TestBoundedIntegral TestCDF write flush read height decode_raw uint8 slice reshape CIFAR10Record transpose cast int32 width depth FixedLengthRecordReader shuffle_batch batch random_crop per_image_standardization int string_input_producer random_flip_left_right print float32 random_brightness cast random_contrast uint8image read_cifar10 per_image_standardization int string_input_producer resize_image_with_crop_or_pad float32 cast uint8image read_cifar10 truncated_normal constant pack relu max_pool_3x3 reshape matmul sparse_softmax_cross_entropy_with_logits conv2d shape int64 cast gather bias_variable weight_variable max_pool_2x2 float32 placeholder softmax reset_default_graph sigmoid sqrt sign bvnu exp isneginf concatenate min pi dot sqrt array tile sin arcsin max _cdf append _check_and_sort_ops gradients consumers extend outputs len pow pow get_attr conv2d_backprop_filter shape reshape reduce_sum pow shape _broadcast_gradient_args | # Probabilistic Line Search This is a Python implementation of a _Probabilistic Line Searches for Stochastic Optimization_ ([NIPS paper][1], [extended version][3]) plus a TensorFlow interface that allows you to use the line search to train your TensorFlow model. **Please note: this is a development version with multiple experimental changes compared to the original paper!** ## The Algorithm in a Nutshell The probabilistic line search is an algorithm for the optimization of a stochastic objective function F. Being at point x and having fixed a search direction d, it maintains a Gaussian process model for the one-dimensional function f(t) = F(x + td). This function and its derivative are evaluated at (possibly multiple) step sizes t, updating the GP after each observation. This | 868 |
Procope/emo2vec | ['word embeddings'] | ['Semi-supervised emotion lexicon expansion with label propagation and specialized word embeddings'] | count_classifier.py bilstm_semeval.py semeval_classifier.py lp_1.py baseline_classifiers.py bilstm_emo2vec.py bilstm_classifier.py crossval_300.py crossval_1.py crossval_baselines.py lp_300.py tensor_lp_1.py hashtag_corpus.py classifier_exp_lex.py tensor_lp_300.py tensor_lp_1_btc.py read_emo_lemma read_emo_lemma classify read_emo_lemma l1_normalize build_fuzzy_lexicon partition read_emo_lemma clip_to_range_0_1 Model kl_divergence partition read_emo_lemma clip_to_range_0_1 Model kl_divergence build_Y partition read_emo_lemma clip_to_range_0_1 kl_divergence max_sequence_len word_index split Model read_emo_lemma Model read_emo_lemma read_emo_lemma Model read_emo_lemma Model read_emo_lemma Model read_emo_lemma split sum dict fit_on_texts print pad_sequences texts_to_sequences build_fuzzy_lexicon l1_normalize append zeros sum max Tokenizer clip_to_range_0_1 items asarray append zeros sum fillna seed int load arange shuffle load | # emo2vec #### Semi-supervised emotion lexicon expansion with label propagation and specialized word embeddings In the proposed framework, emotion-specific word embeddings are learned from a corpus of texts labeled with six basic emotions (anger, disgust, fear, joy, sadness, and surprise). We use a Long Short Term Memory (LSTM) recurrent network that learns emotion-specific representations of words via backpropagation, where the _emotion-specificity_ of a word vector refers to the ability to encode affectual orientation and strength in a subset of its dimensions. The derived vector space model is used to expand an existing emotion lexicon via a novel variant of the Label Propagation algorithm that is tailored to distributed word representations. Batch gradient descent is used to accelerate the optimization of label propagation and to make the optimization feasible for large graphs. - Mario Giulianelli, [Semi-supervised emotion lexicon expansion with label propagation and specialized word embeddings](https://arxiv.org/abs/1708.03910), 2017, Bachelor's Thesis #### Resources - **NRC Word-Emotion Association Lexicon** aka EmoLex: association of words with eight emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and positive) manually annotated on Amazon's Mechanical Turk. The English version of this lexicon is used in emo2vec, it contains 14,182 unigrams. - Crowdsourcing a Word-Emotion Association Lexicon, Saif Mohammad and Peter Turney, Computational Intelligence, 29 (3), 436-465, 2013. - Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon, Saif Mohammad and Peter Turney, In Proceedings of the NAACL-HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, June 2010, LA, California. - **Hashtag Emotion Corpus**: a corpus of 21051 Twitter posts labeled with eight emotions using emotion-word hashtags. | 869 |
Prograf-UFF/HoVW | ['image retrieval'] | ['Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval'] | HoVW/src/clusters.py HoVW/classification/classify-nodes.py HoVW/views/reveal_clusters-graphs.py HoVW/src/index.py HoVW/src/tree.py HoVW/classification/split-train-test.py HoVW/views/visualize_graph_spatial_distribution.py HoVW/src/meanshift-graphs-test.py HoVW/src/assign-node-label.py HoVW/src/utils.py HoVW/src/image.py HoVW/src/centers_dendrogram.py HoVW/executions.py HoVW/src/kmeans-nodes-test.py HoVW/src/trees-distance.py HoVW/views/find_clusters_neighbor.py HoVW/views/reveal_clusters-nodes.py HoVW/src/meanshift-graphs.py HoVW/src/fit.py HoVW/src/pickle4reducer.py HoVW/src/__init__.py HoVW/src/descriptors.py HoVW/classification/kmeans-nodes-test.py HoVW/classification/meanshift-test.py HoVW/src/kmeans-nodes.py HoVW/src/assign-tree-label.py main distribuition_plot init_args main init_args Cluster GeometricDescriptors ZernikeMoments compute_label init_args ImageTreeDistance gabi_do_the_math main calc_dist_trees compute_hierarchy Mask Image img_structuring main init_args main read_image_descriptors init_args assign_labels my_distance compute_kneighbors main compute_distance_matrix get_mean_node save_centers save_codebook estimate_bandwidth compute_kneighbors compute_radius_neighbors main mean_shift Pickle4Reducer dump ForkingPickler4 ImageTree ImageTreeNode init_args combinations_matrix ImageTreeDistance heatmap_plot main calc_dist_trees Log retrive_imgs make_dir handle_tree clusters_len main log retrive_imgs make_dir handle_tree clusters_len main log SpacePoint update_lines d2c add_argument ArgumentParser join xlabel text ylabel bar savefig zip append xticks keys range values len items sorted d init_args name print set_labels t OrderedDict distribuition_plot i c sub append set_nodes_depth keys load dendrogram xlabel ylabel title figure save zip savefig zeros o linkage range len build_top Image z gabi_do_the_math float_ calc_dist_trees compute_label Cluster label Log Image join format close listdir l int asarray print shape sub append listdir range delete predict int time read_image_descriptors fit append print float_ zeros range len print assign_labels compute_distance_matrix compute_kneighbors argmin zeros sum range argsort zeros shape int check_random_state compute_kneighbors mean max asarray range items sorted get_mean_node ones size compute_kneighbors flatten compute_radius_neighbors zeros array range enumerate len append savez save save_centers save_codebook size mean_shift register imshow title savefig colorbar print name zeros range zip argmax combinations_matrix p shape heatmap_plot factorial partial islice m mean combinations unravel_index median array str join name print copyfile mkdir label print label join rmtree mkdir str write items open retrive_imgs make_dir tree handle_tree clusters_len log draw print root set_data set_3d_properties zip sqrt set_coordinates append SpacePoint range amax | # Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval The Hierarchy-of-Visual-Words (HoVW) is a trademark image retrieval method that decomposes images into simpler geometric shapes and defines a descriptor for trademark image representation by encoding the hierarchical arrangement of component shapes. The proposed hierarchical organization of visual data stores each component shape as a visual word. It is capable of representing the geometry of individual elements and the topology of the trademark image, making the descriptor robust against linear as well as to some level of nonlinear transformation. --- ### Check out our paper at [arXiv](https://arxiv.org/abs/1908.02786). | <img src="docs/logo_sibgrapi.png" width="400"> |:trophy: [Best Undergraduate Work Award]()<br />:trophy: [Best Computer Vision/Image Processing/Pattern Recognition Main Track Paper Award]()<br />at the [32nd Conference on Graphics, Patterns and Images (SIBGRAPI) 2019](http://www.mat.puc-rio.br/sibgrapi2019/) |:-:|:-| ## Cite ``` @inproceedings{lourenco2019, title = {{Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval}}, | 870 |
ProofByConstruction/texture-networks | ['style transfer'] | ['A Neural Algorithm of Artistic Style', 'Texture Networks: Feed-forward Synthesis of Textures and Stylized Images'] | texture_network.py network_helpers.py perceptual_loss_network.py tests/style_helpers_test.py style_helpers.py vgg_network.py coco_data.py COCODataBatcher conv2d_block_with_weights conv2d spatial_batch_norm conv2d_with_weights load_image residual_block PerceptualLossNetwork nonresidual_block total_variation gramian conv_block leaky_relu TextureNetwork noise_pyramid input_pyramid join_block conv_chain VGGNetwork TestGramians int imread min resize spatial_batch_norm conv2d_with_weights Variable sqrt conv2d random_uniform rsqrt mul Variable sub random_uniform moments as_list reshape transpose batch_matmul as_list slice square reduce_sum add sqrt sub | ## Texture Networks in Tensorflow. The short-term goal of this project is to implement Texture Networks, as described in http://arxiv.org/abs/1603.03417. (For more background, see http://arxiv.org/abs/1508.06576) Longer term goals include exploring extensions and related architectures around the theme. Development is being done in python 3.5, though you will need to use 2.7 to run Tensorboard. ## Setup You will need to get a copy of the VGG16 network, I recommend the torrent listed at https://github.com/ry/tensorflow-vgg16 Set up a python 3.5 virtual environment, intall tensorflow and then `pip install -r requirements.txt`. (If that scares you, I think the only real reqs so far are tensorflow, scikit, and pillow. If you hit problems let me know.) You should then be good to run `python texture_network.py` and `python vgg_network.py` (after pointing the latter at your local vgg file, and adding an img.jpg to the img directory). | 871 |
PurdueMINDS/JanossyPooling | ['stochastic optimization'] | ['Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs'] | graphsage/train_janossy_gs.py graphsage/preprocess_ppi.py graphsage/janossy_gs/__init__.py graphsage/predict_janossy_gs.py arithmetic_tasks/train.py graphsage/janossy_gs/data_loaders.py graphsage/janossy_gs/graph_models.py arithmetic_tasks/__init__.py graphsage/training_utils.py graphsage/__init__.py arithmetic_tasks/models.py TextModels image_dataset_construction text_dataset_construction determine_vocab_size construct_task_specific_output unison_shuffled train_text valid_argument_check permute func main parse_args janossy_text_input_construction make_inference_paths preprocess_ppi set_logger RunningAverage get_n_params Params save_dict_to_json parse_args build_out_path load_citation load_ppi load_cora load_pubmed load_reddit MeanAggregator Encoder JanossyGraphSage LSTMAggregator SupervisedGraphSage add_argument ArgumentParser print int exit combinations list sort append range len int randint construct_task_specific_output tqdm zeros range janossy_text_input_construction unique permutation len zero_grad mean_absolute_error TextModels str Adam apply_along_axis unison_shuffled append to range int time mean_squared_error backward text_dataset_construction print parameters filter zeros step loss len int str neurons learning_rate batch_size determine_vocab_size iterations lower train_text hidden_layers valid_argument_check parse_args range str replace load write_gpickle format node_link_graph isinstance print fit remove_node nodes dict save edges transform StandardScaler array open setFormatter getLogger addHandler StreamHandler Formatter setLevel INFO FileHandler parameters filter sum lower typical_epochs pop items sorted str OrderedDict zip keys values zeros empty defaultdict zeros empty defaultdict load get defaultdict nodes set dict read_gpickle len | # Janossy Pooling ### Authors: Ryan L. Murphy and Balasubramaniam Srinivasan ## Overview: This is the code for [Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs](https://arxiv.org/abs/1811.01900). We evaluate different Janossy Pooling models for tasks similar to those found in [Deep Sets](https://github.com/manzilzaheer/DeepSets) and [GraphSAGE](https://github.com/williamleif/GraphSAGE). Our implementation follows these, as well as the reference [PyTorch implementation of GraphSAGE](https://github.com/williamleif/graphsage-simple/). The latter repo also contains two datasets that we use. The first set of tasks is to perform arithmetic on sequences of integers: sum, range, unique sum, unique count, and variance. Note that these functions are all permutation-invariant (symmetric). The second set of tasks learns vertex embeddings in graphs for vertex classification. The data are described below. Please see the supplementary section for a brief description and summary of the code. ## Requirements | 872 |
PyRetri/PyRetri | ['content based image retrieval', 'image retrieval'] | ['PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks'] | pyretri/extract/aggregator/aggregators_impl/gmp.py pyretri/datasets/transformer/transformers_base.py search/reid_search_index.py pyretri/models/builder.py pyretri/extract/aggregator/aggregators_impl/crow.py pyretri/datasets/collate_fn/collate_fn_base.py pyretri/datasets/__init__.py pyretri/extract/builder.py pyretri/datasets/transformer/transformers_impl/__init__.py pyretri/evaluate/builder.py pyretri/extract/splitter/splitter_impl/identity.py search/search_rmac_modules/pre_process_dict.py pyretri/extract/extractor/extractors_impl/res_series.py pyretri/extract/extractor/extractors_impl/vgg_series.py pyretri/extract/aggregator/aggregators_impl/scda.py pyretri/index/dim_processor/dim_processors_impl/rmac_pca.py pyretri/models/backbone/backbone_impl/vgg.py search/reid_search_modules/extract_dict.py pyretri/config/config.py pyretri/index/metric/metric_base.py pyretri/utils/__init__.py search/search_pwa_modules/extract_dict.py pyretri/index/builder.py pyretri/datasets/transformer/__init__.py pyretri/index/dim_processor/dim_processors_impl/l2_normalize.py search/search_extract.py search/reid_search_modules/index_dict.py pyretri/models/backbone/__init__.py main/single_index.py pyretri/extract/helper/__init__.py pyretri/index/helper/helper.py search/reid_search_extract.py pyretri/datasets/registry.py pyretri/extract/aggregator/aggregators_base.py search/search_modules/pre_process_dict.py pyretri/index/registry.py pyretri/index/dim_processor/dim_processors_impl/svd.py pyretri/index/metric/metric_impl/__init__.py setup.py pyretri/models/__init__.py pyretri/utils/module_base.py main/extract_feature.py pyretri/datasets/config.py pyretri/models/backbone/backbone_impl/__init__.py pyretri/index/dim_processor/__init__.py pyretri/extract/extractor/extractors_base.py pyretri/extract/aggregator/__init__.py pyretri/datasets/collate_fn/__init__.py pyretri/extract/config.py main/make_data_json.py pyretri/index/dim_processor/dim_processors_impl/pca.py pyretri/extract/splitter/splitter_impl/__init__.py search/utils/search_modules.py pyretri/evaluate/evaluator/__init__.py pyretri/datasets/collate_fn/collate_fn_impl/__init__.py pyretri/utils/misc.py pyretri/index/feature_enhancer/feature_enhancer_impl/identity.py pyretri/utils/builder.py pyretri/index/dim_processor/dim_processors_base.py pyretri/datasets/folder/__init__.py pyretri/evaluate/evaluator/evaluators_impl/oxford_overall.py pyretri/index/feature_enhancer/feature_enhancer_impl/database_augmentation.py pyretri/index/dim_processor/dim_processors_impl/identity.py pyretri/index/metric/__init__.py pyretri/config/__init__.py pyretri/datasets/builder.py pyretri/datasets/folder/folder_base.py pyretri/datasets/folder/folder_impl/__init__.py search/search_pwa_modules/pre_process_dict.py pyretri/extract/aggregator/aggregators_impl/gem.py pyretri/index/metric/metric_impl/knn.py pyretri/evaluate/evaluator/evaluators_impl/overall.py pyretri/extract/utils/__init__.py pyretri/index/helper/__init__.py search/utils/misc.py pyretri/extract/__init__.py pyretri/extract/aggregator/aggregators_impl/__init__.py pyretri/evaluate/helper/__init__.py main/index.py search/search_rmac_modules/index_dict.py pyretri/index/utils/feature_loader.py pyretri/evaluate/__init__.py pyretri/evaluate/registry.py pyretri/evaluate/evaluator/evaluators_impl/reid_overall.py pyretri/index/re_ranker/re_ranker_base.py pyretri/index/dim_processor/dim_processors_impl/part_pca.py main/split_dataset.py search/search_rmac_modules/extract_dict.py pyretri/extract/registry.py pyretri/extract/helper/helper.py pyretri/__init__.py search/search_modules/extract_dict.py pyretri/index/utils/__init__.py pyretri/models/registry.py pyretri/models/backbone/backbone_base.py search/show_search_results.py pyretri/models/backbone/backbone_impl/resnet.py pyretri/extract/splitter/splitter_base.py pyretri/index/__init__.py pyretri/extract/extractor/extractors_impl/__init__.py search/search_index.py search/reid_search_modules/pre_process_dict.py pyretri/extract/splitter/splitter_impl/pcb.py search/search_pwa_modules/index_dict.py pyretri/extract/aggregator/aggregators_impl/spoc.py pyretri/evaluate/helper/helper.py pyretri/index/dim_processor/dim_processors_impl/__init__.py pyretri/extract/extractor/__init__.py pyretri/extract/aggregator/aggregators_impl/gap.py pyretri/evaluate/evaluator/evaluators_impl/__init__.py pyretri/index/re_ranker/re_ranker_impl/query_expansion.py pyretri/extract/splitter/__init__.py pyretri/index/re_ranker/re_ranker_impl/qe_kr.py pyretri/models/backbone/backbone_impl/reid_baseline.py pyretri/extract/utils/split_dataset.py pyretri/index/re_ranker/re_ranker_impl/__init__.py pyretri/index/config.py pyretri/datasets/folder/folder_impl/folder.py pyretri/index/feature_enhancer/feature_enhancer_base.py pyretri/datasets/transformer/transformers_impl/transformers.py pyretri/extract/aggregator/aggregators_impl/r_mac.py search/search_modules/index_dict.py pyretri/extract/utils/make_data_json.py pyretri/models/config.py pyretri/evaluate/config.py pyretri/index/re_ranker/re_ranker_impl/identity.py pyretri/datasets/collate_fn/collate_fn_impl/collate_fn.py search/search_modules/__init__.py pyretri/index/feature_enhancer/__init__.py pyretri/extract/extractor/extractors_impl/reid_series.py pyretri/evaluate/evaluator/evaluators_base.py pyretri/index/dim_processor/dim_processors_impl/part_svd.py pyretri/index/re_ranker/__init__.py pyretri/index/re_ranker/re_ranker_impl/k_reciprocal.py search/search_pwa_extract.py pyretri/utils/registry.py pyretri/extract/aggregator/aggregators_impl/pwa.py pyretri/index/feature_enhancer/feature_enhancer_impl/__init__.py write_version_py readme get_version parse_requirements get_git_hash get_hash main parse_args main parse_args main parse_args main parse_args main parse_args get_defaults_cfg setup_cfg build_loader build_collate build_folder build_transformers get_tranformers_cfg get_folder_cfg get_collate_cfg get_datasets_cfg CollateFnBase CollateFn FolderBase Folder TransformerBase TenCrop PadResize CenterCrop ToTensor DirectResize Normalize TwoFlip ToCaffeTensor ShorterResize build_evaluator build_evaluate_helper get_evaluator_cfg get_evaluate_cfg EvaluatorBase OverAll OxfordOverAll ReIDOverAll EvaluateHelper build_splitter build_extractor build_extract_helper build_aggregators get_extractor_cfg get_extract_cfg get_splitter_cfg get_aggregators_cfg AggregatorBase Crow GAP GeM GMP PWA RMAC SCDA SPoC ExtractorBase ReIDSeries ResSeries VggSeries ExtractHelper SplitterBase Identity PCB make_data_json make_ds_for_general make_ds_for_reid make_ds_for_oxford split_dataset build_index_helper build_processors build_enhance build_metric build_ranker get_processors_cfg get_index_cfg get_metric_cfg get_ranker_cfg get_enhancer_cfg DimProcessorBase Identity L2Normalize PartPCA PartSVD PCA RMACPCA SVD EnhanceBase DBA Identity IndexHelper MetricBase KNN ReRankerBase Identity KReciprocal QEKR QE FeatureLoader build_model get_model_cfg BackboneBase ft_net_dense ClassBlock ft_net PCB_test weights_init_classifier ft_net_middle PCB weights_init_kaiming conv1x1 ResNet resnet50 Bottleneck resnet152 conv3x3 resnet34 resnet18 BasicBlock resnet101 vgg19 VGG vgg16_bn vgg19_bn vgg11_bn make_layers vgg11 vgg13 vgg13_bn vgg16 get_config_from_registry simple_build ensure_dir load_state_dict ModuleBase _register_generic Registry main parse_args load_datasets main parse_args load_datasets main parse_args load_datasets get_evaluate load_datasets main get_fea_names parse_args main parse_args load_datasets main parse_args show_results get_default_result_dict save_to_csv filter_by_keywords get_dir check_result_exist SearchModules _convert_dict_to_cfg decode _minimal_ext_cmd exists get_hash list gen_packages_items add_argument ArgumentParser extract build_folder build_model model save_path do_extract config_file setup_cfg data_json datasets build_loader save_interval get_defaults_cfg opts parse_args build_extract_helper load build_index_helper do_eval query_fea_dir evaluate do_index index build_evaluate_helper show_results feature_names gallery_fea_dir print make_data_json ground_truth dataset type build_transformers list do_single_extract append transformers concatenate save_topk_retrieved_images convert cpu split_dataset split_file CfgNode get_index_cfg get_evaluate_cfg get_extract_cfg get_model_cfg get_datasets_cfg merge_from_file freeze merge_from_list simple_build append list Compose simple_build folder build_transformers simple_build transformers build_collate DataLoader collate_fn get_config_from_registry get_config_from_registry get_config_from_registry get_tranformers_cfg get_folder_cfg CfgNode get_collate_cfg dict dict dict dict dict dict dict simple_build evaluator EvaluateHelper build_evaluator get_config_from_registry get_evaluator_cfg CfgNode append list simple_build simple_build simple_build assemble splitter build_splitter build_aggregators aggregators build_extractor ExtractHelper extractor list get_config_from_registry get_config_from_registry get_config_from_registry get_aggregators_cfg get_splitter_cfg CfgNode get_extractor_cfg dict dict dict dict dict list dict dict join list dict append listdir walk len join list dict append walk join list dict append walk make_ds_for_general make_ds_for_reid make_ds_for_oxford simple_build simple_build append list simple_build simple_build dim_processors build_processors build_enhance metric build_metric build_ranker re_ranker feature_enhancer IndexHelper get_config_from_registry get_config_from_registry get_config_from_registry get_config_from_registry get_processors_cfg CfgNode get_metric_cfg get_ranker_cfg get_enhancer_cfg dict dict dict dict load load_state_dict_from_url load_state_dict CfgNode data normal_ kaiming_normal_ __name__ constant_ data normal_ __name__ constant_ ResNet ResNet ResNet ResNet ResNet Conv2d make_layers VGG make_layers VGG make_layers VGG make_layers VGG make_layers VGG make_layers VGG make_layers VGG make_layers VGG CfgNode default_hyper_params default_hyper_params makedirs data items list format isinstance print set copy_ append keys state_dict dict values models extracts exists join items load_datasets merge_from_other_cfg pre_processes get_default_result_dict get_dir indexes names float listdir fea_dir evaluates dict check_result_exist get_fea_names get_evaluate format replace print range len save_to_csv filter_by_keywords join replace append sorted range len append pop range len CfgNode isinstance | <p align="center"> <img src='teaser_image/logo.jpg'> </p> # PyRetri Benyi Hu, Ren-Jie Song, Xiu-Shen Wei*, Yazhou Yao, Xian-Sheng Hua, Yuehu Liu Corresponding author: [Xiu-Shen Wei](http://www.weixiushen.com/) ## Introduction PyRetri (pronounced as [ˈperɪˈtriː]) is a unified deep learning based unsupervised image retrieval toolbox built on PyTorch, which is designed for researchers and engineers.  ### Major Features | 873 |
