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Functions305 in github.com/BlackHC/BatchBALD

⨍ Functions 305◇ Types & classes 28

↓ 1 callersFunctionrecover_args

src/recover_model.py:53

↓ 1 callersFunctionrecover_args

src/al_notebook/results_loader.py:164

↓ 1 callersMethodreset_mask

src/mc_dropout.py:85

↓ 1 callersFunctionsample_indices

(indices, total_length)

src/dataset_enum.py:412

↓ 1 callersFunctionshow_batch

(batch, **kwargs)

src/al_notebook/show_batch.py:40

↓ 1 callersMethodsnapshot

src/ignite_restoring_score_guard.py:72

↓ 1 callersFunctionstore_iteration_results

(engine: ignite.engine.Engine, store_object, log_interval=1000)

src/ignite_utils.py:40

↓ 1 callersFunctiontrain_model

( model: nn.Module, optimizer: optim.Optimizer, max_epochs, early_stopping_patience, num_i

src/train_model.py:22

src/joint_entropy/test_matmuls.py:45

src/joint_entropy/unoptimized/test_exact_batch.py:56

src/joint_entropy/unoptimized/test_sampling_batch.py:78

src/joint_entropy/unoptimized/test_exact_joint_probs.py:70

src/joint_entropy/unoptimized/test_sampling_sample.py:104

src/joint_entropy/test_matmuls.py:40

src/joint_entropy/unoptimized/test_exact_batch.py:51

src/joint_entropy/unoptimized/test_sampling_batch.py:73

src/joint_entropy/unoptimized/test_exact_joint_probs.py:65

src/joint_entropy/unoptimized/test_sampling_sample.py:99

src/joint_entropy/test_matmuls.py:55

src/joint_entropy/unoptimized/test_exact_batch.py:66

src/joint_entropy/unoptimized/test_sampling_batch.py:88

src/joint_entropy/unoptimized/test_exact_joint_probs.py:80

src/joint_entropy/unoptimized/test_sampling_sample.py:114

src/joint_entropy/test_matmuls.py:50

src/joint_entropy/unoptimized/test_exact_batch.py:61

src/joint_entropy/unoptimized/test_sampling_batch.py:83

src/joint_entropy/unoptimized/test_exact_joint_probs.py:75

src/joint_entropy/unoptimized/test_sampling_sample.py:109

src/joint_entropy/unoptimized/test_exact_batch.py:76

src/joint_entropy/unoptimized/test_sampling_batch.py:98

src/joint_entropy/unoptimized/test_exact_joint_probs.py:90

src/joint_entropy/unoptimized/test_sampling_sample.py:124

src/joint_entropy/unoptimized/test_exact_batch.py:71

src/joint_entropy/unoptimized/test_sampling_batch.py:93

src/joint_entropy/unoptimized/test_exact_joint_probs.py:85

src/joint_entropy/unoptimized/test_sampling_sample.py:119

Method__enter__

src/context_stopwatch.py:15

(self, exc_type, exc_value, traceback)

src/context_stopwatch.py:19

Method__getitem__

src/transformed_dataset.py:21

(self, desc, log_interval)

src/ignite_progress_bar.py:7

src/context_stopwatch.py:7

(self, bayesian_net: mc_dropout.BayesianModule)

src/sampler_model.py:62

(self, num_classes: int)

src/mc_dropout.py:24

(self, num_classes)

src/emnist_model.py:10

(self, dataset, *, transformer=None, vision_transformer=None)

src/transformed_dataset.py:13

(self, dataset: data.Dataset)

src/active_learning_data.py:10

(self, dataset: data.Dataset, target_length)

src/random_fixed_length_sampler.py:12

(self, num_classes)

src/mnist_model.py:9

(self, features, num_classes=1000, init_weights=True, smaller_head=False)

src/vgg_model.py:27

( self, *, patience, score_function, out_of_patience_callback,

src/ignite_restoring_score_guard.py:36

src/random_fixed_length_sampler.py:17

src/transformed_dataset.py:24

src/random_fixed_length_sampler.py:24

Method_get_sample_mask_shape

(self, sample_shape)

src/mc_dropout.py:198

Functionaggregate_accuracies

(stores, percentiles=None, thresholds=None)

