Index

_

__call__() (revelionn.early_stopping.EarlyStopping method) __getitem__() (revelionn.datasets.MultiLabeledImagesDataset method) (revelionn.datasets.SemiSupervisedImagesDataset method)

__init__() (revelionn.datasets.SemiSupervisedImagesDataset method) __len__() (revelionn.datasets.MultiLabeledImagesDataset method)

A

activation (revelionn.activation_extraction.ActivationExtractor attribute)

activation_extractor (revelionn.mapping_module.MappingModelProcessing attribute) ActivationExtractor (class in revelionn.activation_extraction)

B

best_score (revelionn.early_stopping.EarlyStopping attribute)

C

class_labels (revelionn.mapping_module.MappingModelProcessing attribute) classes (revelionn.main_module.MainModelProcessing attribute) classify_images() (revelionn.occlusion.MultiLabelClassifier method) common_layers (revelionn.mapping_nets.simultaneous_mapping_net.MappingModule attribute) ConceptExtractor (class in revelionn.concept_extraction) convert_to_rvl_format() (in module revelionn.utils.model)

count_num_activations() (revelionn.activation_extraction.ActivationExtractor method), [1] counter (revelionn.early_stopping.EarlyStopping attribute) create_dataloader() (in module revelionn.datasets) create_layers_dict() (revelionn.activation_extraction.ActivationExtractor method), [1] create_subgraph() (revelionn.concept_extraction.ConceptExtractor method) (revelionn.concept_extraction.ConceptExtractor static method)

D

decoder_channels (revelionn.mapping_nets.simultaneous_mapping_net.SimultaneousMappingNet attribute) decoders (revelionn.mapping_nets.simultaneous_mapping_net.SimultaneousMappingNet attribute) delta (revelionn.early_stopping.EarlyStopping attribute)

device (revelionn.activation_extraction.ActivationExtractor attribute) (revelionn.main_module.MainModelProcessing attribute) (revelionn.mapping_module.MappingModelProcessing attribute)

E

early_stop (revelionn.early_stopping.EarlyStopping attribute) EarlyStopping (class in revelionn.early_stopping) evaluate_model() (revelionn.main_module.MainModelProcessing method), [1] (revelionn.mapping_module.MappingModelProcessing method), [1], [2] (revelionn.mapping_trainer.MappingTrainer method), [1]

exhaustive_search() (revelionn.concept_extraction.ConceptExtractor method), [1] explain_target_concept() (in module revelionn.utils.explanation) extract_concepts_from_img() (in module revelionn.utils.explanation)

F

find_layer_predicate_recursive() (revelionn.activation_extraction.ActivationExtractor method), [1] find_layers_types_recursive() (revelionn.activation_extraction.ActivationExtractor method), [1] forward() (revelionn.mapping_nets.simultaneous_mapping_net.LayerDecoder method), [1] (revelionn.mapping_nets.simultaneous_mapping_net.MappingModule method), [1] (revelionn.mapping_nets.simultaneous_mapping_net.SimultaneousMappingNet method), [1] (revelionn.mapping_nets.single_mapping_net.SingleMappingNet method), [1]

G

generate_layers() (revelionn.mapping_nets.simultaneous_mapping_net.MappingModule method) (revelionn.mapping_nets.simultaneous_mapping_net.MappingModule static method) get_activation() (revelionn.activation_extraction.ActivationExtractor method), [1] get_activation_extractor() (revelionn.mapping_module.MappingModelProcessing method), [1] get_activations() (revelionn.activation_extraction.ActivationExtractor method), [1] get_class_labels() (revelionn.main_module.MainModelProcessing method), [1] (revelionn.mapping_module.MappingModelProcessing method), [1] get_config() (revelionn.occlusion.MultiLabelClassifier method) get_decoder_channels() (revelionn.mapping_nets.simultaneous_mapping_net.SimultaneousMappingNet method), [1] get_device() (revelionn.main_module.MainModelProcessing method), [1] get_in_features() (revelionn.mapping_nets.single_mapping_net.SingleMappingNet method)

get_labels() (revelionn.occlusion.MultiLabelClassifier method) get_layer_name_by_number() (revelionn.activation_extraction.ActivationExtractor method), [1] get_layers_dict() (revelionn.activation_extraction.ActivationExtractor method), [1] get_layers_for_research() (revelionn.activation_extraction.ActivationExtractor method), [1] get_layers_types() (revelionn.activation_extraction.ActivationExtractor method), [1] get_main_net() (revelionn.activation_extraction.ActivationExtractor method), [1] (revelionn.main_module.MainModelProcessing method), [1] get_mapping_net() (revelionn.mapping_module.MappingModelProcessing method), [1] get_num_neurons_list() (revelionn.mapping_nets.single_mapping_net.SingleMappingNet method), [1] get_num_output_neurons() (revelionn.mapping_nets.simultaneous_mapping_net.SimultaneousMappingNet method), [1] get_num_outs() (revelionn.mapping_nets.simultaneous_mapping_net.SimultaneousMappingNet method), [1] get_num_shared_neurons() (revelionn.mapping_nets.simultaneous_mapping_net.SimultaneousMappingNet method), [1]