Pythonista7/Implementing_ | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | NN_from_Scratch.py learn evaluate NN to_categorical sigmoid activation_tanh split_data sigmoid_prime cost_fn predict zeros enumerate sum log T dot sigmoid sigmoid_prime cost_fn len T zip sigmoid T dot predict int len show learn evaluate plot print rand split_data append range len | # Implementing_ Here's were I try and build some cool stuff from scratch inorder to better understand them. <br> <H4> 1.Build a neural net from scratch only NumPy</H4><br> Dependencies to run this code :<br> * Numpy(numerical computational library) * sklearn(for a dataset to train and test the network) If you dont have the dependencies installed type the following in your command line: * $pip3 install numpy * $pip3 install sklearn | 874 |
QData/TextAttack-Search-Benchmark | ['adversarial text', 'data augmentation'] | ['Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples'] | recipes/word-swap-hownet/strict/constraint.py recipes/word-swap-embedding/strict/beam-search/greedy-recipe.py recipes/word-swap-hownet/lax/population/pso-recipe.py recipes/word-swap-wordnet/strict/greedy-word-wir/random-recipe.py recipes/word-swap-embedding/strict/beam-search/beam32-recipe.py recipes/word-swap-embedding/strict/population/mha-recipe.py recipes/word-swap-wordnet/lax/beam-search/beam8-recipe.py recipes/word-swap-wordnet/lax/beam-search/beam64-recipe.py recipes/word-swap-embedding/strict/greedy-word-wir/delete-recipe.py recipes/word-swap-wordnet/lax/beam-search/beam16-recipe.py recipes/word-swap-embedding/strict/greedy-word-wir/unk-recipe.py recipes/word-swap-hownet/lax/greedy-word-wir/random-recipe.py recipes/word-swap-embedding/lax/beam-search/beam16-recipe.py recipes/word-swap-embedding/lax/greedy-word-wir/gradient-recipe.py recipes/word-swap-embedding/lax/greedy-word-wir/pwws-recipe.py recipes/word-swap-embedding/strict/greedy-word-wir/random-recipe.py recipes/word-swap-wordnet/strict/population/pso-recipe.py recipes/word-swap-hownet/strict/greedy-word-wir/gradient-recipe.py recipes/word-swap-wordnet/strict/beam-search/beam32-recipe.py recipes/word-swap-hownet/lax/greedy-word-wir/pwws-recipe.py recipes/word-swap-hownet/transformation.py recipes/word-swap-embedding/strict/beam-search/beam8-recipe.py recipes/word-swap-hownet/strict/beam-search/beam32-recipe.py recipes/word-swap-wordnet/transformation.py recipes/word-swap-hownet/lax/beam-search/beam32-recipe.py recipes/word-swap-wordnet/strict/beam-search/beam16-recipe.py recipes/word-swap-embedding/lax/greedy-word-wir/random-recipe.py recipes/word-swap-embedding/lax/beam-search/beam64-recipe.py recipes/word-swap-embedding/strict/beam-search/beam64-recipe.py recipes/word-swap-embedding/lax/beam-search/beam4-recipe.py recipes/word-swap-wordnet/lax/beam-search/greedy-recipe.py recipes/word-swap-embedding/strict/beam-search/beam4-recipe.py recipes/word-swap-wordnet/strict/beam-search/greedy-recipe.py recipes/word-swap-wordnet/lax/greedy-word-wir/random-recipe.py recipes/word-swap-wordnet/lax/greedy-word-wir/pwws-recipe.py recipes/word-swap-hownet/lax/greedy-word-wir/unk-recipe.py recipes/word-swap-hownet/strict/beam-search/beam4-recipe.py recipes/word-swap-embedding/lax/beam-search/beam8-recipe.py recipes/word-swap-embedding/strict/constraint.py recipes/word-swap-hownet/lax/beam-search/beam16-recipe.py recipes/word-swap-wordnet/lax/population/pso-recipe.py recipes/word-swap-wordnet/strict/beam-search/beam64-recipe.py recipes/word-swap-hownet/lax/population/genetic-recipe.py recipes/word-swap-wordnet/strict/greedy-word-wir/delete-recipe.py recipes/word-swap-wordnet/strict/population/genetic-recipe.py recipes/word-swap-embedding/lax/beam-search/greedy-recipe.py recipes/word-swap-wordnet/lax/beam-search/beam32-recipe.py recipes/word-swap-embedding/lax/greedy-word-wir/delete-recipe.py recipes/word-swap-hownet/lax/beam-search/beam64-recipe.py recipes/word-swap-wordnet/lax/greedy-word-wir/delete-recipe.py recipes/word-swap-embedding/strict/population/pso-recipe.py recipes/word-swap-embedding/strict/beam-search/beam16-recipe.py recipes/word-swap-embedding/strict/greedy-word-wir/pwws-recipe.py recipes/word-swap-hownet/strict/greedy-word-wir/random-recipe.py recipes/word-swap-wordnet/lax/population/mha-recipe.py recipes/word-swap-wordnet/strict/greedy-word-wir/unk-recipe.py recipes/word-swap-wordnet/lax/constraint.py recipes/word-swap-wordnet/strict/greedy-word-wir/gradient-recipe.py recipes/word-swap-hownet/lax/constraint.py recipes/word-swap-hownet/strict/beam-search/greedy-recipe.py recipes/word-swap-wordnet/strict/greedy-word-wir/pwws-recipe.py recipes/word-swap-hownet/lax/greedy-word-wir/delete-recipe.py recipes/word-swap-embedding/strict/greedy-word-wir/gradient-recipe.py recipes/word-swap-wordnet/strict/population/mha-recipe.py recipes/word-swap-hownet/strict/beam-search/beam16-recipe.py recipes/word-swap-hownet/lax/beam-search/greedy-recipe.py recipes/word-swap-hownet/strict/greedy-word-wir/unk-recipe.py recipes/word-swap-wordnet/lax/population/genetic-recipe.py recipes/word-swap-embedding/lax/population/pso-recipe.py recipes/word-swap-hownet/strict/greedy-word-wir/delete-recipe.py recipes/word-swap-wordnet/strict/beam-search/beam4-recipe.py grid_run.py recipes/word-swap-hownet/strict/population/genetic-recipe.py run_experiment.py recipes/word-swap-embedding/lax/greedy-word-wir/unk-recipe.py recipes/word-swap-hownet/lax/beam-search/beam4-recipe.py recipes/word-swap-hownet/strict/beam-search/beam64-recipe.py recipes/word-swap-embedding/transformation.py recipes/word-swap-wordnet/lax/beam-search/beam4-recipe.py recipes/word-swap-embedding/lax/population/mha-recipe.py recipes/word-swap-wordnet/strict/constraint.py recipes/word-swap-hownet/lax/greedy-word-wir/gradient-recipe.py recipes/word-swap-hownet/strict/population/pso-recipe.py recipes/word-swap-embedding/lax/constraint.py recipes/word-swap-embedding/lax/population/genetic-recipe.py recipes/word-swap-hownet/lax/population/mha-recipe.py recipes/word-swap-hownet/lax/beam-search/beam8-recipe.py recipes/word-swap-hownet/strict/beam-search/beam8-recipe.py recipes/word-swap-embedding/lax/beam-search/beam32-recipe.py recipes/word-swap-wordnet/lax/greedy-word-wir/gradient-recipe.py recipes/word-swap-embedding/strict/population/genetic-recipe.py recipes/word-swap-hownet/strict/greedy-word-wir/pwws-recipe.py recipes/word-swap-wordnet/strict/beam-search/beam8-recipe.py recipes/word-swap-wordnet/lax/greedy-word-wir/unk-recipe.py recipes/word-swap-hownet/strict/population/mha-recipe.py run Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack Attack print join BeamSearch UntargetedClassification GreedySearch GreedyWordSwapWIR AlzantotGeneticAlgorithm MetropolisHastingsSampling ParticleSwarmOptimization | # Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples This repo contains the code and results for paper "[Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples](https://arxiv.org/abs/2009.06368)", which will appear in the [EMNLP 2020 Blackbox NLP Workshop](https://blackboxnlp.github.io/) track proceedings. Note that all the experiment was carried using [**TextAttack**](https://github.com/QData/TextAttack), which is a Python framework for adversarial attacks, data augmentation, and model training in NLP. ## Attack Recipes In TextAttack, an adversarial attack is composed of four components: a transformation, a set of constraints, a goal function, and a search algorithm. An attack recipe is a specification of these four components that TextAttack uses to create the adversarial attacks. Each recipe is a Python file that is imported by TextAttack on the fly. Attack recipes for each experiment are in `recipes` folder, organized by the search space (i.e. transformation and constraints) and search algorithm. Note that the are two version of each recipes: "strict" and "lax". Recipes under `lax` have weaker threshold values for constraints. In our paper, we experimented with both weak and strict constraints, and present results of experiments with strict constraints. ## Results TextAttack can output both `.txt` and `.csv` logs for each run. Result of each experiment is in `results`, organized by the victim model, search space, and search algorithm. Similar to recipes, there are results for both strict and weak constraint settings. Our paper mainly deals with those under strict constraint settings. ## Reproducing Experiments To reproduce these experiments, first install [**TextAttack**](https://github.com/QData/TextAttack). Then, you can run `run_experiment.py` script to run each experiment. | 875 |
QUVA-Lab/timeception | ['action recognition'] | ['Timeception for Complex Action Recognition'] | datasets/charades.py core/image_utils.py nets/i3d_torch_charades_utils.py nets/layers_pytorch.py experiments/train_keras.py nets/resnet_152_pytorch.py nets/i3d_torch_charades.py nets/i3d_keras.py nets/timeception.py core/keras_utils.py core/const.py __doc__.py core/metrics.py nets/i3d_torch_charades_test.py core/config_utils.py experiments/test_keras.py core/data_utils.py core/pytorch_utils.py experiments/train_pytorch.py main.py nets/layers_keras.py nets/timeception_pytorch.py core/config.py core/utils.py nets/resnet_152_keras.py __main cfg_print_cfg get_machine_name cfg_from_list __parse_gpu_id cfg_from_file cfg_from_attrdict __config_gpu_for_keras __config_gpu_for_tensorflow __config_gpu_for_pytorch cfg_merge_dicts import_dl_platform config_gpu cfg_from_dict __config_gpu_for_caffe cfg_sanity_check DataGeneratorCharades AsyncLoaderVideoFeatures DatasetCharades __resize_frame resize_crop_scaled AsyncImageReaderMultiTHUMOSForI3DKerasModel __resize_crop resize_crop resize_keep_aspect_ratio_padded __resize_keep_aspect_ratio_padded AsyncImageReaderBreakfastForI3DKerasModel __resize_keep_aspect_ratio_min_dim AsyncImageReaderResNet152Keras resize_keep_aspect_ratio_max_dim __resize_keep_aspect_ratio_max_dim resize_frame resize_keep_aspect_ratio_min_dim __resize_crop_scaled save_model_figure save_model load_model map_charades calc_num_batches SaveCallback layer_exist map_sklearn acuracy_top_n map_charades accuracy save_model load_model padding1d padding3d summary ModelSaver calc_padding_1d h5_dump get_size_in_kb normalize_sum file_pathes pkl_dump learn_manifold file_names print_array_joined Path folder_names json_dump byte_dump normalize_range_0_to_1 h5_load_multi print_counter get_model_feat_maps_info byte_load remove_extension normalize_mean normalize_l1 get_array_memory_size get_expected_memory_size h5_dump_multi AttrDict pkl_load get_size_in_mb DurationTimer h5_load debinarize_label folder_pathes csv_load mat_load txt_dump convert_dict_to_attrdict calc_num_batches get_file_extension json_load normalize_mean_std array_to_text txt_load yaml_load timestamp normalize_l2 print_array get_size_in_gb __convert_seconds_to_frame_idx __count_time_in_each_video _02_prepare_annotation_frame_dict __sample_frames_ordered extract_features_i3d_charades __preprocess_img __get_video_frame_pathes _13_prepare_annotation_frames_per_video_dict_untrimmed_multi_label_for_i3d _06_prepare_video_annotation_multi_label __get_frame_names_from_csv_file _12_prepare_annotation_frames_per_video_dict_multi_label_all_frames __get_frames_names_in_given_duration __get_frame_names_untrimmed_from_csv_file_for_ordered __relative_to_absolute_pathes _03_prepare_annotation_frame_list _01_prepare_annotation_class_names __pre_process_for_charades __count_how_many_videos_per_class __sample_frames_for_i3d __sample_frames_ordered_for_resnet __get_frames_relative_pathes_in_given_duration _08_prepare_annotation_frames_per_video_dict_multi_label __sample_frames _14_prepare_annotation_frames_per_video_dict_untrimmed_multi_label_for_resnet_ordered __test_video_names_in_annotation_list __get_frame_names_untrimmed_from_csv_file_for_i3d test_tco train_ete __define_data_generator __define_timeception_model train_tco __main train_ete __main __define_loader Model train_tco __define_timeception_model _obtain_input_shape extract_features Inception_Inflated3d evaluate_model conv3d_bn InceptionI3d MaxPool3dSamePadding InceptionModule Unit3D InceptionI3d MaxPool3dSamePadding InceptionModule Unit3D __load_i3d_model_rgb __extract_features_rgb load_model_i3d_charades_rgb_for_testing __get_video_frame_pathes extract_features_rgb ExpandDimsLayer GroupedDenseLayer DepthwiseConv3DLayer DepthwiseDenseLayer GroupedConv3DLayer MaxLayer NormalizationLayer SliceLayer DepthwiseConv1DLayer ChannelShuffleLayer TransposeLayer SqueezeLayer DepthwiseConvOverTimeLayer AverageLayer SqueezeAllLayer DepthwiseConv2DLayer SumLayer ReshapeLayer DepthwiseConv1DLayer ChannelShuffleLayer identity_block conv_block Scale ResNet152 get_mean_std_for_resnet_152_pytorch_model get_resnet_152_charades_model __grouped_convolutions Timeception __temporal_convolutional_block __get_n_channels_per_branch timeception_layers Timeception __config_gpu_for_caffe __config_gpu_for_tensorflow __config_gpu_for_pytorch __config_gpu_for_keras __parse_gpu_id clear_session str set_session __parse_gpu_id ConfigProto Session __parse_gpu_id set_device str __parse_gpu_id gpu_core_id parse_args add_argument ArgumentParser pformat info AttrDict list items isinstance literal_eval type yaml_load cfg_merge_dicts cfg_sanity_check cfg_merge_dicts items list format literal_eval type split format literal_eval zip type split tile resize int float tile resize int float tile resize shape int tile resize shape int tile resize int shape __resize_keep_aspect_ratio_max_dim tile zeros float plot_model compile loads load_weights model_from_json to_json save_weights layers int float constant concatenate cumsum float32 reduce_sum map_fn argsort mean cast reverse append expand_dims range equal invert astype float sum array nan_to_num mean sum float argmax len float len zip save_state_dict load load_state_dict str pad any max conv3d shape pad any max int str remove format isinstance FloatTensor model print apply OrderedDict lower numpy abs prod array convert_dict_to_attrdict File close value File close len File close create_dataset File close create_dataset range len read_csv values loadmat natsorted natsorted natsorted natsorted mean std mean sum array max divide add join dtype size print print join fit_transform array format now info items list isinstance AttrDict arange data_root_path txt_load pkl_dump zip array data_root_path print pkl_dump average sum range len list data_root_path print pkl_dump choice shape append randint array range pkl_load len pkl_dump add dict unique zip append zeros enumerate pkl_load len pkl_dump __get_frame_names_from_csv_file data_root_path pkl_dump __get_frame_names_from_csv_file data_root_path pkl_dump data_root_path __get_frame_names_untrimmed_from_csv_file_for_i3d pkl_dump __get_frame_names_untrimmed_from_csv_file_for_ordered data_root_path max data_root_path print min dict average array len max data_root_path print readlines min dict average array len dict readlines data_root_path len randint choice len int arange tolist float len arange astype int32 float len int float arange len data_root_path print min average sum max len plot_multi data_root_path print append array range pkl_load len data_root_path print vstack enumerate pkl_load len __convert_seconds_to_frame_idx file_names data_root_path __convert_seconds_to_frame_idx file_names data_root_path int float round pkl_dump _sleep list load_video_frames_in_batch transpose squeeze __get_video_frame_pathes range pkl_load update load_model_i3d_charades_rgb_for_testing AsyncVideoReaderCharadesForI3DTorchModel keys time get_images is_busy print reshape dict timestamp summary len array data_root_path array float32 imread astype resize_crop resize_crop astype float32 N_WORKERS now fit_generator __define_timeception_model DATASET_NAME summary SaveCallback info __define_data_generator N_EPOCHS __define_timeception_model get_model_feat_maps_info N_CLASSES BACKBONE_FEATURE BATCH_SIZE BACKBONE_CNN DATASET_NAME data_generator_class N_TC_TIMESTEPS int get_model_feat_maps_info LR ADAM_EPSILON BACKBONE_FEATURE N_TC_LAYERS N_CLASSES Input map_charades Timeception NAME BACKBONE_CNN Model MULTISCALE_TYPE timeception_module CLASSIFICATION_TYPE compile N_TC_TIMESTEPS train_ete add_option OptionParser SCHEME error cfg_from_file config_file warning train_tco parse_args model __define_loader zero_grad save ModelSaver step metric_fn range astype eval float enumerate time backward write int32 loss_fn train numpy get_model_feat_maps_info N_CLASSES BACKBONE_FEATURE BATCH_SIZE n_samples N_WORKERS n_batches BACKBONE_CNN DataLoader DATASET_NAME dataset_class N_TC_TIMESTEPS NLLLoss accuracy parameters BCELoss to str warn _obtain_input_shape int concatenate Model load_weights Input conv3d_bn norm exp print DATA_ROOT_PATH zeros sum Inception_Inflated3d predict load norm exp print DATA_ROOT_PATH sum Inception_Inflated3d predict int str add_option OptionParser gpu_core_id is_local_machine __extract_features_rgb parse_args end_num begin_num load InceptionI3d replace_logits eval load_state_dict train cuda pkl_dump _sleep list load_imgs_in_batch transpose __get_video_frame_pathes expand_dims range pkl_load update __load_i3d_model_rgb DATA_ROOT_PATH AsyncImageReaderCharadesForI3DTorchModel keys time get_images is_busy print dict timestamp summary len load InceptionI3d replace_logits load_state_dict train cuda str add str add _obtain_input_shape conv_block get_source_inputs Model load_weights identity_block Input range load items list format replace print Sequential OrderedDict eval load_state_dict DATA_ROOT_PATH train cuda int_shape __grouped_convolutions range __get_n_channels_per_branch as_list int __temporal_convolutional_block append range int float | ## New and Updated Repository https://github.com/noureldien/timeception https://noureldien.com/research/timeception/ ## Timeception for Complex Action Recognition This code repository is the implementation for the paper [Timeception for Complex Action Recognition](https://arxiv.org/abs/1812.01289). We provide the implementation for 3 different libraries: `keras`, `tensorflow` and `pytorch`.  ### Citation Please consider citing this work using this BibTeX entry ```bibtex | 876 |
R4h4/AIforSEA_Traffic_Management | ['traffic prediction'] | ['T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction'] | preprocessing.py main.py predict.py main Predictor Preprocessor save_predictions values print Preprocessor load_data Predictor predict | # Traffic Management - AI FOR S.E.A. by Grab ## Author: Karsten Eckhardt ### How to use it This script exposes a relatively simple API. The first step is to clone the repository and install all requirements. ``` git clone https://github.com/R4h4/AIforSEA_Traffic_Management.git cd AIforSEA_Traffic_Management pip install -r requirements.txt ``` Next step is to produce the actual prediction: | 877 |
RBrossard/GINEPLUS | ['graph property prediction'] | ['Graph convolutions that can finally model local structure'] | dataset.py stats.py operations.py classifier.py train_classifier.py Classifier parse_combined_name CombinedOGBDataset CombinedOGBEvaluator CustomSplitOGB ClassifierNetwork VNAgg MLP GlobalPool new make_multihop_edges NAIVEGINEPLUS GINEPLUS NodeEmbedding OGBMolEmbedding ConvBlock get_test_valid main append split arange zeros_like fill_ coalesce new edge_index empty_like matmul coo stack repeat num_nodes num_edges range cat SparseTensor collect test Trainer val_dataloader load_from_checkpoint test_dataloader resume_from_checkpoint format LayerSummary Classifier num_parameters print name strftime test TrainsLogger ModelCheckpoint dataset load_from_checkpoint from_argparse_args fit | This is a PyTorch implementation of our paper: [Graph convolutions that can finally model local structure - Rémy Brossard, Oriel Frigo, David Dehaene (2020)](https://arxiv.org/abs/2011.15069) # Installation ``` git clone https://github.com/RBrossard/GINEPLUS.git cd GINEPLUS conda env create --name myenv -f environment.yml conda activate myenv ``` # Results on ogbg-molpcba | 878 |
RElbers/region-mutual-information-pytorch | ['semantic segmentation'] | ['Region Mutual Information Loss for Semantic Segmentation'] | rmi/rmi.py setup.py rmi/__init__.py log_trace transpose RMILoss inverse log_det diagonal cholesky | RElbers/region-mutual-information-pytorch | 879 |