src/al_notebook/results_loader.py:385

Methodapply_noise

src/dataset_enum.py:119

Functionbald_acquisition_function

src/acquisition_functions.py:30

(probs_B_K_C, prev_joint_probs_M_K=None)

src/joint_entropy/exact.py:59

(probs_B_K_C, samples_M_K)

src/joint_entropy/sampling.py:58

(probs_B_K_C, prev_joint_probs_M_K=None)

src/joint_entropy/unoptimized/exact.py:40

(probs_B_K_C, samples_M_K)

src/joint_entropy/unoptimized/sampling.py:37

Functionbatch_conditional_entropy_B

(logits_B_K_C, out_conditional_entropy_B=None)

src/joint_entropy/exact.py:88

Functionbatch_conditional_entropy_B

(logits_B_K_C, out_conditional_entropy_B=None)

src/joint_entropy/unoptimized/exact.py:69

Functionbatch_exact_joint_entropy_logits

This one switches between devices, too.

src/multi_bald.py:183

Functionbatch_jsd

:param batch_p: #batch x #classes :param q: #classes :return: #batch Jensen-Shannon Divergences

src/torch_utils.py:159

Functioncompute_acquisition_bag

( bayesian_model: nn.Module, acquisition_function: AcquisitionFunction, available_loader, num_

src/independent_batch_acquisition.py:15

Functioncompute_multi_bald_batch

( bayesian_model: nn.Module, available_loader, num_classes, k, b, target_size, ini

src/multi_bald.py:20

Functioncuda_meminfo

src/torch_utils.py:73

Functiondataset_subset_split

(dataset, indices)

src/subrange_dataset.py:12

src/run_experiment_no_al.py:158

src/run_experiment.py:231

Methoddeterministic_forward_impl

(self, x: Tensor)

src/vgg_model.py:58

Methodelapsed_time

src/context_stopwatch.py:12

Functionentropy_from_M_K

(joint_probs_M_K)

src/joint_entropy/exact.py:34

Functionentropy_from_M_K

(joint_probs_M_K)

src/joint_entropy/unoptimized/exact.py:33

Functioneval_bayesian_model_consistent

( bayesian_model: mc_dropout.BayesianModule, available_loader, num_classes, k=20, device=None )

src/sampler_model.py:13

Functionexact_module

src/joint_entropy/unoptimized/test_exact_batch.py:101

Functionexact_module

src/joint_entropy/unoptimized/test_sampling_batch.py:123

Functionexact_module

src/joint_entropy/unoptimized/test_exact_joint_probs.py:115

Functionexact_module

src/joint_entropy/unoptimized/test_sampling_sample.py:149

Methodextra_repr

src/mc_dropout.py:82

Functionfilter_dict

(d: dict, *, kv=None, k=None, v=None)

src/al_notebook/results_loader.py:131

src/sampler_model.py:66

(self, input: torch.Tensor)

src/sampler_model.py:80

(self, input_B: torch.Tensor, k: int)

src/mc_dropout.py:31

(self, input: torch.Tensor)

src/mc_dropout.py:101

src/test_acquisition_functions.py:88

Functionfrom_M_K

src/joint_entropy/sampling.py:48

Functionfrom_M_K

src/joint_entropy/unoptimized/sampling.py:27

Functiongather_accuracies

src/al_notebook/results_loader.py:396

Functiongc_cuda

src/al_notebook/torch_utils.py:14

Functionget_RepeatedMNIST

src/dataset_enum.py:79

Functionget_accuracy

src/al_notebook/results_loader.py:273

Functionget_any

src/al_notebook/results_loader.py:76

Functionget_cuda_available_memory

src/torch_utils.py:29

Functionget_diff_args_key2text

(stores, ignore_fields=())

src/al_notebook/results_loader.py:535

Functionget_samples_accuracy_I

Get all samples and accuracies for iterations (I) in a store. We never evaluate the final batch of chosen samples. Accuracies are always for the

src/al_notebook/results_loader.py:277

Functionget_stores_info

(stores, include_keys=False)

src/al_notebook/results_loader.py:488

Functionget_threshold_quantiles_key

src/al_notebook/results_loader.py:412

Functionget_top_n

(scores: np.ndarray, n)

src/independent_batch_acquisition.py:10

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