RQuispeC/top-dropblock | ['person re identification'] | ['Top-DB-Net: Top DropBlock for Activation Enhancement in Person Re-Identification'] | torchreid/data/datasets/dataset.py torchreid/data/transforms.py torchreid/data/datasets/video/ilidsvid.py torchreid/data/datasets/image/__init__.py torchreid/utils/__init__.py torchreid/models/senet.py torchreid/models/shufflenetv2.py torchreid/data/datasets/video/__init__.py torchreid/utils/model_complexity.py torchreid/data/datasets/image/cuhk03.py torchreid/utils/reidtools.py torchreid/data/datasets/video/prid2011.py torchreid/engine/video/__init__.py torchreid/__init__.py torchreid/data/datasets/image/ilids.py torchreid/models/mlfn.py torchreid/models/resnet.py torchreid/metrics/rank_cylib/setup.py torchreid/data/datasets/image/grid.py torchreid/data/datasets/image/sensereid.py torchreid/losses/separation_loss.py torchreid/models/inceptionresnetv2.py torchreid/models/shufflenet.py torchreid/optim/optimizer.py torchreid/utils/loggers.py torchreid/models/__init__.py torchreid/engine/video/softmax.py torchreid/data/datasets/image/dukemtmcreid.py torchreid/data/sampler.py torchreid/models/xception.py torchreid/engine/__init__.py torchreid/engine/video/triplet.py torchreid/models/mobilenetv2.py torchreid/optim/__init__.py torchreid/losses/cross_entropy_loss.py torchreid/data/datasets/image/market1501.py torchreid/models/densenet.py torchreid/models/bdnet.py torchreid/models/hacnn.py torchreid/metrics/__init__.py torchreid/optim/lr_scheduler.py torchreid/metrics/accuracy.py torchreid/utils/avgmeter.py torchreid/data/datamanager.py torchreid/models/inceptionv4.py torchreid/data/datasets/video/mars.py torchreid/data/__init__.py main.py torchreid/data/datasets/__init__.py torchreid/engine/image/triplet.py torchreid/engine/image/triplet_dropbatch_dropbotfeatures.py torchreid/engine/engine.py torchreid/data/datasets/image/viper.py torchreid/losses/__init__.py torchreid/metrics/distance.py torchreid/models/pcb.py torchreid/data/datasets/image/cuhk01.py torchreid/models/osnet.py default_config.py torchreid/data/datasets/video/dukemtmcvidreid.py torchreid/engine/image/softmax.py torchreid/models/mudeep.py torchreid/metrics/rank.py torchreid/utils/rerank.py torchreid/models/squeezenet.py torchreid/losses/npairs_loss.py torchreid/utils/tools.py torchreid/utils/torchtools.py torchreid/engine/image/triplet_dropbatch.py torchreid/models/resnetmid.py torchreid/metrics/rank_cylib/test_cython.py torchreid/engine/image/__init__.py torchreid/models/nasnet.py torchreid/data/datasets/image/prid.py torchreid/losses/hard_mine_triplet_loss.py torchreid/data/datasets/image/msmt17.py engine_run_kwargs optimizer_kwargs videodata_kwargs get_default_config imagedata_kwargs lr_scheduler_kwargs main build_datamanager build_engine reset_config ImageDataManager VideoDataManager DataManager RandomIdentitySampler build_train_sampler RandomIdentitySamplerFast RandomErasing build_transforms Random2DTranslation ColorAugmentation ImageDataset Dataset VideoDataset register_video_dataset init_image_dataset register_image_dataset init_video_dataset CUHK01 CUHK03 DukeMTMCreID GRID iLIDS Market1501 MSMT17 PRID SenseReID VIPeR DukeMTMCVidReID iLIDSVID Mars PRID2011 Engine ImageSoftmaxEngine ImageTripletEngine ImageTripletDropBatchEngine ImageTripletDropBatchDropBotFeaturesEngine VideoSoftmaxEngine VideoTripletEngine CrossEntropyLoss TripletLoss NPairsLoss SeparationLoss DeepSupervision accuracy cosine_distance euclidean_squared_distance compute_distance_matrix eval_market1501 evaluate_py eval_cuhk03 evaluate_rank numpy_include TopBDNet top_bdnet_neck_doubot BatchFeatureErase_Basic nodropnet nodropnet_neck BatchFeatureErase_Top BatchDrop top_bdnet_neck_botdropfeat_doubot bdnet_neck top_bdnet_doubot NoDropNet bdnet top_bdnet_botdropfeat_doubot BatchDropTop densenet161 DenseNet densenet169 densenet201 init_pretrained_weights densenet121_fc512 _DenseLayer _DenseBlock _Transition densenet121 HACNN InceptionB ChannelAttn SoftAttn SpatialAttn HarmAttn HardAttn InceptionA ConvBlock Block17 Block8 Mixed_6a Mixed_5b BasicConv2d InceptionResNetV2 inceptionresnetv2 Block35 Mixed_7a Mixed_4a Mixed_5a Reduction_B Inception_B init_pretrained_weights BasicConv2d Inception_A Reduction_A Mixed_3a Inception_C inceptionv4 InceptionV4 MLFNBlock mlfn MLFN init_pretrained_weights mobilenetv2_x1_0 init_pretrained_weights Bottleneck mobilenetv2_x1_4 ConvBlock MobileNetV2 Reduction ConvLayers MultiScaleB MultiScaleA MuDeep Fusion ConvBlock NormalCell BranchSeparablesStem AvgPoolPad ReductionCell1 ReductionCell0 MaxPoolPad init_pretrained_weights nasnetamobile SeparableConv2d BranchSeparables CellStem0 FirstCell BranchSeparablesReduction CellStem1 NASNetAMobile Conv1x1 osnet_x0_5 osnet_x1_0 OSNet init_pretrained_weights Conv3x3 Conv1x1Linear ConvLayer ChannelGate osnet_x0_25 LightConv3x3 osnet_ibn_x1_0 OSBlock osnet_x0_75 pcb_p6 init_pretrained_weights Bottleneck pcb_p4 conv3x3 PCB BasicBlock DimReduceLayer conv1x1 resnext50_32x4d ResNet resnet50 init_pretrained_weights resnext101_32x8d Bottleneck resnet152 resnet50_fc512 conv3x3 resnet34 resnet18 BasicBlock resnet50_ls resnet101 ResNetMid resnet50mid init_pretrained_weights Bottleneck conv3x3 BasicBlock se_resnext50_32x4d senet154 SENet SEResNetBottleneck SEBottleneck SEResNeXtBottleneck se_resnet50_fc512 init_pretrained_weights Bottleneck se_resnet152 se_resnet50 se_resnext101_32x4d SEModule se_resnet101 shufflenet ShuffleNet init_pretrained_weights Bottleneck ChannelShuffle shufflenet_v2_x2_0 shufflenet_v2_x1_5 InvertedResidual init_pretrained_weights shufflenet_v2_x1_0 channel_shuffle shufflenet_v2_x0_5 ShuffleNetV2 SqueezeNet squeezenet1_1 squeezenet1_0_fc512 init_pretrained_weights Fire squeezenet1_0 Block init_pretrained_weights xception SeparableConv2d Xception show_avai_models build_model warmup_sb warmup_db build_lr_scheduler build_optimizer AverageMeter RankLogger Logger 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 visualize_ranked_activation_results visualize_ranked_mask_activation_results visualize_ranked_results visualize_ranked_threshold_activation_results re_ranking check_isfile collect_env_info read_json download_url set_random_seed write_json read_image mkdir_if_missing resume_from_checkpoint load_checkpoint set_bn_to_eval count_num_param load_pretrained_weights save_checkpoint adjust_learning_rate open_all_layers open_specified_layers CN ImageTripletEngine VideoTripletEngine VideoSoftmaxEngine exit ImageSoftmaxEngine ImageTripletDropBatchEngine ImageTripletDropBatchDropBotFeaturesEngine root transforms sources targets resume_from_checkpoint set_random_seed get_default_config gpu_devices ArgumentParser Logger opts save_dir cuda run seed build_datamanager build_lr_scheduler collect_env_info name build_optimizer freeze parse_args build_engine merge_from_file use_gpu format build_model compute_model_complexity config_file merge_from_list load_pretrained_weights load_weights resume is_available type join print add_argument reset_config RandomIdentitySampler RandomIdentitySamplerFast RandomSampler int format isinstance print Compose Normalize round list keys list keys list keys list keys topk isinstance size t eq mul_ expand_as append sum max cosine_distance euclidean_squared_distance t addmm_ expand t normalize mm cumsum list defaultdict shape append sum range format asarray astype choice mean enumerate invert items print float32 argsort int32 zeros len invert format asarray print cumsum astype float32 argsort shape mean int32 append sum range get_include TopBDNet TopBDNet TopBDNet TopBDNet TopBDNet TopBDNet NoDropNet NoDropNet update list group load_url match load_state_dict keys compile state_dict init_pretrained_weights DenseNet init_pretrained_weights DenseNet init_pretrained_weights DenseNet init_pretrained_weights DenseNet init_pretrained_weights DenseNet InceptionResNetV2 load_imagenet_weights init_pretrained_weights InceptionV4 MLFN format warn format warn MobileNetV2 format warn MobileNetV2 NASNetAMobile init_pretrained_weights load join items format print warn _get_torch_home OrderedDict startswith append download makedirs OSNet init_pretrained_weights OSNet init_pretrained_weights OSNet init_pretrained_weights OSNet init_pretrained_weights OSNet init_pretrained_weights PCB init_pretrained_weights PCB init_pretrained_weights ResNet init_pretrained_weights ResNet init_pretrained_weights ResNet init_pretrained_weights ResNet init_pretrained_weights ResNet init_pretrained_weights ResNet init_pretrained_weights ResNet init_pretrained_weights ResNet init_pretrained_weights ResNet init_pretrained_weights ResNetMid init_pretrained_weights init_pretrained_weights SENet init_pretrained_weights SENet init_pretrained_weights SENet init_pretrained_weights SENet init_pretrained_weights SENet init_pretrained_weights SENet init_pretrained_weights SENet format ShuffleNet warn size view contiguous ShuffleNetV2 init_pretrained_weights ShuffleNetV2 init_pretrained_weights ShuffleNetV2 init_pretrained_weights ShuffleNetV2 init_pretrained_weights SqueezeNet init_pretrained_weights SqueezeNet init_pretrained_weights SqueezeNet init_pretrained_weights init_pretrained_weights Xception print list keys list keys CosineAnnealingLR StepLR isinstance LambdaLR MultiStepLR float named_children isinstance Adam warn RMSprop SGD parameters DataParallel append module 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 join BORDER_CONSTANT format basename imwrite copyMakeBorder _cp_img_to print ones argsort shape resize imread range mkdir_if_missing imwrite floor resize basename copyMakeBorder ones shape COLORMAP_JET imread range format astype mkdir_if_missing join uint8 print applyColorMap argsort BORDER_CONSTANT numpy imwrite zeros_like floor resize basename copyMakeBorder ones shape COLORMAP_JET imread range format astype mkdir_if_missing join uint8 print applyColorMap COLORMAP_BONE argsort BORDER_CONSTANT numpy imwrite floor resize basename copyMakeBorder ones shape COLORMAP_JET imread range format astype mkdir_if_missing join uint8 print applyColorMap COLORMAP_BONE argsort BORDER_CONSTANT numpy minimum exp zeros_like concatenate transpose astype float32 mean int32 unique append zeros sum max range len makedirs format warn isfile dirname mkdir_if_missing seed manual_seed_all manual_seed print format write urlretrieve convert get_pretty_env_info items list join str format print copy OrderedDict dirname startswith save mkdir_if_missing load print load_checkpoint format load_state_dict param_groups eval __name__ parameters train named_children isinstance parameters eval DataParallel train module isinstance warn DataParallel sum module update items list format print load_checkpoint warn OrderedDict load_state_dict startswith append state_dict | [](https://arxiv.org/abs/2010.05435) [](https://paperswithcode.com/sota/person-re-identification-on-cuhk03-detected?p=top-db-net-top-dropblock-for-activation) [](https://paperswithcode.com/sota/person-re-identification-on-cuhk03-labeled?p=top-db-net-top-dropblock-for-activation) [](https://paperswithcode.com/sota/person-re-identification-on-dukemtmc-reid?p=top-db-net-top-dropblock-for-activation) [](https://paperswithcode.com/sota/person-re-identification-on-market-1501?p=top-db-net-top-dropblock-for-activation) Top-DB-Net: Top DropBlock for Activation Enhancement in Person Re-Identification =========== This repository implements 'Top-DB-Net: Top DropBlock for Activation Enhancement in Person Re-Identification' presented at International Conference in Pattern Recognition (ICPR 2020)  ## Installation | 880 |
RRoundTable/EEN-with-Keras | ['video prediction'] | ['Prediction Under Uncertainty with Error-Encoding Networks'] | train.py makeGIF.py dataloaders/data_driving.py dataloaders/data_bird.py visualize.py dataloaders/data_poke.py utils.py dataloaders/data_atari.py model.py img_file_to_gif DeterministicModel MultiInputLayer f_network_decoder g_network_decoder f_network_encoder g_network_encoder LatentResidualModel3Layer phi_network_fc encoder_latent BaselineModel3Layer phi_network_conv named_logs train_epoch test_epoch write_log train read_config log plot_seq ImageLoader ImageLoader ImageLoader ImageLoader mimsave nfeature Sequential add ZeroPadding2D ReLU Conv2D BatchNormalization nfeature Conv2DTranspose n_out Sequential add ZeroPadding2D ReLU BatchNormalization nfeature Sequential add ZeroPadding2D ReLU Conv2D BatchNormalization Sequential add Dense ReLU BatchNormalization n_latent nfeature Sequential add ZeroPadding2D ReLU Conv2D BatchNormalization nfeature Conv2DTranspose n_out Sequential add ZeroPadding2D ReLU BatchNormalization nfeature Dense Sequential add zip Summary zip add add_summary flush get_batch range get_batch range model_filename_g int on_epoch_end format named_logs print epoch_size train_epoch test_epoch save_weights append range model_filename_f str write system now close dirname open load open subplots set_title transpose imshow scatter savefig | ## Introduction This is an unofficial implementation of Error Encoding Networks which is originally developed by Facebook AI Research using Keras. To alleviate the necessity of GPU resources, the smaller network is used by reducing the size of an input image. ## Thanks to This project is independently sponsored by [EpiSys Science](http://episci-inc.com/) which research is mainly focused on **uncertainty detection** in deep learning. ## Error Encoding Network This [paper](https://arxiv.org/pdf/1711.04994.pdf) is branched on a simple idea which is disentangled components of the predictable future state. As a result, it is able to consistently generate diverse predictions without minimizing the alternating latent space or adversarial training. ## Model structure The model is trained to alternate minimizing latent variable model. | 881 |
RYoungJ/ZO-L2L | ['adversarial attack'] | ['Learning to Learn by Zeroth-Order Oracle'] | nn_optimizer/__init__.py nn_optimizer/basezoopt.py main_attack.py train_task_list.py nn_optimizer/zoopt.py optimizee/mnist.py optimizee/__init__.py main cycle optimizer_train_optimizee_attack train_optimizer_attack SignZOOptimizer BaseZOOptimizer AdamZOOptimizer ZOOptimizer VarReducedZOOptimizer NNOptimizer MnistAttack MnistLinearModel MnistModel MnistConvModel CustomLoss AttackModel Optimizee MetaModel data model meta_update meta_model zero_grad clamp_ nondiff_loss save output_dir cuda hasattr data_dir step MetaModel Adam load_state_dict iter gpu_num AttackModel next double range format reset_state eval dataset_loader float __name__ load truncated_bptt_step requires_grad backward print custom_loss named_parameters cycle parameters reset updates_per_epoch zeros train weight loss arange grad_est_q output_dir ckpt_path save cuda std data_dir MetaModel ylabel savefig load_state_dict iter gpu_num append legend AttackModel next range state_dict format plot reset_state save_loss mean eval item dataset_loader load join deepcopy print reshape xlabel fig_preffix reset figure fill_between array save_fig float64 optimizer_train_optimizee_attack set_default_dtype train_optimizer_attack | # Zeroth-Order Learning to Learn This repository contains the code for [Learning to Learn by Zeroth-Order Oracle](https://openreview.net/forum?id=ryxz8CVYDH), which extends the learning to learn (L2L) framework to zeroth-order (ZO) optimization. ## Requirements * Python >= 3.6 * PyTorch >= 1.1.0 * Pillow == 6.1.0 * matplotlib ## Usage We include the MNIST attack experiment here. ### Train the ZO optimizer | 882 |
RadomirPopovicFON/Joint-Multiclass-Debiasing-of-Word-Embeddings | ['word embeddings'] | ['Joint Multiclass Debiasing of Word Embeddings'] | Utils/methods.py Evaluation/code_evaluation.py Evaluation/weat_analysis.py Evaluation/sentiment_analysis.py Classes/weat.py Utils/sets.py Classes/embedding.py Word_Embedding WEAT emb_to_gensim mikolov_evaluation rank_evaluation Keras_Model compare_embeddings generate_centroid scale_bias weat_analysis average_bias_value nullspace make_translation_matrix neutralize_vectors get_SW_dict get_dataset_and_dicts filter_emb change_the_sentences null_intersection make_vectors_equidistant union_of_dictionaries get_combinations get_hardweat_sets get_sets get_def_seeds get_sent_analysis_sets add GensimPairs range evaluate_word_analogies round datapath len items sort index open append float round enumerate split items list print words fit get_value predict set confusion_matrix range append zeros Keras_Model keys enumerate compile set_weights append abs round mean get_stats cs print average_bias_value append abs array list sum values append dot reshape transpose deepcopy items nullspace cos pi sin range enumerate len svd T atleast_2d sum max add set round abs enumerate enumerate eye len fit_on_texts permutation texts_to_sequences LabelEncoder sentiment pad_sequences append train_test_split fit_transform range Tokenizer review set filter_emb reindex print word_index index transform null_intersection read_csv len deepcopy print append max enumerate list keys enumerate len append intersection enumerate len append split items list print min len append combinations range len | # Joint-Multiclass-Debiasing-of-Word-Embeddings This repository contains code for the paper: *Joint Multiclass Debiasing of Word Embeddings*, accepted for *25th International Symposium on Intelligent Systems (ISMIS 2020), Graz, Austria, September 2020*. *Paper (arxiv.org version) can be found at https://arxiv.org/abs/2003.11520.* ## Description Word Embedding, as an important tool for numerous downstream NLP tasks, can contain different kinds of biases, based on gender, religion, race. In this direction, by extending work from [Bolukbasi et al.](https://papers.nips.cc/paper/6228-man-is-to-computer-programmer-as-woman-is-to-homemaker-debiasing-word-embeddings.pdf) and [Caliskan et al.](https://science.sciencemag.org/content/356/6334/183) HardWEAT and SoftWEAT are created with an aim to reduce this phenomenon simultaneously/jointly on multiple bias classes/categories. Former completely eliminates bias measured with WEAT, while latter provides an user with a choice to what extent debiasing procedure will occur. Experiments show that the two methods are able to both decrease bias levels while minimizing the structure modification of vector representation. In addition, debiasing of Word Embeddings, translates to variance decline of polarity scores within the task of Sentiment Analysis.  Here, samples of [Word2Vec](https://drive.google.com/uc?id=0B7XkCwpI5KDYNlNUTTlSS21pQmM), [GloVe](https://nlp.stanford.edu/projects/glove/), [FastText](https://fasttext.cc/docs/en/english-vectors.html) Embeddings are used, containing only words having [frequency of 200 or more within the English Wikipedia](https://github.com/PrincetonML/SIF/blob/master/auxiliary_data/enwiki_vocab_min200.txt), along with [highly polarizing IMDB Movie Dataset](https://www.aclweb.org/anthology/P11-1015/). On these datasets, HardWEAT and SoftWEAT are examined via WEAT bias experiments, Mikolov analogy, Rank Similarity and Sentiment Analysis tasks (see [Main.ipynb](https://github.com/RadomirPopovicFON/Joint-Multiclass-Debiasing-of-Word-Embeddings/blob/master/Main.ipynb)). Furthermore, corresponding [online appendix](https://github.com/RadomirPopovicFON/Joint-Multiclass-Debiasing-of-Word-Embeddings/blob/master/Online%20Appendix.pdf) for the paper is provided. ## Installation For running [Main.ipynb](https://github.com/RadomirPopovicFON/Joint-Multiclass-Debiasing-of-Word-Embeddings/blob/master/Main.ipynb), [python environment](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/) can be created using libraries listed in [requirements.txt](https://github.com/RadomirPopovicFON/Joint-Multiclass-Debiasing-of-Word-Embeddings/blob/master/requirements.txt) file. Project was done by in Python 3.6. | 883 |
RaghavendraCh/RelationExtraction_keras | ['medical relation extraction', 'relation extraction'] | ['A hybrid deep learning approach for medical relation extraction'] | preprocessingscript.py bidirectional_lstm_rel.py bidirectional_lstm_ner.py pos_features.py lstm_model_creator.py read_CRFFeaturesFile_i2b22010_rel_onemodel.py main test_model build_model main test_model build_model get_bilstm_model get_birnn_model get_features_genericlist get_suf_features get_posseq_relationphrase_features getEmbeddingOfSequence get_assertion_semantic_classes_relationphrase_features get_pos_features posTagsToString get_pmi_relationphrase_features get_gazetter_features get_chunk_features convertWordIndexesToEmbeddings word_features get_other_relationphrase_features get_pre_features get_wordlevel_features print get_bilstm_model num_hidden_layers fit Model xrange save modelfile Input compile merge postProcessing_triword_adding_pretrainedemb_and_features_dir load_model modelfile model readWord2Vec loadXYfrompickle ArgumentParser tagset test_model read_featuresfile_triword_adding_pretrainedemb_and_features parse_args build_model train_crfFeaturesFile train readTags embedding_size print add_argument dict numberofclasses loadFeatures2Tokens XYpicklefile len print postProcessing_createTestCRFFeaturesFile_dir others readTensorsForEachRelationFromCRFFeaturesFile Model Input compile merge Model Input compile merge pos get_suf_features gazetter pre chunk get_pos_features suf get_chunk_features append word_features get_gazetter_features get_pre_features index index index lower range reversed index lower range reversed index lower any isupper append getEmbeddingOfSequence append relation_verbs_words index pos_tag lower conjunction_words append tokenize enumerate TrWP_posseq_words TrIP_posseq_words TrAP_posseq_words TrCP_posseq_words TrNAP_posseq_words pos_tag posTagsToString append tokenize enumerate append lower sorted change_state_words cause_words continue_words evidence_words certainty_words allergy_words append hypothetical_cue_list_words association_words exception_words uncertainty_words experience_words stop_words deny_words def_determiner_words enumerate fail_words decline_words avoid_words instance_words copular_words disappear_words history_words | # Bidirectional LSTM for Medical Relation Extraction task Mining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain. This helps in various applications, such as decision support system, safety surveillance, and new treatment discovery. We build a deep learning approach that utilizes both word level and sentence-level representations to extract the relationships between treatment and problem.  See [the paper](https://arxiv.org/pdf/1806.11189.pdf) for more information. This repository contains python implementations of deep learning using Keras library. # Source * `bidirectional_lstm_ner.py`: deep learning code for medical term identification. </br> * `bidirectional_lstm_rel.py`: deep learning code for relation extraction. </br> * `lstm_model_creator.py`: Contains methods to create LSTM model.</br> * `pos_features.py`: Class to get the word level features.</br> | 884 |
Rahul-Venugopal/Image-augmentation_1 | ['data augmentation', 'image augmentation'] | ['Augmentor: An Image Augmentation Library for Machine Learning'] | checks/check_perspective_transform.py checks/check_bilateral_blur.py checks/check_piecewise_affine.py checks/check_average_blur.py checks/check_imshow.py checks/check_some_of.py checks/check_snowflakes_layer.py imgaug/augmenters/geometric.py checks/check_elastic_transformation.py checks/check_contrast.py setup.py imgaug/external/opensimplex.py checks/check_add_to_hue_and_saturation.py checks/check_seed.py checks/check_affine.py test/augmenters/test_contrast.py checks/check_fast_snowy_landscape.py test/augmenters/test_arithmetic.py test/augmenters/test_weather.py test/run_all.py test/augmenters/test_segmentation.py test/augmenters/test_convolutional.py test/test_imgaug.py test/test_parameters.py checks/check_visually.py imgaug/__init__.py checks/check_withcolorspace.py imgaug/augmenters/size.py imgaug/testutils.py imgaug/augmenters/arithmetic.py checks/check_snowflakes.py test/augmenters/test_flip.py imgaug/parameters.py test/augmenters/test_overlay.py imgaug/augmenters/overlay.py checks/check_single_image_warning.py checks/check_performance.py checks/check_jpeg_compression.py checks/check_channel_shuffle.py generate_documentation_images.py checks/check_background_augmentation.py checks/check_clouds.py imgaug/augmenters/segmentation.py imgaug/imgaug.py imgaug/augmenters/flip.py checks/check_superpixels.py checks/check_rot90.py checks/check_parameters.py checks/check_bb_augmentation.py imgaug/augmenters/weather.py checks/check_noise.py checks/check_affinecv2.py imgaug/augmenters/color.py test/test_readme_examples.py test/augmenters/test_color.py checks/check_laplace_noise.py checks/check_segmentation_maps.py checks/check_withchannels.py test/augmenters/test_size.py checks/check_fog.py checks/check_crop_and_pad.py test/augmenters/test_mixed_files.py imgaug/augmenters/blur.py checks/check_poisson_noise.py imgaug/augmenters/convolutional.py checks/check_directed_edge_detect.py imgaug/augmenters/meta.py test/augmenters/test_geometric.py checks/check_impulse_noise.py checks/check_heatmaps.py checks/check_motion_blur.py checks/check_scale.py test/augmenters/test_blur.py checks/check_fixed_size.py imgaug/augmenters/__init__.py test/augmenters/test_meta.py checks/check_median_blur.py imgaug/augmenters/contrast.py chapter_augmenters_contrastnormalization chapter_examples_segmentation_maps_simple chapter_augmenters_cropandpad grid chapter_augmenters_crop save chapter_augmenters_withcolorspace chapter_examples_keypoints chapter_augmenters_withchannels chapter_examples_heatmaps_arr_small chapter_augmenters_add chapter_examples_bounding_boxes_simple decompress_jpg chapter_parameters chapter_alpha_masks_frequency chapter_augmenters_scale chapter_augmenters_noop chapter_augmenters_invert chapter_examples_keypoints_simple chapter_augmenters_coarsedropout chapter_augmenters_fliplr chapter_examples_segmentation_maps chapter_alpha_constant chapter_augmenters_averageblur checkerboard chapter_augmenters_changecolorspace chapter_examples_heatmaps_padding chapter_augmenters_lambda chapter_alpha chapter_alpha_masks_introduction chapter_augmenters_multiply chapter_examples_segmentation_maps_bool_full chapter_alpha_masks_sigmoid chapter_augmenters_piecewiseaffine chapter_examples_segmentation_maps_array chapter_augmenters_directededgedetect draw_distributions_grid chapter_augmenters_medianblur chapter_examples_heatmaps_simple chapter_alpha_masks_simplex chapter_examples_heatmaps chapter_parameters_discrete chapter_examples_bounding_boxes_rotation chapter_augmenters_pad chapter_alpha_masks_iterative chapter_examples_basics_heavy arrdiff chapter_examples_heatmaps_scaling chapter_examples_segmentation_maps_bool_small main chapter_augmenters_multiplyelementwise chapter_augmenters_elastictransformation chapter_parameters_arithmetic chapter_augmenters_assertlambda chapter_augmenters_dropout chapter_augmenters_addelementwise chapter_augmenters_assertshape compress_to_jpg chapter_examples_heatmaps_arr_full chapter_augmenters chapter_augmenters_superpixels chapter_augmenters_sometimes chapter_augmenters_flipud chapter_augmenters_affine chapter_examples_basics chapter_augmenters_gaussianblur chapter_examples_heatmaps_multiple_full chapter_augmenters_oneof chapter_examples_bounding_boxes_projection chapter_augmenters_grayscale chapter_augmenters_sharpen chapter_augmenters_convolve chapter_examples_bounding_boxes chapter_parameters_continuous run_and_save_augseq chapter_examples_bounding_boxes_shift chapter_augmenters_someof chapter_examples_heatmaps_multiple_small chapter_examples_basics_simple chapter_parameters_introduction chapter_examples_bounding_boxes_iou chapter_augmenters_sequential chapter_parameters_special chapter_augmenters_additivegaussiannoise chapter_examples_bounding_boxes_ooi chapter_augmenters_edgedetect chapter_augmenters_emboss main main main main main load_images main draw_grid main main main main main main main main main main main main main main main main main main main main main main keypoints_draw_on_image main main main main main main main main main to_grid main main main main BoundingBox is_float_array pool is_string pad_to_aspect_ratio HooksKeypoints _interpolate_points caller_name is_np_array KeypointsOnImage copy_random_state SegmentationMapOnImage seed quokka_keypoints Batch BackgroundAugmenter is_integer_array quokka_bounding_boxes imresize_many_images Keypoint is_single_float imshow is_single_bool pad HeatmapsOnImage is_iterable angle_between_vectors is_callable HooksHeatmaps _compute_resized_shape _interpolate_points_by_max_distance derive_random_state quokka_heatmap new_random_state _quokka_normalize_extract show_grid is_generator quokka_square BatchLoader compute_line_intersection_point forward_random_state is_single_integer compute_paddings_for_aspect_ratio do_assert BoundingBoxesOnImage imresize_single_image Polygon _convert_points_to_shapely_line_string draw_grid _interpolate_point_pair dummy_random_state MultiPolygon max_pool is_single_number quokka_segmentation_map draw_text derive_random_states HooksImages compute_geometric_median avg_pool current_random_state quokka Discretize force_np_float_dtype FromLowerResolution Poisson Weibull Choice Negative Normal Sigmoid ForceSign DiscreteUniform Power Uniform Divide Deterministic Add draw_distributions_grid RandomSign Multiply IterativeNoiseAggregator StochasticParameter ChiSquare Absolute show_distributions_grid Binomial both_np_float_if_one_is_float SimplexNoise handle_continuous_param Beta Clip FrequencyNoise handle_discrete_param Subtract handle_probability_param Positive Laplace create_random_keypoints keypoints_equal reseed array_equal_lists create_random_images SaltAndPepper AdditiveLaplaceNoise AdditiveGaussianNoise CoarseSaltAndPepper Add CoarseDropout Pepper MultiplyElementwise Invert JpegCompression Multiply CoarsePepper ContrastNormalization ImpulseNoise AdditivePoissonNoise AddElementwise ReplaceElementwise Salt CoarseSalt Dropout MedianBlur AverageBlur MotionBlur GaussianBlur BilateralBlur Grayscale WithColorspace InColorspace ChangeColorspace AddToHueAndSaturation LinearContrast _ContrastFuncWrapper GammaContrast SigmoidContrast _adjust_linear LogContrast _PreserveDtype Sharpen Emboss EdgeDetect Convolve DirectedEdgeDetect Flipud Fliplr AffineCv2 PerspectiveTransform PiecewiseAffine Affine Rot90 ElasticTransformation invert_reduce_to_nonempty Sequential reduce_to_nonempty restore_augmented_images_dtypes_ WithChannels copy_dtypes_for_restore AssertLambda handle_children_list Noop clip_augmented_images restore_augmented_image_dtype Augmenter restore_augmented_images_dtypes SomeOf clip_augmented_images_ OneOf Lambda shuffle_channels Sometimes restore_augmented_image_dtype_ clip_augmented_image_ AssertShape clip_augmented_image ChannelShuffle FrequencyNoiseAlpha SimplexNoiseAlpha AlphaElementwise Alpha Superpixels _handle_pad_mode_param Pad Crop CropAndPad _handle_position_parameter Scale CropToFixedSize _crop_prevent_zero_size KeepSizeByResize PadToFixedSize Clouds CloudLayer FastSnowyLandscape SnowflakesLayer Fog Snowflakes OpenSimplex floor overflow main test_SegmentationMapOnImage_get_arr_int test_Polygon_exterior_almost_equals test_is_callable test_SegmentationMapOnImage_deepcopy test_derive_random_state test_seed test_Keypoint test_Polygon_is_out_of_image test_Polygon_yy test_quokka_heatmap test_is_float_array test_BoundingBoxesOnImage test_Polygon_change_first_point_by_index test_draw_grid test_derive_random_states test_HeatmapsOnImage_draw test_quokka test_is_single_float test_Polygon_is_partly_within_image test_is_iterable test_Polygon_is_fully_within_image _test_Polygon_repr_str test_compute_paddings_for_aspect_ratio test_pad_to_aspect_ratio test_HeatmapsOnImage_pad test_SegmentationMapOnImage_to_heatmaps test_quokka_keypoints test_Polygon___repr__ test_SegmentationMapOnImage_draw_on_image test_SegmentationMapOnImage_copy test_is_string test_quokka_bounding_boxes test_SegmentationMapOnImage_scale _test_Polygon_cut_clip test_KeypointsOnImage test_Polygon_copy test_SegmentationMapOnImage_from_heatmaps test__interpolate_points test_HeatmapsOnImage_avg_pool test_avg_pool test_HeatmapsOnImage_max_pool test_HeatmapsOnImage_scale test_SegmentationMapOnImage_draw test_Polygon__compute_inside_image_point_mask test_quokka_square test_Polygon_find_closest_point_idx test_dummy_random_state test_HeatmapsOnImage_pad_to_aspect_ratio test_SegmentationMapOnImage_pad_to_aspect_ratio test_Polygon_xx_int test_Polygon_cut_out_of_image test_SegmentationMapOnImage_bool test_Polygon_draw_on_image test_BatchLoader test_Polygon_to_shapely_line_string test_Polygon_shift test_Polygon_deepcopy test__interpolate_point_pair test__quokka_normalize_extract test_Polygon_almost_equals test_Polygon_area test___convert_points_to_shapely_line_string test_imresize_single_image test_Polygon_from_shapely test_Polygon_extract_from_image test_pad test__compute_resized_shape main test_copy_random_state test_HeatmapsOnImage_draw_on_image test_Polygon_xx test_Polygon_clip_out_of_image test_max_pool test_new_random_state test_HeatmapsOnImage_invert test_BoundingBox test_Polygon_to_shapely_polygon test_SegmentationMapOnImage_pad test_current_random_state test_Polygon___init__ test_Polygon_to_bounding_box test_Polygon_is_valid test_Polygon_project test_caller_name test_pool test_compute_line_intersection_point test_is_single_integer test_is_single_bool test_is_np_array test_imresize_many_images test_HeatmapsOnImage_change_normalization test_draw_text test__interpolate_points_by_max_distance test_quokka_segmentation_map test_Polygon_yy_int test_Polygon_change_first_point_by_coords test_is_integer_array test_is_single_number test_Polygon___str__ test_BackgroundAugmenter__augment_images_worker test_forward_random_state test_HeatmapsOnImage_from_uint8 test_parameters_Laplace test_parameters_Choice test_parameters_DiscreteUniform test_parameters_Discretize test_parameters_Poisson test_parameters_handle_probability_param test_parameters_Negative test_parameters_Deterministic test_parameters_FromLowerResolution test_parameters_handle_continuous_param test_parameters_operators test_parameters_ForceSign test_parameters_force_np_float_dtype test_parameters_Uniform test_parameters_draw_distribution_graph test_parameters_Positive test_parameters_Clip test_parameters_copy test_parameters_Biomial test_parameters_both_np_float_if_one_is_float test_parameters_Normal test_parameters_Weibull test_parameters_Beta test_parameters_handle_discrete_param test_parameters_Sigmoid test_parameters_RandomSign main test_parameters_Subtract test_parameters_Absolute test_parameters_ChiSquare test_parameters_Divide test_parameters_IterativeNoiseAggregator test_parameters_Power test_parameters_Multiply test_parameters_Add test_parameters_draw_distribution_grid example_show example_heavy_augmentations example_background_classes example_background_augment_batches example_determinism example_standard_situation example_single_augmenters example_unusual_distributions main example_keypoints example_hooks example_withchannels test_CoarseSalt test_Invert test_Salt test_ContrastNormalization test_JpegCompression test_AdditiveGaussianNoise test_Multiply test_CoarsePepper test_Add test_ReplaceElementwise test_Dropout test_CoarseDropout test_CoarseSaltAndPepper main test_SaltAndPepper test_AddElementwise test_Pepper test_MultiplyElementwise test_AverageBlur test_MotionBlur test_MedianBlur test_GaussianBlur main main test_Grayscale test_AddToHueAndSaturation test_SigmoidContrast test_GammaContrast test_LinearContrast main test_LogContrast test_contrast_adjust_linear test_Convolve main test_Sharpen test_Emboss main test_Flipud test_Fliplr test_ElasticTransformation test_PiecewiseAffine test_AffineCv2 test_Affine test_Rot90 main test_PerspectiveTransform test_restore_augmented_image_dtype_ test_Augmenter_augment_batches test_Lambda test_Augmenter_augment_segmentation_maps test_Sequential test_clip_augmented_image_ test_OneOf test_SomeOf test_AssertShape test_Augmenter_augment_keypoints test_clip_augmented_image test_invert_reduce_to_nonempty test_restore_augmented_images_dtypes_ test_Augmenter test_reduce_to_nonempty test_WithChannels test_AssertLambda test_Noop test_ChannelShuffle test_copy_dtypes_for_restore test_restore_augmented_image_dtype test_Augmenter_remove main test_Sometimes test_2d_inputs test_restore_augmented_images_dtypes test_clip_augmented_images test_Augmenter_copy_random_state test_Augmenter_hooks test_clip_augmented_images_ test_Augmenter_find test_keypoint_augmentation test_dtype_preservation main test_determinism test_unusual_channel_numbers main test_Alpha test_AlphaElementwise main test_Superpixels test__handle_position_parameter test_CropToFixedSize test_Pad test_KeepSizeByResize test_PadToFixedSize main test_Crop test_Scale test_Clouds test_Fog test_Snowflakes main test_FastSnowyLandscape chapter_examples_bounding_boxes chapter_examples_keypoints chapter_examples_basics chapter_examples_segmentation_maps chapter_alpha chapter_parameters chapter_augmenters chapter_examples_heatmaps join decompress_jpg print compress_to_jpg isfile imread makedirs sum shape power abs prod fromarray BytesIO close getvalue save BytesIO write close getvalue imread int set sqrt pad ceil zeros max range len tile chapter_examples_basics_heavy chapter_examples_basics_simple seed Sequential grid save augment_images array seed Sequential grid save augment_images array chapter_examples_keypoints_simple seed print Sequential len grid quokka KeypointsOnImage draw_on_image save keypoints range to_deterministic chapter_examples_bounding_boxes_shift chapter_examples_bounding_boxes_rotation chapter_examples_bounding_boxes_simple chapter_examples_bounding_boxes_iou chapter_examples_bounding_boxes_projection chapter_examples_bounding_boxes_ooi seed BoundingBoxesOnImage print Sequential len grid quokka bounding_boxes draw_on_image save range to_deterministic seed BoundingBoxesOnImage print Sequential len grid quokka bounding_boxes draw_on_image save range to_deterministic seed BoundingBoxesOnImage grid draw_bbs Affine quokka remove_out_of_image cut_out_of_image save to_deterministic seed BoundingBoxesOnImage shift grid draw_on_image save quokka seed BoundingBoxesOnImage imresize_single_image grid on draw_on_image save quokka seed BoundingBox iou grid extend copy draw_text draw_on_image save quokka chapter_examples_heatmaps_padding chapter_examples_heatmaps_multiple_small chapter_examples_heatmaps_scaling chapter_examples_heatmaps_arr_small chapter_examples_heatmaps_simple seed to_deterministic reshape draw_grid Sequential astype float32 HeatmapsOnImage tile zip append save avg_pool range augment_image quokka seed to_deterministic zip draw_grid Sequential HeatmapsOnImage draw_on_image save augment_image append zeros range quokka hstack HeatmapsOnImage draw_on_image save zeros range quokka seed to_deterministic draw_grid Sequential get_arr HeatmapsOnImage save zip append zeros range augment_image quokka hstack HeatmapsOnImage save zeros get_arr quokka draw_grid draw_heatmaps HeatmapsOnImage save zeros augment_image draw_grid HeatmapsOnImage save zeros quokka chapter_examples_segmentation_maps_bool_small chapter_examples_segmentation_maps_simple chapter_examples_segmentation_maps_array seed to_deterministic draw_grid Sequential draw draw_on_image save zip append zeros SegmentationMapOnImage range augment_image quokka seed to_deterministic draw_grid Sequential draw draw_on_image save zip append zeros SegmentationMapOnImage range augment_image quokka draw_grid save zeros SegmentationMapOnImage quokka draw_grid get_arr_int save zeros SegmentationMapOnImage quokka seed grid save chapter_augmenters_flipud chapter_augmenters_contrastnormalization chapter_augmenters_affine chapter_augmenters_gaussianblur chapter_augmenters_cropandpad chapter_augmenters_oneof chapter_augmenters_scale chapter_augmenters_crop chapter_augmenters_grayscale chapter_augmenters_noop chapter_augmenters_sharpen chapter_augmenters_emboss chapter_augmenters_piecewiseaffine chapter_augmenters_convolve chapter_augmenters_withcolorspace chapter_augmenters_directededgedetect chapter_augmenters_withchannels chapter_augmenters_multiply chapter_augmenters_invert chapter_augmenters_medianblur chapter_augmenters_add chapter_augmenters_coarsedropout chapter_augmenters_pad chapter_augmenters_fliplr chapter_augmenters_someof chapter_augmenters_multiplyelementwise chapter_augmenters_elastictransformation chapter_augmenters_averageblur chapter_augmenters_assertlambda chapter_augmenters_dropout chapter_augmenters_changecolorspace chapter_augmenters_addelementwise chapter_augmenters_lambda chapter_augmenters_assertshape chapter_augmenters_sequential chapter_augmenters_superpixels chapter_augmenters_additivegaussiannoise chapter_augmenters_edgedetect chapter_augmenters_sometimes Sequential run_and_save_augseq run_and_save_augseq SomeOf OneOf run_and_save_augseq GaussianBlur Sequential run_and_save_augseq Sometimes run_and_save_augseq WithColorspace Affine run_and_save_augseq WithChannels Add Noop run_and_save_augseq Lambda run_and_save_augseq run_and_save_augseq Scale run_and_save_augseq CropAndPad run_and_save_augseq Fliplr Flipud run_and_save_augseq run_and_save_augseq Superpixels linspace Sequential run_and_save_augseq Grayscale run_and_save_augseq linspace GaussianBlur run_and_save_augseq run_and_save_augseq AverageBlur MedianBlur run_and_save_augseq run_and_save_augseq Convolve array Sharpen run_and_save_augseq linspace Emboss run_and_save_augseq linspace run_and_save_augseq EdgeDetect linspace DirectedEdgeDetect run_and_save_augseq linspace run_and_save_augseq Add run_and_save_augseq AddElementwise AdditiveGaussianNoise run_and_save_augseq Multiply run_and_save_augseq run_and_save_augseq MultiplyElementwise run_and_save_augseq Dropout CoarseDropout run_and_save_augseq Invert run_and_save_augseq run_and_save_augseq ContrastNormalization run_and_save_augseq Affine PiecewiseAffine run_and_save_augseq linspace run_and_save_augseq linspace ElasticTransformation chapter_parameters_discrete chapter_parameters_continuous chapter_parameters_introduction chapter_parameters_special chapter_parameters_arithmetic seed grid Sequential save array seed draw_distributions_grid save seed draw_distributions_grid save seed draw_distributions_grid save seed draw_distributions_grid save chapter_alpha_masks_sigmoid chapter_alpha_masks_iterative chapter_alpha_constant chapter_alpha_masks_introduction chapter_alpha_masks_frequency chapter_alpha_masks_simplex seed grid extend save augment_images array seed grid Alpha save augment_images array seed uint8 grid SimplexNoiseAlpha hstack astype save tile augment_images array seed uint8 grid astype pad FrequencyNoiseAlpha draw_samples linspace save tile draw_text augment_images vstack array range append enumerate seed uint8 hstack astype grid pad draw_text IterativeNoiseAggregator vstack tile save append draw_samples enumerate seed uint8 create_for_noise hstack astype grid pad draw_text vstack linspace tile save append draw_samples range enumerate to_deterministic print name draw_grid shape imshow pad on draw_on_image append range augment_image quokka arange namedWindow waitKey astronaut draw_text AddToHueAndSaturation augment_images WINDOW_NORMAL BoundingBox imwrite KeypointsOnImage vstack copy_random_state_ hstack copy set zip BoundingBoxesOnImage int imresize_single_image resizeWindow AverageBlur keypoints_aug _augment_small_2 list get_batch _augment_small_1 BackgroundAugmenter Sequential augment_batches load_images _augment_small_3 images_aug _augment_small_4 BatchLoader imresize_single_image Batch astronaut draw_text KeypointsOnImage sleep append range extend draw_on_image zip append range len Affine BilateralBlur ChannelShuffle imread uint8 add_argument imresize_many_images ArgumentParser parse_args min cos deg2rad DirectedEdgeDetect cycle sin array ElasticTransformation PiecewiseAffine PerspectiveTransform CropAndPad Scale HeatmapsOnImage Alpha zeros astype enumerate MedianBlur MotionBlur draw_samples time format augment_keypoints Keypoint xrange randint dstack keypoints_draw_on_image min copy pad keypoints max quokka_keypoints quokka_heatmap augment_heatmaps camera show_grid seed Fliplr SegmentationMapOnImage z Noop extend to_grid SomeOf hstack draw_on_image zip append range len reversed Superpixels augment_bounding_boxes Sometimes tile WithChannels WithColorspace Add get_state dummy_random_state set_state uniform BoundingBox do_assert isinstance int list do_assert all is_single_integer isinstance is_np_array shape append round enumerate imresize_single_image _compute_resized_shape extract_from_image _quokka_normalize_extract shape imread imresize_single_image extract_from_image _compute_resized_shape astype float32 _quokka_normalize_extract shape imread tuple extract_from_image _compute_resized_shape polygon _quokka_normalize_extract shape scale append zeros SegmentationMapOnImage array y1 _compute_resized_shape Keypoint x1 _quokka_normalize_extract KeypointsOnImage on append BoundingBoxesOnImage BoundingBox y1 _compute_resized_shape x1 _quokka_normalize_extract on append norm _make_line fromarray dtype do_assert uint8 truetype Draw asarray setflags text astype dtype do_assert all isinstance INTER_AREA astype set is_single_number shape INTER_CUBIC xrange resize INTER_NEAREST INTER_LINEAR zeros len shape do_assert imresize_many_images append do_assert ceil int do_assert floor pad compute_paddings_for_aspect_ratio dtype list do_assert is_single_integer tuple astype block_reduce int do_assert is_np_array set sqrt xrange ceil zeros max draw_grid imshow show do_assert namedWindow waitKey destroyWindow set_window_title WINDOW_NORMAL norm cdist min mean sum max len append _interpolate_points float32 list _interpolate_point_pair extend zip append int list do_assert _interpolate_point_pair extend sqrt zip append do_assert check_value_range all isinstance is_single_number check_value_range do_assert all isinstance is_single_number do_assert all isinstance draw_grid array imresize_many_images len draw_distributions_grid imshow Keypoint KeypointsOnImage xrange append randint do_assert isinstance zip keypoints xrange len seed handle_continuous_param handle_continuous_param handle_continuous_param do_assert isinstance Binomial is_single_number StochasticParameter Uniform is_iterable do_assert FromLowerResolution isinstance Binomial is_single_number StochasticParameter Uniform is_iterable Beta FromLowerResolution handle_probability_param Beta ForceSign Beta FromLowerResolution handle_probability_param ForceSign Beta ForceSign Beta FromLowerResolution handle_probability_param ForceSign handle_continuous_param warn adjust_gamma _PreserveDtype _PreserveDtype adjust_sigmoid adjust_log _PreserveDtype dtype clip_augmented_image_ float64 astype restore_augmented_image_dtype_ handle_continuous_param handle_continuous_param handle_continuous_param handle_continuous_param is_np_array is_np_array copy is_np_array is_np_array copy is_np_array all do_assert is_iterable isinstance append do_assert hasattr enumerate list zip do_assert permutation arange zip create_for_noise Choice SimplexNoise Normal IterativeNoiseAggregator create_for_noise Choice FrequencyNoise Normal IterativeNoiseAggregator do_assert all isinstance is_string StochasticParameter do_assert abs str do_assert all isinstance StochasticParameter CropAndPad recursive_validate recursive_negate CropAndPad SnowflakesLayer test_SegmentationMapOnImage_get_arr_int test_is_callable test_SegmentationMapOnImage_deepcopy test_derive_random_state test_seed test_Keypoint test_Polygon_is_out_of_image test_Polygon_yy test_quokka_heatmap test_is_float_array test_BoundingBoxesOnImage test_draw_grid test_derive_random_states test_HeatmapsOnImage_draw test_quokka test_is_single_float test_Polygon_is_partly_within_image test_is_iterable test_Polygon_is_fully_within_image test_compute_paddings_for_aspect_ratio test_pad_to_aspect_ratio test_HeatmapsOnImage_pad test_SegmentationMapOnImage_to_heatmaps test_quokka_keypoints test_Polygon___repr__ test_SegmentationMapOnImage_draw_on_image test_SegmentationMapOnImage_copy test_is_string test_quokka_bounding_boxes test_SegmentationMapOnImage_scale test_KeypointsOnImage test_Polygon_copy test_SegmentationMapOnImage_from_heatmaps test__interpolate_points test_HeatmapsOnImage_avg_pool test_avg_pool test_HeatmapsOnImage_max_pool test_HeatmapsOnImage_scale test_SegmentationMapOnImage_draw test_Polygon__compute_inside_image_point_mask test_quokka_square test_Polygon_find_closest_point_idx test_dummy_random_state test_HeatmapsOnImage_pad_to_aspect_ratio test_SegmentationMapOnImage_pad_to_aspect_ratio test_Polygon_xx_int test_Polygon_cut_out_of_image test_SegmentationMapOnImage_bool test_Polygon_draw_on_image test_BatchLoader test_Polygon_shift test_Polygon_deepcopy test__interpolate_point_pair test__quokka_normalize_extract test_Polygon_area test___convert_points_to_shapely_line_string test_imresize_single_image test_Polygon_from_shapely test_Polygon_extract_from_image test_pad test__compute_resized_shape test_copy_random_state test_HeatmapsOnImage_draw_on_image test_Polygon_xx test_Polygon_clip_out_of_image test_max_pool test_new_random_state test_HeatmapsOnImage_invert test_BoundingBox test_Polygon_to_shapely_polygon test_SegmentationMapOnImage_pad test_current_random_state test_Polygon___init__ test_Polygon_to_bounding_box test_Polygon_is_valid test_Polygon_project test_caller_name test_pool test_compute_line_intersection_point test_is_single_integer test_is_single_bool test_is_np_array test_imresize_many_images test_HeatmapsOnImage_change_normalization test_draw_text test__interpolate_points_by_max_distance test_quokka_segmentation_map test_Polygon_yy_int test_is_integer_array test_is_single_number test_Polygon___str__ test_BackgroundAugmenter__augment_images_worker test_forward_random_state test_HeatmapsOnImage_from_uint8 seed reseed RandomState seed new_random_state RandomState RandomState copy_random_state randint RandomState derive_random_state randint RandomState derive_random_states forward_random_state uniform RandomState BoundingBoxesOnImage _quokka_normalize_extract BoundingBox zeros _compute_resized_shape quokka quokka_square quokka_heatmap quokka_segmentation_map BoundingBox y quokka_keypoints extract_from_image sqrt zip append keypoints x quokka center_y center_x extract_from_image quokka_bounding_boxes sqrt bounding_boxes zip append quokka compute_line_intersection_point BoundingBox draw_text zeros sum max range sum uint8 size imresize_many_images astype pad int32 zip zeros abs sum uint8 imresize_single_image size astype pad int32 zip zeros abs zeros pad zeros compute_paddings_for_aspect_ratio zeros pad_to_aspect_ratio uint8 pool float32 average int32 tile max uint8 avg_pool uint8 max_pool uint8 draw_grid hstack vstack zeros generate_similar_points_manhattan shift Keypoint project to_distance_maps zeros_like get_coords_array to_keypoint_image on KeypointsOnImage ones_like from_distance_maps concatenate from_keypoint_image copy sqrt draw_on_image deepcopy shift float32 divide from_coords_array zeros BoundingBox zeros_like randint y2 project x1 x2 pad intersection union eps y1 to_keypoints astype copy draw_on_image deepcopy iou extract_from_image shift extend float32 cut_out_of_image zeros BoundingBoxesOnImage BoundingBox from_xyxy_array eps deepcopy reseed shift float32 to_xyxy_array copy remove_out_of_image shape cut_out_of_image on int32 draw_on_image zeros HeatmapsOnImage range float32 HeatmapsOnImage uint8 float32 tile HeatmapsOnImage float32 HeatmapsOnImage pad float32 HeatmapsOnImage float32 pad_to_aspect_ratio HeatmapsOnImage avg_pool float32 HeatmapsOnImage float32 max_pool HeatmapsOnImage float32 scale uint8 from_uint8 HeatmapsOnImage change_normalization float32 SegmentationMapOnImage array get_arr_int concatenate get_arr_int float32 int32 SegmentationMapOnImage uint8 imresize_single_image concatenate draw float32 int32 SegmentationMapOnImage array uint8 imresize_single_image astype float32 draw_on_image tile int32 SegmentationMapOnImage abs max pad SegmentationMapOnImage arr int32 pad_to_aspect_ratio pad int32 SegmentationMapOnImage arr imresize_single_image int32 scale SegmentationMapOnImage arr clip concatenate to_heatmaps float32 int32 SegmentationMapOnImage concatenate float32 copy shape zeros from_0to1 from_heatmaps concatenate float32 copy int32 SegmentationMapOnImage deepcopy concatenate float32 int32 SegmentationMapOnImage float64 zeros float32 Polygon Polygon Polygon Polygon Polygon Polygon area Polygon project Polygon find_closest_point_index Polygon zeros _compute_inside_image_point_mask Polygon Polygon Polygon zeros is_out_of_image Polygon _test_Polygon_cut_clip _test_Polygon_cut_clip zeros func Polygon shift Polygon uint8 Polygon reshape astype float32 draw_on_image tile enumerate Polygon extract_from_image astype copy int32 zeros change_first_point_by_coords Polygon change_first_point_by_index Polygon to_shapely_line_string Polygon to_shapely_polygon coords zip Polygon to_bounding_box Polygon Polygon change_first_point_by_index sqrt exterior zip enumerate from_shapely copy Polygon deepcopy Polygon _test_Polygon_repr_str _test_Polygon_repr_str func Polygon Polygon Polygon _interpolate_point_pair _interpolate_points _interpolate_points_by_max_distance get terminate xrange append BatchLoader get Batch Add BackgroundAugmenter close dumps put Noop loads terminate Queue _augment_images_worker gen BatchLoader join_thread test_parameters_Laplace test_parameters_Choice test_parameters_DiscreteUniform test_parameters_Discretize test_parameters_Poisson test_parameters_handle_probability_param test_parameters_Negative test_parameters_Deterministic test_parameters_FromLowerResolution test_parameters_handle_continuous_param test_parameters_operators test_parameters_ForceSign test_parameters_force_np_float_dtype test_parameters_Uniform test_parameters_draw_distribution_graph test_parameters_Positive test_parameters_Clip test_parameters_copy test_parameters_Biomial test_parameters_both_np_float_if_one_is_float test_parameters_Normal test_parameters_Weibull test_parameters_Beta test_parameters_handle_discrete_param test_parameters_Sigmoid test_parameters_RandomSign test_parameters_Subtract test_parameters_Absolute test_parameters_ChiSquare test_parameters_Divide test_parameters_IterativeNoiseAggregator test_parameters_Power test_parameters_Multiply test_parameters_Add test_parameters_draw_distribution_grid Deterministic handle_continuous_param handle_discrete_param Deterministic handle_probability_param Deterministic enumerate both_np_float_if_one_is_float zeros draw_distribution_graph draw_distributions_grid draw_grid astype int32 abs draw_distribution_graph Uniform shape sum prod reseed Binomial size abs astype float32 Choice draw_sample unique zip xrange draw_samples sum append eps reseed Choice draw_sample unique zip draw_samples sum Uniform reseed draw_sample DiscreteUniform draw_samples sum int Poisson reseed draw_sample draw_samples sum max poisson normal reseed size Choice mean Normal draw_sample histogram xrange zip draw_samples clip eps reseed size Choice mean draw_sample laplace histogram xrange zip draw_samples clip Laplace chisquare reseed size Choice mean draw_sample histogram xrange zip draw_samples ChiSquare clip reseed Weibull size weibull Choice mean draw_sample histogram xrange zip draw_samples gamma clip eps reseed size draw_sample histogram draw_samples Uniform eps zip reseed Beta size _var Choice mean draw_sample _mean histogram xrange beta draw_samples eps RandomState Deterministic reseed sort Choice flatten draw_sample unique xrange draw_samples FromLowerResolution reseed Binomial logical_and unique xrange label draw_samples sum eps Deterministic reseed Clip Choice draw_sample draw_samples Discretize Deterministic reseed draw_sample DiscreteUniform draw_samples eps Deterministic reseed sort flatten Multiply draw_samples Uniform eps Divide Deterministic reseed sort Choice flatten draw_sample unique draw_samples Uniform eps Deterministic Add reseed sort flatten draw_samples Uniform eps Deterministic reseed sort flatten Subtract draw_samples Uniform eps Deterministic reseed sort flatten Power draw_samples Uniform Absolute eps Deterministic reseed sort Choice is_single_float draw_sample unique draw_samples Deterministic reseed RandomSign Choice draw_sample xrange draw_samples sum Deterministic reseed Choice draw_sample ForceSign draw_samples sum reseed Positive draw_samples Deterministic reseed draw_samples Negative Deterministic sum eps Uniform Deterministic reseed Choice draw_sample IterativeNoiseAggregator histogram xrange append draw_samples abs eps exp Deterministic reseed Choice Sigmoid draw_sample xrange draw_samples abs reseed Choice __div__ Normal __rdiv__ DiscreteUniform Uniform Discretize deepcopy reseed copy Uniform example_show example_heavy_augmentations example_background_classes example_background_augment_batches example_determinism example_standard_situation example_single_augmenters example_unusual_distributions example_keypoints example_hooks example_withchannels load_batch print Sequential augment_images range train_on_images print randint Sequential augment_images print randint Sequential show_grid print Sequential len extend show_grid augment_images randint range to_deterministic concatenate print augment_keypoints Sequential Keypoint imshow KeypointsOnImage augment_images draw_on_image zip append randint keypoints range enumerate to_deterministic Flipud print Affine augment_image randint GaussianBlur Fliplr print randint augment_images WithChannels print Clip Choice Normal augment_images DiscreteUniform randint GaussianBlur Uniform print ones Sequential show_grid HooksImages augment_images to_deterministic imresize_single_image print draw_grid Sequential augment_batches astronaut imshow append array range list get_batch print BackgroundAugmenter Sequential hstack imshow terminate images_aug BatchLoader test_AdditiveGaussianNoise test_CoarseSaltAndPepper test_Salt test_Multiply test_Add test_CoarseSalt test_ReplaceElementwise test_SaltAndPepper test_AddElementwise test_Pepper test_CoarseDropout test_Invert test_MultiplyElementwise test_ContrastNormalization test_JpegCompression test_CoarsePepper test_Dropout std reseed ones augment_keypoints astype quokka_heatmap mean AdditiveGaussianNoise int32 xrange append augment_images abs array to_deterministic to_deterministic reseed ones augment_keypoints len quokka_heatmap mean xrange augment_images array zeros Dropout zeros_like reseed ones CoarseDropout quokka_heatmap average mean xrange append augment_images augment_image get_parameters reseed ones augment_keypoints quokka_heatmap Multiply xrange unique augment_images array augment_image to_deterministic all get_parameters reseed ones augment_keypoints size MultiplyElementwise quokka_heatmap flatten xrange unique augment_images sum array augment_image to_deterministic all get_parameters reseed ones augment_keypoints MultiplyElementwise quokka_heatmap mean xrange augment_image unique augment_images sum array zeros ReplaceElementwise to_deterministic SaltAndPepper reseed mean zeros sum augment_image CoarseSaltAndPepper reseed quokka_heatmap mean histogram xrange append zeros abs augment_image len Salt reseed mean zeros sum augment_image reseed quokka_heatmap mean histogram xrange append zeros abs augment_image CoarseSalt len reseed Pepper mean zeros sum augment_image reseed CoarsePepper quokka_heatmap mean histogram xrange append zeros abs augment_image len get_parameters Add reseed ones augment_keypoints zeros quokka_heatmap xrange unique augment_images array augment_image to_deterministic all get_parameters reseed ones augment_keypoints size zeros quokka_heatmap flatten array xrange unique augment_images sum AddElementwise augment_image to_deterministic get_parameters reseed augment_keypoints Invert quokka_heatmap array_equal xrange unique zeros augment_image to_deterministic get_parameters reseed augment_keypoints ContrastNormalization quokka_heatmap xrange unique zeros augment_image to_deterministic quokka_keypoints reseed astype float32 quokka_heatmap average JpegCompression _TwoValueParam zip augment_images keypoints abs augment_image quokka test_AverageBlur test_MedianBlur test_GaussianBlur reseed augment_keypoints xrange append augment_images GaussianBlur array to_deterministic items list reseed augment_keypoints copy array_equal dict AverageBlur xrange zeros augment_image to_deterministic all zeros_like reseed augment_keypoints copy array_equal xrange zeros MedianBlur augment_image to_deterministic uint8 reseed astype float32 MotionBlur tile allclose zeros matrix augment_image test_AddToHueAndSaturation test_Grayscale _add_hue_saturation reseed astype float32 dict xrange allclose AddToHueAndSaturation zeros abs augment_image enumerate Grayscale zeros_like reseed _compute_luminosity astype copy average int32 xrange histogram append zeros abs augment_image test_SigmoidContrast test_GammaContrast test_LinearContrast test_LogContrast test_contrast_adjust_linear uint8 all reseed augment_keypoints tolist GammaContrast set HeatmapsOnImage KeypointsOnImage tile xrange zeros augment_image len uint8 all product reseed augment_keypoints tolist set HeatmapsOnImage KeypointsOnImage SigmoidContrast tile xrange zeros augment_image len uint8 all reseed zeros augment_keypoints tolist set HeatmapsOnImage KeypointsOnImage tile xrange LogContrast augment_image len uint8 all LinearContrast reseed augment_keypoints tolist set HeatmapsOnImage KeypointsOnImage tile xrange zeros augment_image len uint8 _adjust_linear test_Sharpen test_Convolve test_Emboss uint8 all get_parameters reseed float32 array_equal Convolve int32 tile xrange append augment_image enumerate uint8 reseed Sharpen float32 _compute_sharpened_base_img augment_image uint8 reseed Emboss _compute_embossed_base_img augment_image test_Flipud test_Fliplr fliplr get_parameters reseed augment_keypoints keypoints_equal len float32 HeatmapsOnImage array_equal xrange augment_image augment_images get_arr array Fliplr to_deterministic Flipud get_parameters reseed augment_keypoints keypoints_equal len float32 HeatmapsOnImage array_equal flipud xrange augment_images get_arr array augment_image to_deterministic test_ElasticTransformation test_PiecewiseAffine test_AffineCv2 test_Affine test_Rot90 test_PerspectiveTransform xrange reseed ones HeatmapsOnImage append sum augment_image augment_keypoints astype Affine copy label zeros to_deterministic int get_parameters float32 average int32 augment_images array int get_parameters reseed augment_keypoints AffineCv2 ones astype float32 copy HeatmapsOnImage average int32 xrange append augment_images array zeros to_deterministic BoundingBox PiecewiseAffine KeypointsOnImage xrange keypoints abs get_arr reseed HeatmapsOnImage append sum augment_image augment_keypoints astype draw_on_image to_deterministic T get_parameters extract_from_image float32 average zeros std KeypointsOnImage xrange keypoints Deterministic reseed HeatmapsOnImage sum augment_image augment_keypoints PerspectiveTransform astype tile zip zeros int uint8 get_parameters imresize_single_image float32 augment_images get_coords_array KeypointsOnImage xrange abs ElasticTransformation reseed HeatmapsOnImage pad sum augment_image KEYPOINT_AUG_ALPHA_THRESH astype KEYPOINT_AUG_SIGMA_THRESH to_deterministic get_parameters imresize_single_image float32 average zeros std rot90 uint8 clip imresize_single_image augment_keypoints arr_0to1 astype float32 HeatmapsOnImage augment_heatmaps KeypointsOnImage _TwoValueParam zip augment_images keypoints Rot90 augment_image test_restore_augmented_image_dtype_ test_Augmenter_augment_batches test_Augmenter_augment_segmentation_maps test_Lambda test_Sequential test_clip_augmented_image_ test_OneOf test_SomeOf test_AssertShape test_Augmenter_augment_keypoints test_clip_augmented_image test_invert_reduce_to_nonempty test_restore_augmented_images_dtypes_ test_Augmenter test_reduce_to_nonempty test_WithChannels test_AssertLambda test_Noop test_ChannelShuffle test_copy_dtypes_for_restore test_restore_augmented_image_dtype test_Augmenter_remove test_Sometimes test_2d_inputs test_restore_augmented_images_dtypes test_clip_augmented_images test_Augmenter_copy_random_state test_Augmenter_hooks test_clip_augmented_images_ test_Augmenter_find create_random_keypoints reseed augment_keypoints Noop create_random_images augment_images to_deterministic reseed augment_keypoints Lambda float32 HeatmapsOnImage xrange augment_images array to_deterministic AssertLambda reseed augment_keypoints float32 HeatmapsOnImage augment_images array to_deterministic reseed augment_keypoints AssertShape float32 HeatmapsOnImage xrange augment_images array zeros to_deterministic zeros copy_dtypes_for_restore zeros restore_augmented_image_dtype_ int32 zeros restore_augmented_image_dtype int32 uint8 astype restore_augmented_images_dtypes_ zeros copy_dtypes_for_restore uint8 astype zeros restore_augmented_images_dtypes copy_dtypes_for_restore zeros clip_augmented_image_ zeros clip_augmented_image zeros xrange clip_augmented_images_ len zeros len xrange clip_augmented_images reduce_to_nonempty invert_reduce_to_nonempty BoundingBox DummyAugmenterCallsParent Sequential augment_bounding_boxes randint is_np_array vstack get_all_children xrange list reseed augment_batches DummyAugmenter HeatmapsOnImage localize_random_state RandomState Crop augment_keypoints choice tile augment_heatmaps zip zeros deepcopy uint8 get_parameters draw_grid DummyAugmenterBBs augment_images DummyAugmenterRepr reseed augment_keypoints KeypointsOnImage Affine concatenate reseed float32 int32 tile SegmentationMapOnImage find_augmenters_by_name Flipud reseed Sequential find_augmenters_by_names Noop find_augmenters Fliplr reseed get_seq find_augmenters_by_name remove_augmenters reseed augment_keypoints HooksKeypoints Sequential copy Affine HooksImages augment_images array remove_augmenters Sequential localize_random_state_ copy_random_state augment_images quokka_square array Fliplr to_deterministic Sequential xrange get_arr all reseed add HeatmapsOnImage array_equal augment_image Flipud augment_keypoints to_deterministic keypoints_equal float32 augment_images allclose randint array Fliplr uint8 reseed float32 randint Choice HeatmapsOnImage array_equal augment_images xrange zip append zeros sum fliplr augment_image SomeOf enumerate int list items reseed OneOf xrange zeros sum augment_image len fliplr randint xrange Add reseed HeatmapsOnImage array_equal augment_image Crop augment_keypoints flipud Sometimes unique to_deterministic uint8 get_parameters keypoints_equal float32 augment_images zeros array Fliplr get_parameters Add concatenate Sequential copy augment_images WithChannels zeros augment_image all reseed reshape float32 array_equal HeatmapsOnImage KeypointsOnImage xrange augment_image ChannelShuffle reseed augment_images array Fliplr list reseed augment_batches Keypoint array_equal array fliplr Fliplr test_keypoint_augmentation test_dtype_preservation test_determinism test_unusual_channel_numbers reseed augment_images augment_keypoints to_deterministic y zip reseed augment_keypoints print from_keypoint_image Keypoint to_keypoint_image extend sqrt KeypointsOnImage augment_image append keypoints range x to_deterministic reseed augment_images augment_image reseed augment_images set test_AlphaElementwise test_Alpha HooksKeypoints Sequential Choice KeypointsOnImage xrange round Add reseed get_children_lists HeatmapsOnImage Noop append augment_image astype Alpha Affine unique deepcopy uint8 get_parameters keypoints_equal shift float32 average histogram HooksImages zeros HooksKeypoints flatten KeypointsOnImage Add reseed HeatmapsOnImage Noop augment_image size astype AlphaElementwise Affine _DummyMaskParameter uint8 deepcopy shift float32 histogram HooksImages zeros test_Superpixels all get_parameters reseed _array_equals_tolerant copy Superpixels tile xrange augment_image values test_CropToFixedSize test_Pad test_KeepSizeByResize test_PadToFixedSize test__handle_position_parameter test_Crop test_Scale range _handle_position_parameter Poisson Choice KeypointsOnImage xrange abs list reseed add HeatmapsOnImage shape augment_image astype set tile remove get_parameters float32 Scale average array KeypointsOnImage xrange argmax all reseed ones HeatmapsOnImage pad append augment_image Pad augment_keypoints zeros to_deterministic int shift float32 augment_images array int Crop reseed augment_keypoints shift float32 randint KeypointsOnImage round xrange augment_image append augment_images argmax array zeros to_deterministic uint8 reseed augment_keypoints astype float32 HeatmapsOnImage KeypointsOnImage tile zeros PadToFixedSize augment_image uint8 reseed reshape augment_keypoints astype float32 HeatmapsOnImage KeypointsOnImage CropToFixedSize tile zeros augment_image uint8 Crop imresize_single_image reseed astype float32 _draw_samples HeatmapsOnImage new_random_state KeypointsOnImage scale KeepSizeByResize augment_image test_Clouds test_Fog test_Snowflakes test_FastSnowyLandscape uint8 COLOR_RGB2HLS reseed float64 FastSnowyLandscape astype float32 COLOR_BGR2HLS COLOR_HLS2BGR COLOR_HLS2RGB augment_images augment_image cvtColor reseed astype float32 zeros augment_image reseed astype float32 zeros augment_image reseed astype float32 zeros augment_image | # imgaug This python library helps you with augmenting images for your machine learning projects. It converts a set of input images into a new, much larger set of slightly altered images. [](https://travis-ci.org/aleju/imgaug) [](https://codecov.io/gh/aleju/imgaug) [](https://www.codacy.com/app/aleju/imgaug?utm_source=github.com&utm_medium=referral&utm_content=aleju/imgaug&utm_campaign=Badge_Grade) <table> <tr> <th> </th> <th>Image</th> | 885 |
Rajlaxmi04/Network-Traffic-Anomaly-Detection-using-PCA-and-BiGAN | ['anomaly detection'] | ['Efficient GAN-Based Anomaly Detection'] | utils/adapt_data.py bigan/run_kdd.py bigan/kdd_utilities.py data/kdd.py main.py utils/evaluations.py path run decoder discriminator leakyReLu encoder _leakyReLu_impl get_getter create_logdir display_progression_epoch train_and_test display_parameters run set_nc _get_dataset get_train get_test _adapt get_shape_input _encode_text_dummy get_shape_label _col_names _to_xy _get_adapted_dataset adapt_labels do_prc format d print error nc m rd w import_module example info label dataset nb_epochs split print int str chr write flush getLogger batch_size warn placeholder get_test discriminator Supervisor encoder format RandomState get_train copy latent_dim create_logdir info int set_nc learning_rate decoder print float32 display_parameters bool transform astype PCA copy float32 _to_xy _encode_text_dummy _col_names sample MinMaxScaler read_csv fit _adapt _get_dataset drop columns format get_dummies append columns int permutation RandomState concatenate xlabel makedirs close ylabel precision_recall_curve ylim title savefig figure fill_between xlim step auc | # Network Traffic Anomaly Detection using PCA and BIGAN ## Prerequisites. To run the code, follow those steps: Install Python 3 ``` sudo apt install python3 python3-pip ``` Download the project code: ``` git clone https://github.com/Rajlaxmi04/Network-Traffic-Anomaly-Detection-using-PCA-and-BiGAN | 886 |
RaleLee/Co-GAT | ['sentiment analysis', 'graph attention'] | ['Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification'] | utils/process.py utils/help.py utils/__init__.py utils/dict.py utils/load.py nn/encode.py nn/__init__.py nn/decode.py test_model.py nn/model.py nn/relation.py main.py UniLSTMLayer UniLinearLayer RelationDecoder LinearDecoder BiLSTMLayer GraphAttentionLayer BiRNNEncoder GAT UtterancePretrainedModel BiGraphEncoder TaggingAgent GraphRelation GraphAttentionLayer GAT WordAlphabet AbstractAlphabet PieceAlphabet LabelAlphabet load_json_file iterable_support NormalMetric fix_random_state noise_augment nest_list expand_list load_txt ReferMetric _collate_func _GeneralDataSet DataHub evaluate training seed manual_seed_all manual_seed is_available append get_freq UNK_SIGN isinstance append extend isinstance append range len close append range len time backward AdamW clip_grad_norm_ zero_grad Adam tqdm measure parameters train step validate_act time validate_emot iterable_support sent_vocab len NormalMetric extend act_vocab tqdm index eval expand_list ReferMetric | # Co-GAT This repository contains the PyTorch implementation of the paper: **[Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification](https://ojs.aaai.org/index.php/AAAI/article/view/17616)**. Libo Qin, Zhouyang Li, Wanxiang Che, Minheng Ni, Ting Liu. ***AAAI 2021***. ## Architecture <img src="img/framework.jpg"> ## Requirements Our code relies on Python 3.6 and following libraries: | 887 |
Ranlot/single-parameter-fit | ['time series'] | ['Real numbers, data science and chaos: How to fit any dataset with a single parameter'] | helperFunctions.py binaryToDecimal decimalToBinary dyadicDecoder logisticDecoder generateData binaryToDecFaster findInitialCondition binaryReducer mpf join print phi map binaryToDecFaster len partial | ### Real numbers, data science and chaos: How to fit any dataset with a single parameter ##### *All details and more examples can be found in the accompanying [arXiv:1904.12320](https://arxiv.org/abs/1904.12320) paper (also hosted [here](1904.12320.pdf)).* [](https://mybinder.org/v2/gh/Ranlot/single-parameter-fit/master) We show how any dataset of any modality (time-series, images, sound...) can be approximated by a well-behaved (continuous, differentiable...) scalar function with a single real-valued parameter: <p align="center"> <img src="resources/decodingFunction.png" width="200"/> </p> Building upon elementary concepts from chaos theory, we adopt a pedagogical approach demonstrating how to adjust this parameter in order to achieve arbitrary precision fit to all samples of the data. | 888 |
RashadGarayev/PersonDetection | ['pedestrian detection'] | ['Why do linear SVMs trained on HOG features perform so well?'] | training_SVM.py visualize_real_time.py visualize_image.py Sliding.py sliding_window range | # Person Detection using HOG Feature and SVM Classifier ### Tested ```python3 visualize_image.py```  ## Usage ```git clone https://github.com/RashadGarayev/PersonDetection``` ```cd PersonDetection``` ### For training ```python3 training_SVM.py``` | 889 |
RasmusRPaulsen/Deep-MVLM | ['pose tracking'] | ['Multi-view consensus CNN for 3D facial landmark placement'] | utils/util.py base/base_trainer.py utils3d/__init__.py train.py trainer/__init__.py deepmvlm/__init__.py preparedata.py logger/logger.py data_loader/data_loaders.py model/loss.py logger/tensorboardutils.py prediction/__init__.py utils/__init__.py deepmvlm/api.py trainer/trainer.py utils3d/utils3d.py test.py predict.py logger/__init__.py data_loader/FaceDataset.py parse_config.py model/metric.py model/model.py base/__init__.py utils3d/render3d.py base/base_model.py base/base_data_loader.py prediction/predict2d.py _set_by_path ConfigParser _get_opt_name _update_config _get_by_path main process_one_file process_file_list process_files_in_dir delete_lock_file random_transform prepare_bu_3dfe_data process_file_bu_3dfe split_data_into_train_and_test create_lock_file main write_lm_names_to_result_file read_3d_landmarks get_working_device get_landmark_bounds write_landmark_accuracy get_device_and_load_model visualise_landmarks_as_spheres_with_accuracy get_landmarks_bounding_box_diagonal_length main predict_one_subject test_on_bu_3d_fe show_batch main test_model_mvlm get_cuda_info test_dataloader BaseDataLoader BaseModel BaseTrainer FaceDataLoader FaceDataset DeepMVLM setup_logging TensorboardWriter nll_loss mse_loss my_metric my_metric2 conv3x3 ResidualBlock MVLMModel HourGlassModule Predict2D Trainer read_json inf_loop write_json Timer ensure_dir no_transform Render3D Utils3D _set_by_path target _get_opt_name getattr flags startswith visualise_mesh_and_landmarks write_landmarks_as_vtk_points DeepMVLM print predict_one_file write_landmarks_as_text print predict_one_file write_landmarks_as_text DeepMVLM DeepMVLM print predict_one_file get_mesh_files_in_dir write_landmarks_as_text str process_files_in_dir process_file_list isdir name print process_one_file gethostname close write open remove exists randint uniform double GetOutput vtkPNGWriter SetFocalPoint SetParallelProjection create_lock_file AddActor vtkPoints SetInput max open delete_lock_file SetAmbient str basename SetOffScreenRendering SetClippingRange SetViewUp vtkActor SetDiffuse dirname SetInterpolate vtkTexture vtkWindowToImageFilter SetInputBufferTypeToRGB RotateZ range SetParallelScale vtkImageShiftScale SetMapper AddRenderer vtkBMPReader SetTexture SetInputData vtkVRMLImporter SetOutputScalarTypeToUnsignedChar SetScale RotateX SetVisibility close Write SetTransform SetColor Update GetNumberOfPoints Identity vtkTransform SetSize GetInput vtkTransformPolyDataFilter SetQualityTo32Bit SetScalars SetPosition SetSpecular print SetFileName random_transform Modified vtkPolyData SetInputConnection vtkPolyDataMapper write Render SetBackground SetShift GetOutputPort SetPoints isfile vtkRenderer RotateY SetInputBufferTypeToZBuffer vtkRenderWindow makedirs int replace write close dirname append open print split_data_into_train_and_test process_file_bu_3dfe makedirs prepare_bu_3dfe_data device load str initialize format get_working_device error debug DataParallel eval resume load_state_dict info to get_logger compute_all_landmarks_from_view_lines Utils3D project_landmarks_to_surface Predict2D render_3d_file get_device_and_load_model predict_heatmaps_from_images Render3D compute_lines_from_heatmap_maxima str print write euclidean range len min max get_landmark_bounds sqrt GetOutput AddInputData vtkAppendPolyData SetNumberOfComponents SetPhiResolution SetRadius vtkPolyDataWriter get_landmarks_bounding_box_diagonal_length vtkDoubleArray euclidean range GetNumberOfPoints SetInputData SetThetaResolution Write Update vtkSphereSource SetScalars SetFileName SetCenter SetValue SetNumberOfValues len str write dirname flush Predict2D landmarks predict_heatmaps_from_images compute_lines_from_heatmap_maxima open compute_all_landmarks_from_view_lines str basename read_3d_landmarks write_landmark_accuracy render_3d_file Utils3D get_device_and_load_model visualise_landmarks_as_spheres_with_accuracy Render3D flush time print write temp_dir isfile project_landmarks_to_surface len test_on_bu_3d_fe ioff show size random axis sqrt imshow figure zeros numpy range print show_batch initialize enumerate initialize get_logger info print device lr_scheduler initialize train Trainer getattr split_validation optim str basicConfig format list items print read_json dictConfig Path is_file mkdir Path repeat | # Deep learning based 3D landmark placement A tool for accurately placing 3D landmarks on 3D facial scans based on the paper [Multi-view Consensus CNN for 3D Facial Landmark Placement](https://arxiv.org/abs/1910.06007).  ## Citing Deep-MVLM If you use Deep-MVLM in your research, please cite the [paper](https://arxiv.org/abs/1910.06007): ``` @inproceedings{paulsen2018multi, title={Multi-view Consensus CNN for 3D Facial Landmark Placement}, author={Paulsen, Rasmus R and Juhl, Kristine Aavild and Haspang, Thilde Marie and Hansen, Thomas and Ganz, Melanie and Einarsson, Gudmundur}, | 890 |
Rayhane-mamah/Tacotron-2 | ['speech synthesis'] | ['Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions'] | wavenet_vocoder/__init__.py paper_hparams.py datasets/preprocessor.py wavenet_vocoder/feeder.py tacotron/synthesize.py tacotron/utils/plot.py tacotron/utils/symbols.py wavenet_vocoder/util.py tacotron/models/helpers.py wavenet_vocoder/models/mixture.py train.py tacotron/synthesizer.py tacotron/feeder.py tacotron/models/__init__.py tacotron/models/tacotron.py test_wavenet_feeder.py tacotron/models/attention.py tacotron/utils/cleaners.py tacotron/utils/__init__.py datasets/wavenet_preprocessor.py wavenet_vocoder/synthesizer.py tacotron/train.py tacotron/models/custom_decoder.py tacotron/models/Architecture_wrappers.py datasets/audio.py wavenet_vocoder/models/gaussian.py preprocess.py hparams.py synthesize.py datasets/__init__.py tacotron/models/modules.py wavenet_vocoder/models/wavenet.py wavenet_vocoder/models/__init__.py wavenet_vocoder/synthesize.py tacotron/utils/numbers.py wavenet_vocoder/models/modules.py wavenet_vocoder/train.py tacotron/__init__.py tacotron/utils/text.py tacotron/utils/cmudict.py infolog.py wavenet_preprocess.py hparams_debug_string _close_logfile log init _send_slack hparams_debug_string preprocess norm_data write_metadata main run_preprocess main get_sentences prepare_run synthesize check_time_alignment _limit_time _adjust_time_resolution get_groups _assert_ready_for_upsample main _ensure_divisible run read_seq save_seq main train prepare_run main preprocess write_metadata run_preprocess _mel_to_linear load_wav _build_mel_basis _linear_to_mel librosa_pad_lr save_wavenet_wav start_and_end_indices get_hop_size _griffin_lim_tensorflow num_frames _mel_to_linear_tensorflow melspectrogram _amp_to_db _istft preemphasis save_wav _db_to_amp_tensorflow _denormalize inv_linear_spectrogram inv_mel_spectrogram_tensorflow _db_to_amp inv_mel_spectrogram inv_linear_spectrogram_tensorflow _normalize _griffin_lim _denormalize_tensorflow _stft inv_preemphasis _lws_processor pad_lr linearspectrogram trim_silence _process_utterance build_from_path _process_utterance build_from_path Feeder run_eval generate_fast run_live run_synthesis tacotron_synthesize Synthesizer add_embedding_stats time_string add_train_stats model_test_mode model_train_mode train add_eval_stats tacotron_train TacotronDecoderCellState TacotronDecoderCell TacotronEncoderCell LocationSensitiveAttention _compute_attention _location_sensitive_score _smoothing_normalization CustomDecoderOutput CustomDecoder TacoTrainingHelper _teacher_forcing_ratio_decay _go_frames TacoTestHelper ZoneoutLSTMCell CBHG StopProjection HighwayNet MaskedSigmoidCrossEntropy sequence_mask MaskedLinearLoss Postnet Prenet MaskedMSE FrameProjection conv1d EncoderRNN _round_up_tf DecoderRNN EncoderConvolutions Tacotron split_func create_model lowercase english_cleaners expand_abbreviations collapse_whitespace basic_cleaners convert_to_ascii transliteration_cleaners expand_numbers _parse_cmudict _get_pronunciation CMUDict normalize_numbers _expand_dollars _expand_ordinal _expand_decimal_point _expand_number _remove_commas plot_alignment plot_spectrogram split_title_line text_to_sequence _clean_text _symbols_to_sequence _should_keep_symbol sequence_to_text _arpabet_to_sequence ValueWindow _pad_inputs Feeder _round_up _interp _pad_targets _round_down _ensure_divisible run_synthesis wavenet_synthesize _pad_inputs Synthesizer add_test_stats create_shadow_saver add_embedding_stats time_string save_log eval_step wavenet_train add_train_stats save_checkpoint model_test_mode model_train_mode train load_averaged_model mulaw inv_mulaw_quantize _log1p sequence_mask is_mulaw_quantize is_scalar_input is_raw inv_mulaw _sign _abs _asint is_mulaw _assert_valid_input_type mulaw_quantize _asfloat waveplot plot_spectrogram sample_from_gaussian gaussian_maximum_likelihood_estimation_loss log_sum_exp log_prob_from_logits discretized_mix_logistic_loss sample_from_discretized_mix_logistic MaskedCrossEntropyLoss WeightNorm CausalConv1D ReluActivation ConvTranspose2D _conv1x1_forward ResidualConv1DGLU GaussianMaximumLikelihoodEstimation Embedding LeakyReluActivation SubPixelConvolution ConvTranspose1D DiscretizedMixtureLogisticLoss MaskedMeanSquaredError Conv1D1x1 ResizeConvolution NearestNeighborUpsample _expand_global_features WaveNet receptive_field_size maybe_Normalize_weights create_model values _close_logfile format write open print start write close encode Request urlopen add_header join n_jobs build_from_path write_metadata makedirs format print sample_rate sum max len language join reader print base_dir startswith voice join output preprocess base_dir norm_data hparams parse print add_argument ArgumentParser parse_args run_preprocess join hparams parse checkpoint sentences format wavenet_synthesize sleep reset_default_graph tacotron_synthesize log synthesize wavenet_synthesize warn get_sentences tacotron_synthesize prepare_run load train_with_GTA join base_dir get_hop_size randint append trim_silence _ensure_divisible _assert_ready_for_upsample len _adjust_time_resolution _limit_time check_time_alignment tqdm run isfile tf_log_level str format base_dir init slack_url makedirs read_seq join save_seq wavenet_train tacotron_train sleep output_dir reset_default_graph tacotron_synthesize log wavenet_input wavenet_train train tacotron_train input_dir int16 write astype int16 write astype size range int sample_rate frame_shift_ms hop_size _stft signal_normalization _amp_to_db ref_level_db abs magnitude_power _stft _linear_to_mel signal_normalization _amp_to_db ref_level_db abs magnitude_power T _denormalize run_lws _lws_processor astype float32 signal_normalization _db_to_amp ref_level_db power magnitude_power use_lws _mel_to_linear T _denormalize run_lws _lws_processor astype float32 signal_normalization _db_to_amp ref_level_db power magnitude_power use_lws _db_to_amp_tensorflow _denormalize_tensorflow signal_normalization pow ref_level_db magnitude_power _db_to_amp_tensorflow _denormalize_tensorflow signal_normalization pow _mel_to_linear_tensorflow ref_level_db magnitude_power complex exp griffin_lim_iters angle _stft rand astype pi _istft range use_lws num_frames len _build_mel_basis pinv _build_mel_basis pinv _build_mel_basis exp log min_level_db symmetric_mels allow_clipping_in_normalization symmetric_mels allow_clipping_in_normalization symmetric_mels allow_clipping_in_normalization ProcessPoolExecutor load_wav rescale preemphasize librosa_pad_lr save silence_threshold max start_and_end_indices use_lws mulaw is_mulaw_quantize get_hop_size pad int16 preemphasis n_fft format quantize_channels wavenet_pad_sides astype is_mulaw join T float32 pad_lr mulaw_quantize trim_silence input_type rescaling_max len submit join partial replace append listdir synthesize load hparams_debug_string Synthesizer generate_fast input log load join format hparams_debug_string Synthesizer log makedirs load join format hparams_debug_string Synthesizer input_dir log makedirs format run_live model_checkpoint_path output_dir log visualize_embeddings ProjectorConfig zip add append add_summary Summary Value model set_random_seed tacotron_random_seed ValueWindow Saver placeholder model_train_mode GL_on_GPU predict_linear format hparams_debug_string replace inv_mel_spectrogram_tensorflow base_dir model_test_mode ConfigProto inv_linear_spectrogram_tensorflow Variable float32 tacotron_input Coordinator tacotron_train_steps makedirs squeeze concat attention_mechanism matmul attention_layer expand_dims values dtype get_variable tacotron_teacher_forcing_init_ratio convert_to_tensor tacotron_teacher_forcing_final_ratio cosine_decay tacotron_teacher_forcing_start_decay tacotron_teacher_forcing_decay_alpha less float cond mod shape cond zeros equal convert_to_tensor reduce_max _round_up_tf ones sequence_mask outputs_per_step sequence_mask outputs_per_step int sequence_mask ones sample_rate reduce_sum abs outputs_per_step num_freq append range sub lowercase collapse_whitespace lowercase convert_to_ascii collapse_whitespace convert_to_ascii expand_abbreviations collapse_whitespace expand_numbers append _get_pronunciation sub split split group split int group sub split xlabel add_subplot ylabel colorbar tight_layout close imshow title savefig figure split_title_line set_title text add_subplot tight_layout colorbar close imshow savefig figure rot90 split_title_line append match group len cleaner getattr sorted mels_dir format model_checkpoint_path run_synthesis output_dir log Summary add_summary variables dict zip restore add_test_stats join time format T _interp waveplot _hparams run save_wavenet_wav plot_spectrogram log len join T format _interp waveplot save_wavenet_wav plot_spectrogram log run save create_shadow_saver wavenet_random_seed wavenet_train_steps speakers_path _assert_valid_input_type _assert_valid_input_type _assert_valid_input_type mulaw _asfloat ndarray isscalar isinstance ndarray isscalar isinstance ndarray isscalar isinstance ndarray isscalar isinstance ndarray isscalar isinstance subplot set_title text sample_rate close tight_layout savefig figure exp squeeze square pi maximum Normal cdf log sample Normal exp maximum get_shape reduce_max len get_shape reduce_max len exp softplus ones transpose maximum where sigmoid log_prob_from_logits log exp one_hot transpose maximum reduce_sum shape random_uniform argmax log one_hot sequence_mask ones float32 discretized_mix_logistic_loss sequence_mask ones float32 sequence_mask gaussian_maximum_likelihood_estimation_loss ones float32 sequence_mask reshape tile shape input_type is_mulaw_quantize | # Tacotron-2: Tensorflow implementation of DeepMind's Tacotron-2. A deep neural network architecture described in this paper: [Natural TTS synthesis by conditioning Wavenet on MEL spectogram predictions](https://arxiv.org/pdf/1712.05884.pdf) This Repository contains additional improvements and attempts over the paper, we thus propose **paper_hparams.py** file which holds the exact hyperparameters to reproduce the paper results without any additional extras. Suggested **hparams.py** file which is default in use, contains the hyperparameters with extras that proved to provide better results in most cases. Feel free to toy with the parameters as needed. DIFFERENCES WILL BE HIGHLIGHTED IN DOCUMENTATION SHORTLY. # Repository Structure: Tacotron-2 ├── datasets ├── en_UK (0) │ └── by_book | 891 |
RayyanRiaz/EVGAE | ['link prediction'] | ['Epitomic Variational Graph Autoencoder'] | helpers.py analysis/base.py modules.py main.py model.py get_planetoid_dataset map_labels train test QyGivenX Encoder EpitomeVGAE FCNet GCNNet Analyser VAEAnalyser size linear_sum_assignment zeros max range join Planetoid NormalizeFeatures realpath dirname kl_losses negative_sampling backward size zero_grad mean encode step node_decoder recon_loss_without_reduction eval test_model | # EVGAE Epitomic Variational Graph Autoencoder The conda environment, used to run the code has been dumped in environment.yml file | 892 |
ReMine-Lab/RRGen | ['response generation'] | ['Automating App Review Response Generation'] | src/tester.py baseline/tf_bleu.py src/checkpoint.py src/get_label.py src/util.py src/model.py src/split_train_test.py src/embedding.py src/metrics/ttest_bleu.py src/decoder.py src/get_sentiment.py src/vis_attention.py src/metrics/bleu_perl.py src/split_cross_data.py src/translate.py src/metrics/sentence_bleu.py src/trainer.py src/parameter.py src/metrics/nmt_bleu.py src/evaluator.py src/metrics/reference.py src/external_feature.py src/encoder.py src/blue.py src/metrics/scorer.py data_ananlysis/dataset_sum.py src/metrics/meteor.py fleiss_kappa.py fleiss_kappa read_data evaluate compute_blue4 build_dict get_bow get_response_word_length load_cates get_cate_num load_checkpoint save_checkpoint LuongAttnDecoderRNN collate_fn Embedding AttrDict Dataset EncoderRNN evaluate LoadExtFeature _get_keywords get_fn_label sub_fn_process get_label sub_process get_data split_data main convert_xml modify_xml run_surf FileSentiment get_data main RateSentiment sub_process get_path _load_test_data _valid_test train compute_grad_norm translate sequence_mask write_to_tensorboard detach_hidden variable2numpy masked_cross_entropy get_gpu_memory_usage plot_heat_maps get_ref_cand MeteorReference MeteorError MeteorScorer _get_ngrams compute_bleu Reference Scorer SentenceBleuReference SentenceBleuScorer read_gen_texts read_sources chunks shape float sum zeros sentence_bleu enumerate len join print check_output process_data dirname get list filter_tokens map Dictionary compactify filter_extremes append doc2bow coo_matrix close literal_eval open join remove makedirs map abspath save listdir split _pad_sequences sort transpose zip vocab_size cuda max list tolist encoder cut range LongTensor use_sent_len size eval detach_hidden masked_cross_entropy decoder Variable max_seq_len contiguous zeros dump strip readlines close lemmatize open enumerate split str prettify write close dicttoxml BeautifulSoup sub split open enumerate readlines close open print system read prettify write close BeautifulSoup open strip str parse int read get_label close strftime split convert_xml run_surf open str sub_save print append enumerate join readlines close convert_xml run_surf open join read parse str readlines close strip enumerate open get_fn_label sorted print close writelines listdir open decode replace communicate Popen split print Popen split RateSentiment get amap tqdm get_data Pool range len print join listdir append readlines split open print strip compute_bleu translate tqdm split append numpy enumerate len list norm filter float max zero_grad max_grad_norm vocab_size tensor cuda max list tolist encoder cut range compute_grad_norm LongTensor use_sent_len size detach_hidden clip_grad_norm masked_cross_entropy decoder backward Variable max_seq_len contiguous parameters zeros step data tensor cuda max list topk transpose tolist append encoder cut range LongTensor use_sent_len size eval detach_hidden item join decoder Variable max_seq_len split zeros len max Variable size expand cuda expand_as long is_cuda int sequence_mask view log_softmax size contiguous float sum format replace add_histogram grad named_parameters variable2numpy add_scalar detach_ detach check_output dict zip range len show int subplots arange set_xticklabels set_yticklabels invert_yaxis set_yticks set_xlim colorbar set_xticks pcolor tick_top xticks set_ylim yticks join remove print check_output readlines close writelines open append split tuple range Counter len _get_ngrams exp Counter zip float sum range len readlines close open append enumerate split readlines close split open append enumerate range len | # Automating App Review Response Generation Dataset and replication package for the paper Automating App Review Response Generation (ASE 2019). ## Usage Run the code with ```angular2html $ python model.py ``` You can change the configures in `parameter.py`, including the `hidden_size`, `word_vec_size`, `num_epochs`, etc. The important parameters are ``` use_sent_rate -- whether include sentence rating or not | 893 |
ReemHal/Browser-Based-Annotator | ['image retrieval'] | ['Looking at Outfit to Parse Clothing'] | png_parsers/iap_test.py png_parsers/image_annotation_parser.py read AnnotationData format seek print loads array open | A Web-based Tool for Image Annotation ==================== The image annotation tool is an online tool for labeling objects within RGBA images. The tool is based on the open source Js Segment Annotation Tool developed by Kota Yamaguchi [1](#ref1). It is browser-based and is, therefor, compatible with a wide variety of platforms. The tool is designed for versatility and ease of use to allow users to efficiently label large numbers of images. For more detail on how to use the tool please refer to the [User Guide](https://gitlab.guelphrobolab.ca/lab-tools/js-segment-annotator/blob/master/Documentation/UserGuide.pdf). What’s new in this version ==================== To achieve our goal of creating a versatile, easy to use labeling tool, we introduce several features including: 1. Multi-labeled segments: image segments can be annotated with more than a single label simultaneously. The same segment can represent a tomato and an unripe fruit. 2. Segment overlap: annotated segments are not required to be disjoint. An object can be labeled as a t-shirt and a portion of it can also be labeled as a logo. | 894 |
RelationRx/SparseDDD | ['time series'] | ['Sparse Dynamic Distribution Decomposition: Efficient Integration of Trajectory and Snapshot Time Series Data'] | python/mc_integration.py python/q_vec_to_p_mat.py python/single_time_point_error.py python/Package_RunMe_Comparison.py python/q_matrix_comparisons.py python/p_matrix_comparisons.py python/tightfig.py python/utils.py python/RunMe_SDE_BoundedOU.py python/calculate_g_matrix.py calculate_g_matrix multiply mc_integration p_matrix_comparisons logical q_matrix_comparisons qvec_to_pmat single_time_point_error tightfig process_q_matrix process_matrix process_p_matrix T any isinf norm Data_Dim multiply min len basis_nd sum max Basis_eps sum time_pts pts_of_int concatenate reshape size concat Lambda isnan num_tpts zeros single_time_point_error err_mat range sum time_pts logical concatenate grad_mat reshape size concat solve Lambda single_time_point_error isnan n_ts_samples num_tpts zeros MassMat range Indc MassMat astype solve T expm concat isnan empty NaN nan MassMat calculate_g_matrix arange concat logical_not max all view nargin get pos iscell cellarray set strcmp bsxfun ti error reshape min hax findall cell2mat print func concatenate | # SparseDDD Implementation of Sparse Dynamic Distribution Decomposition ======= This repository is the official implementation of Sparse Dynamic Distribution Decomposition: Efficient Integration of Trajectory and SnapshotTime Series Data. ## Requirements Files run on 2019b version of Matlab, Octave does not have some of the statistical commands used. Requirements: - Symbolic Math Toolbox (for dynamical system in /data only) - Statistics and Machine Learning toolbox. - Optimisation toolbox. | 895 |
ReubenDo/jSTABL | ['lesion segmentation', 'data augmentation'] | ['Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets'] | jstabl/wmh/inference_WMH.py jstabl/glioma/train_BRATS_pseudohealthy.py jstabl/utilities/jaccard.py jstabl/networks/Discriminator.py jstabl/utilities/scale_normalization.py jstabl/networks/UNetModalityMeanTogether.py jstabl/glioma/train_BRATS_adversarial.py setup.py jstabl/utilities/acti.py jstabl/utilities/cropping.py jstabl/glioma/train_BRATS_augmentation.py jstabl/wmh/train_WMH_noDA.py jstabl/glioma/inference_BRATS.py jstabl/glioma/train_BRATS_noDA.py jstabl/utilities/sampling.py parsing_data main inference_padding set_requires_grad parsing_data main train infinite_iterable merge_skips parsing_data main train infinite_iterable parsing_data main train infinite_iterable parsing_data main train infinite_iterable Discriminator StackedConvLayers ConvNormNonlin InitWeights_He Upsample Generic_UNet LeakyRelu find_zeros crop jaccard_lesion jaccard2D onehot jaccard jaccard_tissue GridSampler GridAggregator ScaleNormalization parsing_data main inference_padding parsing_data main train infinite_iterable Transform GridSampler DataLoader load_state_dict append to cat format astype Resample WriteImage nib_to_sitk eval window_size sitkNearestNeighbor GridAggregator enumerate load print ImagesDataset numpy len Image model_dir output_dir device Generic_UNet INTENSITY tolist Subject append LeakyReLU epoch_infe format Compose is_available parsing_data path_file join print inference_padding dict dataset_split isfile read_csv makedirs parse_args add_argument ArgumentParser parameters shape reshape permute cat arange model_dir DataLoader ReduceLROnPlateau save IntTensor BCEWithLogitsLoss DataFrame str Adam load_state_dict append to next infinite_iterable state_dict format param_groups eval Queue type load join time learning_rate print ImagesDataset to_csv tqdm dict parameters isfile step read_csv len path_lesion split_lesion exists open str Discriminator close split_control stdout path_control LABEL train zero_grad warmup item path_pseudohealthy split_pseudohealthy tuple min where shape max amax load affine Crop get_fdata WriteImage find_zeros ReadImage sum reshape onehot mean shape abs abs reshape float onehot mean shape to sum sum reshape onehot mean shape abs sum reshape onehot mean shape abs parameters max dataset_split_source dataset_split_target path_source path_target | # Learning joint Segmentation of Tissues And Brain Lesions (jSTABL) from task-specific hetero-modal domain-shifted datasets Public PyTorch implementation of [Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets](https://arxiv.org/abs/2009.04009) published in Medical Image Analysis. This work proposed a technique to perform joint brain tissue and lesion segmentation. Due to the lack of fully-annotated data, the framework has been trained using hetero-modal (missing modalities), task-specific (tissue or lesion annotations) and domain-shifted (different acquisition protocols) datasets. Two types of lesions are considered: gliomas and white matter lesions. ### Example of joint brain tissues and glioma segmentation: *Example of automatic joint brain tissues and glioma segmentation using jSTABL. Segmented tissue structures: grey matter (green), white matter (yellow), basal ganglia (light blue), ventricles (red), cerebellum (orange) and brainstem (dark blue). Segmented glioma sub-regions: oedema/invasion (purple), non-enhancing solid core (turquoise) and enhancing core (lime green).* If you find this code useful for your research, please cite the following paper: ``` @article{DORENT2021101862, title = "Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets", | 896 |
RiTUAL-UH/emphasis-2019 | ['common sense reasoning'] | ['Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions'] | Emphasis_selection/utils/data.py Emphasis_selection/helper.py Emphasis_selection/logger.py Emphasis_selection/trainer_prob.py Emphasis_selection/model/seqmodel.py Emphasis_selection/config.py Emphasis_selection/visualization/attention_visualization.py Emphasis_selection/utils/Evaluation2.py Emphasis_selection/model/seqmodel_Elmo.py Emphasis_selection/main.py Helper Logger to_tensor_labels check_predictions tensor_logging Trainer visualize_attention get_batch_all_label_pred to_tensor Attention EmbeddingLayer SeqModel FeatureEncoder Attention ElmoLayer SeqModel_Elmo FeatureEncoder Encoder flatten read_text_embeddings Dataset Corpus fix_padding js Jensen_Shannon match_M Average topK MSE intersection createHTML items replace log_scalar named_parameters is_available log_histogram numpy sum join tolist createHTML cpu numpy append enumerate list ndarray isinstance append range len extend map is_available cuda append Tensor abs max enumerate len max map cuda is_available tensor long enumerate print range append len print tolist flat_f1_score append zeros array len print Average min intersection append array range len print chain list mean_squared_error list js print chain array join close write map open | Github repository of ["Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions"](https://www.aclweb.org/anthology/P19-1112/) ACL 2019.  You can find the dataset here: http://ritual.uh.edu/resources/emphasis-2019/ @inproceedings{shirani2019learning, title={Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions}, author={Shirani, Amirreza and Dernoncourt, Franck and Asente, Paul and Lipka, Nedim and Kim, Seokhwan and Echevarria, Jose and Solorio, Thamar}, booktitle={Proceedings of the 57th Conference of the Association for Computational Linguistics}, pages={1167--1172}, year={2019} | 897 |
Riashat/Active-Learning-Bayesian-Convolutional-Neural-Networks | ['active learning'] | ['Deep Bayesian Active Learning with Image Data'] | ConvNets/active_learning/Acquisition_Functions/Maximum_Entropy/cifar/acquisition_highest_entropy_cifar10.py ConvNets/Model_Fitting_Analysis/Dropout_BALD/W_Decay_N=100/Q5_c_7p5_d_0p4.py ConvNets/FINAL_Averaged_Experiments/N=3000/Q=10/Dropout_Variation_Ratio/Dropout_Variation_Ratio_Q10_N3000.py ConvNets/Model_Fitting_Analysis/Dropout_BALD/W_Decay_N=100/Q5_c_0p2_d_0p4.py ConvNets/Uncertainty_Comparison/Dropout_Keras/Dropout_Regression_Bald_NewConfigs_Version5.py ConvNets/tests/keras/wrappers/test_scikit_learn.py active_learning/mnist_N1000/Dropout_Uncertainty_Model_Averaging/Dropout_Model_Averaging_Variation_Ratio_Q10_N1000.py active_learning/mnist_N1000/regression_example/Prob_BackProp_Random.py ConvNets/FINAL_Averaged_Experiments/Model_Architectures_Calibration/Non_Linearity/trial_log_loss.py ConvNets/active_learning/Acquisition_Functions/BCNN_Maximal_Uncertainty/Bayes_Segnet/efficient_Bayes_Segnet.py ConvNets/build/lib/keras/utils/test_utils.py ConvNets/keras/models.py ConvNets/FINAL_Averaged_Experiments/Dropout_and_Random_Uncertainty_Estimate/Dropout_Random_Variation_Ratio_Q10_N1000.py ConvNets/FINAL_Averaged_Experiments/Final_Experiments_Run/RESULTS ANALYSIS/RunTime_Comparison.py ConvNets/examples/antirectifier.py ConvNets/FINAL_Averaged_Experiments/Final_Experiments_Run/MBR_Multi_Class/MBR_Multi_Class_Original.py ConvNets/Cluster_Experiments/Dropout_Bald/New_Dropout_BALD_mnist.py ConvNets/build/lib/keras/preprocessing/image.py ConvNets/FINAL_Averaged_Experiments/Final_Experiments_Run/Dropout_Max_Entropy_Q10_N1000.py ConvNets/Comparison_Plots/Pooled_Images/Pooled_Images.py active_learning/mnist_N1000/cnn_model_architectures/VGG8_Bald_Q10_N1000.py ConvNets/active_learning/Acquisition_Functions/BCNN_Maximal_Uncertainty/Bayes_Segnet/Bayes_Segnet.py ConvNets/active_learning/Acquisition_Functions/Bayesian_Active_Learning/Dropout_BALD/Dropout_BALD_acquisition.py ConvNets/Cluster_Experiments/Dropout_Max_Entropy/Cluster_GPU_BCNN_Max_Entropy_CIFAR10.py ConvNets/FINAL_Averaged_Experiments/Final_Experiments_Run/Non_Linearity/Sigmoid_Dropout_BALD_Q10_N1000.py ConvNets/RESULTS/Third_Subset_Results/Query=1_Exp1/Loss/Comparison_Variation_Ratio.py ConvNets/active_learning/Acquisition_Functions/Maximum_Entropy/mnist/efficient_highest_entropy.py ConvNets/FINAL_Averaged_Experiments/Final_Experiments_Run/Dropout_Model_Averaging/Previous_Results/Compare_Dropout_Model_Averaging.py ConvNets/FINAL_Averaged_Experiments/ALL_RESULTS/ANALYSIS/Comparison_Dropout_Softmax/Small_Data_Accuracy_Comparison.py ConvNets/FINAL_Averaged_Experiments/Final_Experiments_Run/Binary_Comparison/Binary_VarRatio_Q10_N600.py ConvNets/Uncertainty_Comparison/Uncertainty_Estimate_Comparison_Results/Accuracy_Small_Data.py ConvNets/active_learning/Acquisition_Functions/Minimum_Expected_Entropy/trial.py ConvNets/RESULTS/Second_Results/1st/Loss/Comparison_Max_Entropy.py ConvNets/RESULTS/Second_Results/1st/Accuracy/Accuracy.py ConvNets/FINAL_Averaged_Experiments/N=100/Q=5/c=2.5_Experiments/Dropout_Segnet_Q5_N100.py ConvNets/RESULTS/Third_Subset_Results/Query=10/Pooled_Images/Pooled_Images.py ConvNets/tests/keras/layers/test_call.py ConvNets/build/lib/keras/backend/theano_backend.py ConvNets/Cluster_Experiments/Max_Entropy/trial.py ConvNets/examples/imdb_bidirectional_lstm.py ConvNets/Uncertainty_Comparison/deepGP_approxEP-master/theano/tests/DGPEP_AL_Random.py ConvNets/FINAL_Averaged_Experiments/Final_Experiments_Run/Uncertainty_Estimate_Result_Analysis/Accuracy_Comparison.py ConvNets/active_learning/mnist_cnn.py ConvNets/FINAL_Averaged_Experiments/ALL_RESULTS/ANALYSIS/Softmax_Output/Accuracy.py ConvNets/FINAL_Averaged_Experiments/ALL_RESULTS/ANALYSIS/Q=1_N=100/Accuracy.py ConvNets/FINAL_Averaged_Experiments/N=1000/Q=10/BvSB/BvSB_Q10_N1000.py ConvNets/FINAL_Averaged_Experiments/N=3000/Q=100/Minimum_Expected_Entropy/MEE_Q100_N3000.py ConvNets/Uncertainty_Comparison/black_box_alpha_BNN/boston_housing/theano/experiment_approximate_VI.py ConvNets/Uncertainty_Comparison/black_box_alpha_BNN/boston_housing/theano/network_layer.py ConvNets/FINAL_Averaged_Experiments/Final_Experiments_Run/N100_Dropout_Least_Confident.py ConvNets/Cluster_Experiments/Dropout_Variation_Ratio/Cluster_GPU_Variation_Ration.py ConvNets/FINAL_Averaged_Experiments/N=1000/Q=5/Max_Entropy/Max_Entropy_Q5_N1000.py ConvNets/PAPER_SUBMISSION_RESULTS/Main_Acquisition_Functions_Comparison/Max_Entropy_Paper_Q10_N1000.py ConvNets/Uncertainty_Comparison/Dropout_Keras/trial.py ConvNets/FINAL_Averaged_Experiments/Dropout_and_Random_Uncertainty_Estimate/Dropout_Bald_Q10_N1000.py ConvNets/Model_Fitting_Analysis/Dropout_BALD/W_Decay_N=100/Q5_c_2p5_d_0p3.py ConvNets/FINAL_Averaged_Experiments/CIFAR10_Experiments/Q=10_N=3000/Max_Entropy_CIFAR10_Q10_N3000.py ConvNets/Model_Fitting_Analysis/Dropout_BALD/W_Decay_N=100/Results/c=2.5p=0.3/Accuracy.py ConvNets/RESULTS/Second_Results/2nd/Loss/Comparison_Random.py ConvNets/Main_Experiments/Dropout_Max_Entropy/Subset_Dropout_Max_Entropy_mnist.py ConvNets/FINAL_Averaged_Experiments/Binary_Classification/Dropout_Variation_Ratio/Binary_Variation_Ratio_Q10_N1000.py ConvNets/PAPER_SUBMISSION_RESULTS/Binary_Classification/Binary_Accuracy.py ConvNets/active_learning/trial_mnist_cnn.py ConvNets/FINAL_Averaged_Experiments/N=3000/Q=5/Random/Random_Q5_N3000.py ConvNets/FINAL_Averaged_Experiments/Final_Experiments_Run/Max_Entropy_Q10_N1000.py ConvNets/Model_Fitting_Analysis/Dropout_BALD/W_Decay_N=100/Results/c=1.25p=0.4/Comparison_Bald.py ConvNets/FINAL_Averaged_Experiments/ALL_RESULTS/ANALYSIS/Query_Rate_Comparison/Query_Comparison_N1000.py 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ConvNets/Cluster_Experiments/Max_Entropy/Cluster_GPU_Highest_Entropy_CIFAR10.py ConvNets/FINAL_Averaged_Experiments/Final_Experiments_Run/RESULTS ANALYSIS/Binary_Accuracy.py ConvNets/Uncertainty_Comparison/black_box_alpha_BNN/boston_housing/theano/BB_Alpha_Value1_AL_Random.py ConvNets/build/lib/keras/datasets/cifar10.py ConvNets/tests/keras/layers/test_convolutional.py ConvNets/RESULTS/Third_Subset_Results/Query=1_Exp1/Loss/Comparison_Segnet.py ConvNets/FINAL_Averaged_Experiments/N=1000/Q=1/Dropout_Least_Confident/Dropout_Least_Confident_Uncertainty_Sampling_Q1_N1000.py ConvNets/FINAL_Averaged_Experiments/CIFAR10_Experiments/Q=10_N=3000/Random_CIFAR10_Q10_N3000.py ConvNets/FINAL_Averaged_Experiments/Final_Experiments_Run/N100_Dropout_Bald.py ConvNets/active_learning/Acquisition_Functions/BCNN_Maximal_Uncertainty/Variation_Ratio/trial_variation_ratio.py ConvNets/active_learning/Acquisition_Functions/Random_Acquisition/cifar/trial.py 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ConvNets/Uncertainty_Comparison/Probabilistic-Backpropagation-master/theano/test_PBP_net.py ConvNets/FINAL_Averaged_Experiments/Model_Architectures_Calibration/Architectures/VGG8_Bald_Q10_N1000.py ConvNets/tests/keras/preprocessing/test_image.py ConvNets/FINAL_Averaged_Experiments/Final_Experiments_Run/RESULTS ANALYSIS/N100_Compare_Dropout.py active_learning/mnist_N1000/regression_example/AEP_DGP_Bald.py ConvNets/Model_Fitting_Analysis/Dropout_BALD/W_Decay_N=100/Q5_c_2p5_d_0p4.py ConvNets/FINAL_Averaged_Experiments/Final_Experiments_Run/BvSB_Q10_N1000.py ConvNets/build/lib/keras/layers/__init__.py ConvNets/Model_Fitting_Analysis/Dropout_BALD/W_Decay_N=100/Results/c=1.75p=0.45/Accuracy.py ConvNets/FINAL_Averaged_Experiments/N=1000/Q=10/Minimum_Expected_Entropy/MEE_Q10_N1000.py ConvNets/active_learning/Acquisition_Functions/BCNN_Maximal_Uncertainty/GPU/Variation_Ratio/Average_GPU_Variation_Ratio.py ConvNets/RESULTS/Cluster_Experiment_Results/2nd/Accuracy/All_Accuracy.py ConvNets/PAPER_SUBMISSION_RESULTS/Binary_Classification/Binary_Bald_Q10_N1000.py ConvNets/build/lib/keras/activations.py ConvNets/RESULTS/Second_Results/2nd/Loss/Comparison_Bald.py ConvNets/Uncertainty_Comparison/black_box_alpha_BNN/boston_housing/theano/BB_Alpha_Value0.5_AL_Random.py ConvNets/FINAL_Averaged_Experiments/Comparison_SSL/Dropout_Bald_Weighted_Inputs_Q1_N100.py ConvNets/build/lib/keras/backend/tensorflow_backend.py ConvNets/FINAL_Averaged_Experiments/ALL_RESULTS/ANALYSIS/Binary/Accuracy.py deprocess_image get tanh softplus relu linear sigmoid softmax hard_sigmoid TensorBoard EarlyStopping RemoteMonitor BaseLogger ModelCheckpoint History LearningRateScheduler CallbackList Callback get NonNeg Constraint UnitNorm MaxNorm get normal he_uniform glorot_uniform zero glorot_normal get_fans lecun_uniform identity he_normal orthogonal uniform one make_batches model_from_json standardize_y Graph slice_X Sequential standardize_weights model_from_config weighted_objective batch_shuffle standardize_X model_from_yaml Model get_function_name get categorical_crossentropy cosine_proximity hinge mean_absolute_error root_mean_squared_error mean_squared_logarithmic_error mean_absolute_percentage_error squared_hinge mean_squared_error binary_crossentropy poisson get Adagrad Optimizer Adadelta Adamax Adam RMSprop clip_norm SGD kl_divergence l1l2 ActivityRegularizer get l1 l2 Regularizer activity_l2 activity_l1 activity_l1l2 WeightRegularizer set_epsilon cast_to_floatx floatx set_floatx epsilon function zeros_like gradients softplus l2_normalize _set_session variable get_value flatten random_uniform permute_dimensions gather argmax max abs log round clip resize_images spatial_2d_padding exp repeat_elements set_value random_normal ones transpose argmin squeeze placeholder shape conv2d cast count_params hard_sigmoid expand_dims sum prod temporal_padding switch ones_like Function categorical_crossentropy dropout concatenate relu square mean sqrt eval softmax pool2d tile equal rnn minimum tanh reshape min _get_session ndim maximum dot pow batch_flatten any repeat sigmoid zeros std binary_crossentropy function zeros_like gradients softplus l2_normalize variable get_value _on_gpu flatten random_uniform permute_dimensions gather argmax max abs log round clip resize_images spatial_2d_padding exp repeat_elements set_value random_normal ones transpose argmin squeeze placeholder shape conv2d cast count_params hard_sigmoid expand_dims sum prod temporal_padding switch ones_like Function categorical_crossentropy dropout concatenate relu square mean sqrt eval softmax pool2d tile equal rnn minimum tanh reshape min ndim maximum batch_flatten dot pow sigmoid any repeat zeros std binary_crossentropy load_batch load_data load_data get_file ParanoidURLopener load_data load_data get_word_index load_data PReLU ELU ThresholdedReLU ThresholdedLinear LeakyReLU ParametricSoftplus Graph Sequential UpSampling2D conv_output_length _Pooling1D AveragePooling1D Convolution1D UpSampling1D _Pooling2D MaxPooling2D AveragePooling2D ZeroPadding2D Convolution2D MaxPooling1D ZeroPadding1D SiameseHead add_shared_layer Activation Flatten RepeatVector Reshape Layer LambdaMerge Dropout Masking Highway Lambda Dense AutoEncoder ActivityRegularization MaskedLambda MaskedLayer TimeDistributedMerge Permute Merge Siamese MaxoutDense TimeDistributedDense Embedding GaussianDropout GaussianNoise BatchNormalization Recurrent LSTM GRU SimpleRNN img_to_array random_channel_shift random_zoom load_img horizontal_flip random_barrel_transform vertical_flip list_pictures random_rotation random_shear ImageDataGenerator random_shift array_to_img pad_sequences skipgrams make_sampling_table base_filter text_to_word_sequence one_hot Tokenizer make_tuple Progbar get_from_module load_array HDF5Matrix save_array get_layer container_from_config model_summary multiclass_logloss binary_logloss probas_to_classes to_categorical accuracy normalize categorical_probas_to_classes get_test_data plot layer_typename to_graph ModelToDot get_layer_to_name BaseWrapper KerasRegressor KerasClassifier get_earliest_class_that_defined_member class_to_source_link get_method_signature process_method_docstring get_classes_ancestors process_class_docstring class_to_docs_link code_snippet CharacterTable colors Antirectifier parse_stories get_stories vectorize_stories tokenize parse_stories get_stories vectorize_stories tokenize continuity_loss deprocess_image Evaluator eval_loss_and_grads preprocess_image preprocess_data preprocess_labels make_submission load_data sample train_model deprocess_image Evaluator gram_matrix eval_loss_and_grads content_loss total_variation_loss preprocess_image style_loss get tanh softplus relu linear sigmoid softmax hard_sigmoid TensorBoard EarlyStopping RemoteMonitor BaseLogger ModelCheckpoint History LearningRateScheduler CallbackList Callback get NonNeg Constraint UnitNorm MaxNorm get normal he_uniform glorot_uniform zero glorot_normal get_fans lecun_uniform identity he_normal orthogonal uniform one make_batches model_from_json standardize_y Graph slice_X Sequential standardize_weights model_from_config weighted_objective batch_shuffle standardize_X model_from_yaml Model get_function_name get categorical_crossentropy cosine_proximity hinge mean_absolute_error root_mean_squared_error mean_squared_logarithmic_error mean_absolute_percentage_error squared_hinge mean_squared_error binary_crossentropy poisson get Adagrad Optimizer Adadelta Adamax Adam RMSprop clip_norm SGD kl_divergence l1l2 ActivityRegularizer get l1 l2 Regularizer activity_l2 activity_l1 activity_l1l2 WeightRegularizer set_epsilon cast_to_floatx floatx set_floatx epsilon function zeros_like gradients softplus l2_normalize _set_session variable get_value flatten random_uniform permute_dimensions gather argmax max abs log round clip resize_images spatial_2d_padding exp repeat_elements set_value random_normal ones transpose argmin squeeze placeholder shape conv2d cast count_params hard_sigmoid expand_dims sum prod temporal_padding switch ones_like Function categorical_crossentropy dropout concatenate relu square mean sqrt eval softmax pool2d tile equal rnn minimum tanh reshape min _get_session ndim maximum dot pow batch_flatten any repeat sigmoid zeros std binary_crossentropy function zeros_like gradients softplus l2_normalize variable get_value _on_gpu flatten random_uniform permute_dimensions gather argmax max abs log round clip resize_images spatial_2d_padding exp repeat_elements set_value random_normal ones transpose argmin squeeze placeholder shape conv2d cast count_params hard_sigmoid expand_dims sum prod temporal_padding switch ones_like Function categorical_crossentropy dropout concatenate relu square mean sqrt eval softmax pool2d tile equal rnn minimum tanh reshape min ndim maximum batch_flatten dot pow sigmoid any repeat zeros std binary_crossentropy load_batch load_data load_data get_file ParanoidURLopener load_data load_data get_word_index load_data PReLU ELU ThresholdedReLU ThresholdedLinear LeakyReLU ParametricSoftplus Graph Sequential UpSampling2D conv_output_length _Pooling1D AveragePooling1D Convolution1D UpSampling1D _Pooling2D MaxPooling2D AveragePooling2D ZeroPadding2D Convolution2D MaxPooling1D ZeroPadding1D SiameseHead add_shared_layer Activation Flatten RepeatVector Reshape Layer LambdaMerge Dropout Masking Highway Lambda Dense AutoEncoder ActivityRegularization MaskedLambda MaskedLayer TimeDistributedMerge Permute Merge Siamese MaxoutDense TimeDistributedDense Embedding GaussianDropout GaussianNoise BatchNormalization Recurrent LSTM GRU SimpleRNN img_to_array random_channel_shift random_zoom load_img horizontal_flip random_barrel_transform vertical_flip list_pictures random_rotation random_shear ImageDataGenerator random_shift array_to_img pad_sequences skipgrams make_sampling_table base_filter text_to_word_sequence one_hot Tokenizer make_tuple Progbar get_from_module load_array HDF5Matrix save_array get_layer container_from_config model_summary multiclass_logloss binary_logloss probas_to_classes to_categorical accuracy normalize categorical_probas_to_classes get_test_data plot layer_typename to_graph ModelToDot get_layer_to_name BaseWrapper KerasRegressor KerasClassifier data model test_loss_masking test_masking _test_weights_sequential create_graph_model test_sequential create_sequential_model test_graph _test_weights_graph test_Dense check_layer_output_shape test_RepeatVector test_MaxPooling1D test_AveragePooling1D test_SimpleRNN test_Permute test_Convolution1D test_Reshape test_TimeDistributedDense test_MaxPooling2D test_Flatten test_UpSampling2D test_Convolution2D test_ZeroPadding1D test_ZeroPadding2D test_UpSampling1D test_AveragePooling2D test_image_classification test_temporal_classification test_temporal_regression test_sequence_to_sequence test_stacked_lstm_char_prediction test_vector_regression test_vector_classification test_linear test_softplus get_standard_values test_tanh test_softmax test_relu test_hard_sigmoid test_sigmoid test_EarlyStopping test_ModelCheckpoint test_LearningRateScheduler test_TensorBoard test_maxnorm test_unitnorm test_nonneg test_identity test_identity_oddballs test_he_normal _runner test_glorot_uniform test_identity test_orthogonal test_glorot_normal test_lecun_uniform test_he_uniform test_uniform test_zero test_one test_normal test_2o_1i_sample_weights test_merge_overlap test_siamese_3 _get_test_data test_siamese_5 test_merge_concat test_sequential_fit_generator test_sequential_count_params test_recursive test_siamese_2 test_1o_1i_2 test_create_output test_1o_2i test_1o_1i test_count_params test_siamese_4 test_merge_sum test_graph_fit_generator test_siamese_1 test_sequential test_merge_recursivity test_lambda test_2o_1i_weights test_merge_dot test_rmsprop test_adagrad test_adamax test_adam test_adadelta test_sgd get_model _test_optimizer test_A_reg test_W_reg create_model check_single_tensor_operation check_two_tensor_operation TestBackend test_imdb test_mnist test_cifar test_reuters test_leaky_relu test_prelu test_thresholded_relu get_standard_values test_parametric_softplus test_thresholded_linear test_elu test_sequential_call test_layer_call test_convolution_2d test_averagepooling_2d test_averagepooling_1d test_convolution_1d test_zero_padding_2d test_upsampling_1d test_upsampling_2d test_maxpooling_2d test_maxpooling_1d test_dense test_activation test_time_dist_dense test_merge test_sequences test_non_zero_output test_base test_connections test_siamese_all test_repeat_vector test_flatten test_maxout_dense _runner test_autoencoder_second_layer test_time_dist_merge test_act_reg test_autoencoder test_naming test_non_zero test_masked test_input_output test_highway test_dropout test_reshape test_siamese_theano_only test_unitnorm_constraint test_GaussianNoise test_GaussianDropout _runner test_batchnorm_shapes test_batchnorm_mode_1 test_batchnorm_nested test_batchnorm_weight_init test_batchnorm_config test_batchnorm_save_weights test_batchnorm_mode_0 test_SimpleRNN test_LSTM test_GRU _runner setup_function test_image_data_generator teardown_function test_make_sampling_table test_skipgrams test_pad_sequences test_tokenizer test_one_hot test_keras_regressor test_keras_classifier VGG_16 VGG_19 VGG_8 VGG_Net AlexNet GoogLeNet LeNet5 make_batches fit_q WeightsParser make_functions BB_alpha make_batches LogSumExp adam Network Network_layer Network Network_layer AEPDGP_net d_trace_MKzz_dhypers compute_psi2 chol2inv compute_kernel matrixInverse compute_psi1 FITC_network AEPDGP_net EQ_kernel FITC_layer PBP_net load_PBP_net_from_file Network Network_layer PBP PBP_net load_PBP_net_from_file Prior transpose astype clip ndim rnn get_fans sqrt get_fans sqrt get_fans sqrt get_fans sqrt get_fans sqrt normal reshape svd expand_dims asarray int reshape shuffle flatten len ceil int float tolist hasattr reshape argmax asarray isinstance load loads get pop dict container_from_config compile string_types isinstance epsilon abs inf clip epsilon clip inf log l2_normalize switch str Session Variable asarray initializer run get_shape get_shape len get_shape len get_shape len get_shape square reduce_mean cast len get_shape cast len get_shape reduce_any cast bool len get_shape len get_shape len inf cast clip_by_value get_shape len resize_nearest_neighbor value permute_dimensions as_list split pack reshape run pack reduce_any transpose unpack step_function reverse zip append cond cast clip_by_value cast clip_by_value cast clip_by_value log cast clip_by_value randint get_shape len transpose cast transpose cast avg_pool max_pool randint randint len clip tuple repeat_elements stack insert ndim addbroadcast shape zeros shape zeros asarray squeeze dimshuffle scan minimum softmax clip sigmoid clip RandomStreams sqrt sum square eval dimshuffle dnn_conv max_pool_2d dimshuffle RandomStreams RandomStreams load items reshape close open get_file join str reshape zeros range load_batch join print extractall retrieve close open expanduser makedirs load seed endswith close shuffle zip append max open get_file open add SiameseHead Siamese range len uniform rotate int uniform shift fliplr range flipud range uniform zoom transpose reshape transpose asarray convert open astype max enumerate len list array range log seed int min shuffle append randint max range enumerate len punctuation replace len translate lower maketrans split text_to_word_sequence string_types get isinstance defaultdict dtype open_file createCArray close from_dtype shape root data empty open_file close get deepcopy pop items add_input add_output isinstance Graph Sequential Merge get_layer append add_node input_order str input_shape layers display print display_layer_info nodes getattr output_order asarray zeros max range len atleast_1d norm minimum subtract maximum sum log sum log normal random randint zeros range chain items write_png to_graph ABCMeta get_classes_ancestors append getargspec defaults zip lower replace __module__ str replace __module__ sub replace sub replace int strip map append tokenize split parse_stories readlines append zeros len expand_dims imread astype imresize square reshape astype f_outputs read_csv copy transform StandardScaler fit to_categorical astype LabelEncoder int32 fit print format sum exp log evaluate print reshape to_categorical astype now shape compile fit dot transpose batch_flatten gram_matrix square reshape to_categorical astype load_data evaluate print Sequential fit add Dense Activation compile Dropout seed Sequential predict fit add Masking array compile TimeDistributedDense get ones reshape variable weighted_objective eval weighted_loss Dense Sequential Activation add add_output add_input Graph Dense add_node train_on_batch test_on_batch evaluate fit train_on_batch test_on_batch evaluate fit _test_weights_sequential create_sequential_model compile create_graph_model _test_weights_graph compile set_input_shape function placeholder shape len check_layer_output_shape random Reshape random check_layer_output_shape Permute check_layer_output_shape random Flatten RepeatVector check_layer_output_shape random Dense check_layer_output_shape random check_layer_output_shape random TimeDistributedDense check_layer_output_shape random Convolution1D check_layer_output_shape random Convolution2D MaxPooling1D check_layer_output_shape random AveragePooling1D check_layer_output_shape random random check_layer_output_shape MaxPooling2D random check_layer_output_shape AveragePooling2D UpSampling1D check_layer_output_shape random check_layer_output_shape UpSampling2D random check_layer_output_shape random ZeroPadding1D random check_layer_output_shape ZeroPadding2D check_layer_output_shape random SimpleRNN seed get_test_data Sequential to_categorical compile fit seed get_test_data Sequential to_categorical add GRU compile fit seed get_test_data Sequential add GRU compile fit seed get_test_data Sequential fit add compile TimeDistributedDense seed chr ord len Sequential fit ascii_lowercase zeros argmax range compile enumerate seed get_test_data Sequential to_categorical compile fit seed get_test_data Sequential compile fit function placeholder softmax get_standard_values assert_allclose function softplus placeholder get_standard_values assert_allclose function placeholder sigmoid get_standard_values vectorize assert_allclose function placeholder get_standard_values hard_sigmoid vectorize assert_allclose assert_allclose function get_standard_values placeholder tanh function placeholder get_standard_values assert_allclose remove get_test_data Sequential to_categorical add Dense compile fit get_test_data Sequential to_categorical add Dense compile fit get_test_data Sequential to_categorical add Dense compile fit _set_session to_categorical _get_session get_test_data T maxnorm variable norm_instance eval assert_allclose nonneg variable nonneg_instance identity_instance identity identity abs variable unitnorm sqrt eval unitnorm_instance sum max get_value init uniform _runner normal _runner sqrt lecun_uniform _runner sqrt glorot_uniform _runner sqrt glorot_normal _runner sqrt he_uniform _runner sqrt he_normal _runner orthogonal _runner _runner zero _runner one _runner seed to_categorical get_test_data evaluate Sequential fit_generator add Dense data_generator _get_test_data Activation compile len Sequential save_weights _get_test_data Activation train_on_batch to_yaml predict_classes add predict model_from_json get_config Dense predict_proba load_weights model_from_yaml remove evaluate summary to_json fit remove evaluate predict_classes get_config Sequential predict Merge add Dense predict_proba save_weights _get_test_data load_weights Activation compile fit Sequential Merge add Dense _get_test_data Activation compile remove evaluate predict_classes get_config Sequential predict Merge add Dense predict_proba save_weights _get_test_data load_weights Activation compile fit remove evaluate predict_classes get_config Sequential predict Merge add Dense predict_proba save_weights _get_test_data load_weights Activation compile fit train_on_batch remove evaluate predict_classes get_config Sequential predict Merge add Dense predict_proba save_weights _get_test_data load_weights Activation compile fit remove evaluate predict_classes Lambda Sequential predict get_config add LambdaMerge Dense predict_proba save_weights _get_test_data load_weights Activation compile fit Sequential add Dense Activation compile remove evaluate predict_classes get_config Sequential predict add Dense predict_proba Siamese _get_test_data save_weights load_weights Activation compile fit remove evaluate predict_classes get_config Sequential predict Merge add Dense predict_proba save_weights _get_test_data load_weights add_shared_layer Activation compile fit add_output add_input evaluate Graph fit_generator Dense data_generator_graph compile add_node seed add_input add_output train_on_batch evaluate Graph fit predict test_on_batch Dense compile add_node add_output add_input train_on_batch evaluate Graph get_config fit predict test_on_batch Dense summary Activation compile add_node add_output add_input train_on_batch evaluate Graph get_config fit predict test_on_batch Dense compile add_node add_output add_input train_on_batch evaluate Graph get_config add_shared_node fit predict test_on_batch Dense compile add_node add_output add_input train_on_batch evaluate Graph get_config add_shared_node fit predict test_on_batch Dense compile add_node add_output add_input train_on_batch evaluate Graph get_config add_shared_node fit predict test_on_batch Dense compile add_node add_output add_input train_on_batch remove evaluate Graph fit predict test_on_batch Dense save_weights load_weights compile add_node add_output add_input train_on_batch evaluate Graph fit predict test_on_batch Dense uniform compile add_node add_output add_input evaluate Graph get_config Sequential fit predict add Dense compile add_node add_input train_on_batch evaluate Graph fit predict test_on_batch Dense compile add_node add_output add_input Graph Dense compile add_node Dense Sequential Activation add get_config get_model compile fit _test_optimizer SGD _test_optimizer RMSprop Adagrad _test_optimizer _test_optimizer Adadelta _test_optimizer Adam Adamax _test_optimizer Dense Sequential Activation add evaluate create_model compile fit evaluate create_model compile fit eval variable random assert_allclose eval variable random assert_allclose load_data load_data load_data load_data seed get_output get_config variable eval get_standard_values LeakyReLU assert_allclose seed PReLU get_output get_config variable random shape eval get_standard_values assert_allclose seed get_output exp ELU get_config variable eval get_standard_values assert_allclose seed get_output exp get_config variable build eval vstack ParametricSoftplus log assert_allclose seed get_output get_config variable eval get_standard_values ThresholdedLinear assert_allclose seed get_output get_config variable eval ThresholdedReLU get_standard_values assert_allclose function astype placeholder Dense floatx layer assert_allclose function model assert_allclose Sequential astype predict placeholder add Dense floatx model2 compile get_output ones get_config variable Convolution1D eval get_output ones get_config variable eval MaxPooling1D get_output ones get_config AveragePooling1D variable eval get_output ones get_config variable eval Convolution2D get_output ones get_config variable eval MaxPooling2D get_output ones get_config variable eval AveragePooling2D get_output ones get_config variable eval ZeroPadding2D assert_allclose get_output ones get_config variable UpSampling1D eval UpSampling2D get_output get_config rand variable eval repeat assert_allclose get_output get_input ones variable eval assert_allclose Layer get_output get_input ones variable Layer eval assert_allclose set_previous _runner Layer MaskedLayer _runner set_input_shape Merge _runner Layer _runner Dropout Activation _runner _runner Reshape Flatten _runner RepeatVector _runner Dense _runner ActivityRegularization _runner _runner TimeDistributedDense TimeDistributedMerge _runner Highway _runner AutoEncoder _runner Layer Sequential add Dense AutoEncoder compile MaxoutDense _runner train_on_batch Sequential random add Dense compile Masking function array Masking function array Masking function array get_config get_params build Dense get_output Siamese output_shape Dense get_output Siamese output_shape train_on_batch norm Embedding compile Sequential assert_allclose get_value astype add Dense array Activation Flatten GaussianNoise _runner GaussianDropout _runner get_output variable random eval seed normal get_output assert_allclose Sequential variable add mean eval beta gamma std BatchNormalization compile fit seed get_output variable mean eval beta gamma std BatchNormalization assert_allclose get_output variable beta gamma BatchNormalization seed get_output ones variable mean eval beta gamma std BatchNormalization assert_allclose get_config BatchNormalization get_weights BatchNormalization set_weights add_output add_input Graph Sequential add Dense Activation BatchNormalization compile add_node train_on_batch get_output_mask layer_class ones assert_allclose Sequential predict reset_states add compile _runner GRU _runner LSTM _runner seed str rand convert mkdir save range rmtree seed arange list_pictures vstack flow ImageDataGenerator append fit pad_sequences assert_allclose asarray make_sampling_table assert_allclose arange skipgrams one_hot fit_on_texts fit_on_sequences texts_to_matrix append texts_to_sequences_generator Tokenizer score Sequential KerasClassifier add Dense Activation fit score KerasRegressor Sequential add Dense Activation fit print Sequential add Dense MaxPooling2D Convolution2D Activation Flatten Dropout print Sequential add Dense MaxPooling2D Convolution2D Activation Flatten Dropout print Sequential add Dense MaxPooling2D ZeroPadding2D Convolution2D Activation Flatten Dropout print Sequential add Dense MaxPooling2D Convolution2D Activation Flatten Dropout print Sequential add Dense ZeroPadding2D MaxPooling2D Convolution2D Flatten Dropout print Sequential add Dense ZeroPadding2D MaxPooling2D Convolution2D Flatten Dropout print Sequential add Dense ZeroPadding2D MaxPooling2D Convolution2D Flatten Dropout randn num_weights WeightsParser sum log add_shape make_batches print grad energy_grad choice make_functions print_perf zip array range max grad float32 shape sqrt zip append shared zeros cdist exp cdist sqrt exp prod T exp ones outer dot sqrt sum prod T exp ones outer dot sqrt sum construct_from_dictionary_PBP_network load_object net | Riashat/Active-Learning-Bayesian-Convolutional-Neural-Networks | 898 |
Ricardozzf/sdsrcnn | ['pedestrian detection', 'semantic segmentation', 'autonomous driving'] | ['Illuminating Pedestrians via Simultaneous Detection & Segmentation'] | external/caffe/examples/web_demo/exifutil.py external/caffe/tools/extra/summarize.py external/caffe/python/caffe/io.py external/caffe/python/caffe/detector.py external/caffe/python/caffe/test/test_io.py external/caffe/scripts/copy_notebook.py external/caffe/python/caffe/pycaffe.py external/caffe/examples/web_demo/app.py external/caffe/tools/extra/resize_and_crop_images.py external/caffe/python/classify.py external/caffe/scripts/download_model_binary.py external/caffe/python/caffe/test/test_python_layer.py external/caffe/examples/pycaffe/layers/pascal_multilabel_datalayers.py external/caffe/examples/pycaffe/caffenet.py external/caffe/python/caffe/test/test_net_spec.py external/caffe/python/caffe/test/test_solver.py external/caffe/scripts/split_caffe_proto.py external/caffe/examples/pycaffe/tools.py external/caffe/python/caffe/net_spec.py external/caffe/python/draw_net.py external/caffe/tools/extra/extract_seconds.py external/caffe/python/caffe/test/test_coord_map.py external/caffe/python/caffe/classifier.py external/caffe/python/caffe/coord_map.py external/caffe/python/caffe/test/test_layer_type_list.py external/caffe/scripts/cpp_lint.py external/caffe/src/caffe/test/test_data/generate_sample_data.py external/caffe/examples/pycaffe/layers/pyloss.py external/caffe/python/caffe/test/test_net.py external/caffe/tools/extra/parse_log.py external/caffe/python/caffe/test/test_python_layer_with_param_str.py external/caffe/python/caffe/draw.py external/caffe/python/caffe/__init__.py external/caffe/python/detect.py external/caffe/examples/finetune_flickr_style/assemble_data.py external/caffe/python/train.py download_image make_net max_pool caffenet conv_relu fc_relu CaffeSolver SimpleTransformer EuclideanLossLayer start_tornado start_from_terminal embed_image_html classify_upload index allowed_file ImagenetClassifier classify_url open_oriented_im apply_orientation main main main parse_args train solve time Classifier coord_map UndefinedMapException conv_params coord_map_from_to AxisMismatchException inverse crop_params compose crop Detector get_edge_label draw_net get_layer_label get_pydot_graph choose_color_by_layertype get_pooling_types_dict draw_net_to_file Transformer blobproto_to_array datum_to_array array_to_blobproto array_to_datum resize_image arraylist_to_blobprotovector_str blobprotovector_str_to_arraylist load_image oversample Layers Function Parameters Top NetSpec assign_proto param_name_dict to_proto _Net_blobs _Net_forward_all _Net_set_input_arrays _Net_backward _Net_params _Net_forward _Net_outputs _Net_forward_backward_all _Net_blob_loss_weights _Net_batch _Net_get_id_name _Net_inputs _Net_layer_dict TestCoordMap coord_net_spec TestBlobProtoToArray TestArrayToDatum TestLayerTypeList TestLevels TestStages simple_net_file TestNet TestAllInOne lenet TestNetSpec silent_net anon_lenet exception_net_file parameter_net_file SimpleLayer phase_net_file TestPythonLayer ParameterLayer PhaseLayer python_net_file ExceptionLayer SimpleParamLayer TestLayerWithParam python_param_net_file TestSolver ParseNolintSuppressions CheckVlogArguments CheckSectionSpacing FindNextMultiLineCommentEnd ReplaceAll CheckForFunctionLengths _SetOutputFormat _IsTestFilename _VerboseLevel CheckBraces RemoveMultiLineComments ResetNolintSuppressions CheckForNonStandardConstructs _SetVerboseLevel PrintUsage _NestingState CheckIncludeLine CheckAccess _CppLintState Search CheckInvalidIncrement RemoveMultiLineCommentsFromRange CleansedLines CheckForBadCharacters UpdateIncludeState FindPreviousMatchingAngleBracket CheckEmptyBlockBody FindNextMultiLineCommentStart Match _NamespaceInfo CheckMakePairUsesDeduction CheckCheck IsBlankLine _SetFilters ProcessLine _FunctionState CheckPosixThreading GetLineWidth GetHeaderGuardCPPVariable IsCppString _IncludeState CheckSpacing _ClassInfo CheckForCopyright IsErrorSuppressedByNolint ProcessFileData CheckForMultilineCommentsAndStrings CloseExpression _PreprocessorInfo _OutputFormat CheckForIncludeWhatYouUse CheckSpacingForFunctionCall FindEndOfExpressionInLine FindNextMatchingAngleBracket _SetCountingStyle ProcessFile _IncludeError CleanseRawStrings CheckAltTokens CheckForNewlineAtEOF ParseArguments CheckForNonConstReference PrintCategories _Filters main FilesBelongToSameModule CheckCStyleCast FileInfo _BlockInfo CheckForHeaderGuard CheckCaffeDataLayerSetUp ReverseCloseExpression CleanseComments _DropCommonSuffixes _ClassifyInclude CheckStyle CheckCaffeAlternatives FindStartOfExpressionInLine _ShouldPrintError CheckComment Error _GetTextInside CheckLanguage CheckCaffeRandom GetPreviousNonBlankLine reporthook parse_readme_frontmatter model_checks_out valid_dirname get_start_time extract_seconds extract_datetime_from_line get_log_created_year imread urlretrieve Convolution InnerProduct Data SoftmaxWithLoss LRN Accuracy max_pool InnerProduct conv_relu fc_relu Dropout get read info load_image classify_image StringIO join replace info secure_filename save filename open_oriented_im classify_image fromarray replace astype save resize StringIO iteritems listen HTTPServer format print start WSGIContainer update start_tornado add_option OptionParser debug port parse_args ImagenetClassifier forward run hasattr _getexif astype float32 tile apply_orientation open transpose model_def endswith ArgumentParser save mean_file channel_swap output_file dirname expanduser parse_args input_file predict Classifier set_mode_cpu load time isdir print add_argument set_mode_gpu pretrained_model gpu len DataFrame Detector format to_hdf detect_selective_search mean set_index to_csv detect_windows read_csv add_argument ArgumentParser read NetParameter output_image_file rankdir Merge TRAIN draw_net_to_file TEST Process str join init_log start append new_uid range log len before_backward layers display add_callback after_backward after_forward Timer append before_forward range len max_iter restore time set_solver_count set_solver_rank add_callback set_device set_multiprocess SGDSolver after_backward set_mode_gpu layer_wise_reduce step bcast NCCL len get params array get params array crop_params conv_params pop collect_bottoms add fn coord_map compose coord_map_from_to items DESCRIPTOR batch_size str num_output get_pooling_types_dict add_edge get_edge_label Dot exclude get_layer_label add_node values choose_color_by_layertype Edge Node bottom append type layer include top data array diff shape BlobProto extend flat extend BlobProtoVector ParseFromString BlobProtoVector extend tostring shape Datum flat data len astype float32 tile zoom tuple resize fill empty array concatenate shape tile empty array LayerParameter NetParameter _to_proto extend Counter OrderedDict values iteritems hasattr isinstance extend add getattr setattr OrderedDict _blobs _blob_names zip OrderedDict _blob_loss_weights _blob_names zip OrderedDict layers _layer_names zip OrderedDict list keys list keys iteritems layers index set outputs _forward len iteritems _backward layers inputs index set len iteritems asarray extend copy next _batch itervalues forward len iteritems izip_longest asarray backward extend copy next _batch itervalues zip forward len ascontiguousarray concatenate itervalues zeros next range len data Pooling pool Convolution NetSpec Deconvolution conv Input NamedTemporaryFile str close write data Pooling pool1 conv2 pool2 ip1 relu1 SoftmaxWithLoss Convolution NetSpec DummyData ip2 ReLU InnerProduct label conv1 Pooling SoftmaxWithLoss Convolution DummyData ReLU InnerProduct data NetSpec DummyData Silence data2 error search add group clear compile compile compile SetOutputFormat SetCountingStyle SetFilters _Filters startswith IsErrorSuppressedByNolint _ShouldPrintError write IncrementErrorCount replace append Match group find startswith endswith range error FindNextMultiLineCommentEnd RemoveMultiLineCommentsFromRange FindNextMultiLineCommentStart rstrip find xrange len FindEndOfExpressionInLine xrange len FindStartOfExpressionInLine error min search I xrange len FileInfo RepositoryName sep sub ParseNolintSuppressions error startswith split GetHeaderGuardCPPVariable enumerate error enumerate error len error replace count error find error find error find error find error Search error match InnermostClass replace error escape Match Search error group Search Check error lines Count End group Begin xrange NumLines Match raw_lines Search error match group error Match group pop group append Search pop group append Search elided replace CheckSpacingForFunctionCall rfind error len group min CloseExpression NumLines sub xrange find CheckComment Match Search lines_without_raw_strings error group starting_linenum Match range Search error rfind len group ReverseCloseExpression Search Match CloseExpression find error Match CloseExpression find elided error strip group FindEndOfExpressionInLine xrange find Match CloseExpression len error Match finditer normalize isinstance GetLineWidth int InnermostClass CheckCheck error CheckAltTokens CheckBraces CheckSpacing CheckSectionSpacing CheckEmptyBlockBody CheckAccess GetHeaderGuardCPPVariable lines_without_raw_strings _DropCommonSuffixes RepositoryName match split CheckNextIncludeOrder CanonicalizeAlphabeticalOrder FileInfo error search group SetLastHeader match _ClassifyInclude Match pop end search set itervalues append M rstrip replace CheckCStyleCast error _GetTextInside CheckIncludeLine search group lstrip startswith Match ResetSection Search split rfind error group ReverseCloseExpression lstrip xrange findall Match Search ReplaceAll error Match Search endswith replace setdefault group search CleanseComments open FilesBelongToSameModule error search copy sub xrange NumLines FullName keys error search CheckPosixThreading ParseNolintSuppressions CheckVlogArguments CheckMakePairUsesDeduction CheckCaffeDataLayerSetUp CheckLanguage CheckInvalidIncrement CheckCaffeRandom CheckForNonConstReference check_fn Update CheckForNonStandardConstructs CheckStyle raw_lines CheckForMultilineCommentsAndStrings CheckCaffeAlternatives CheckForFunctionLengths CleansedLines _NestingState CheckForBadCharacters CheckForNewlineAtEOF _IncludeState NumLines RemoveMultiLineComments CheckForCopyright ResetNolintSuppressions CheckForHeaderGuard xrange CheckCompletedBlocks CheckForIncludeWhatYouUse ProcessLine _FunctionState Error rstrip endswith len write ProcessFileData _SetVerboseLevel range split write exit join write exit _VerboseLevel int getopt _SetOutputFormat set _SetVerboseLevel PrintCategories _SetFilters _OutputFormat PrintUsage _SetCountingStyle split getreader ParseArguments ResetErrorCounts stderr exit verbose_level PrintErrorCounts StreamReaderWriter ProcessFile getwriter int time write flush load join index int rfind datetime split getctime year strip extract_datetime_from_line get_start_time total_seconds strip write get_log_created_year close extract_datetime_from_line open | Ricardozzf/sdsrcnn | 899 |
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