H

heuristic_search() (revelionn.concept_extraction.ConceptExtractor method), [1]

I

img_dir (revelionn.datasets.MultiLabeledImagesDataset attribute) (revelionn.datasets.SemiSupervisedImagesDataset attribute) img_labels (revelionn.datasets.MultiLabeledImagesDataset attribute) (revelionn.datasets.SemiSupervisedImagesDataset attribute)

in_features (revelionn.mapping_nets.single_mapping_net.SingleMappingNet attribute) is_concatenate (revelionn.activation_extraction.ActivationExtractor attribute)

L

labels() (revelionn.datasets.MultiLabeledImagesDataset method), [1] LayerDecoder (class in revelionn.mapping_nets.simultaneous_mapping_net) layers (revelionn.mapping_nets.simultaneous_mapping_net.LayerDecoder attribute) layers_dict (revelionn.activation_extraction.ActivationExtractor attribute) layers_for_research (revelionn.activation_extraction.ActivationExtractor attribute) layers_types (revelionn.activation_extraction.ActivationExtractor attribute)

layers_types_dict (revelionn.activation_extraction.ActivationExtractor attribute) linear_search() (revelionn.concept_extraction.ConceptExtractor method), [1] load_main_model() (in module revelionn.utils.model) load_mapping_model() (in module revelionn.utils.model) load_model() (revelionn.main_module.MainModelProcessing method), [1] (revelionn.mapping_module.MappingModelProcessing method), [1]

M

main_net (revelionn.activation_extraction.ActivationExtractor attribute) (revelionn.main_module.MainModelProcessing attribute) MainModelProcessing (class in revelionn.main_module) mapping_net (revelionn.mapping_module.MappingModelProcessing attribute) MappingModelProcessing (class in revelionn.mapping_module) MappingModule (class in revelionn.mapping_nets.simultaneous_mapping_net) MappingTrainer (class in revelionn.mapping_trainer) module revelionn.activation_extraction revelionn.concept_extraction revelionn.datasets revelionn.early_stopping revelionn.main_module revelionn.mapping_module revelionn.mapping_nets.simultaneous_mapping_net revelionn.mapping_nets.single_mapping_net revelionn.mapping_trainer revelionn.occlusion revelionn.utils.explanation revelionn.utils.model

MultiLabelClassifier (class in revelionn.occlusion) MultiLabeledImagesDataset (class in revelionn.datasets)

N

num_neurons_list (revelionn.mapping_nets.single_mapping_net.SingleMappingNet attribute) num_output_neurons (revelionn.mapping_nets.simultaneous_mapping_net.SimultaneousMappingNet attribute)

num_outs (revelionn.mapping_nets.simultaneous_mapping_net.SimultaneousMappingNet attribute) num_shared_neurons (revelionn.mapping_nets.simultaneous_mapping_net.SimultaneousMappingNet attribute)

O

ontology (revelionn.concept_extraction.ConceptExtractor attribute)

order_concepts() (revelionn.concept_extraction.ConceptExtractor method), [1] output_layers_list (revelionn.mapping_nets.simultaneous_mapping_net.MappingModule attribute)

P

patience (revelionn.early_stopping.EarlyStopping attribute)

perform_occlusion() (in module revelionn.occlusion)

R

register_hooks() (revelionn.activation_extraction.ActivationExtractor method), [1] revelionn.activation_extraction module revelionn.concept_extraction module revelionn.datasets module revelionn.early_stopping module revelionn.main_module module revelionn.mapping_module module

revelionn.mapping_nets.simultaneous_mapping_net module revelionn.mapping_nets.single_mapping_net module revelionn.mapping_trainer module revelionn.occlusion module revelionn.utils.explanation module revelionn.utils.model module

S

save_checkpoint() (revelionn.early_stopping.EarlyStopping method), [1] SemiSupervisedImagesDataset (class in revelionn.datasets) separate_unlabeled() (revelionn.datasets.SemiSupervisedImagesDataset method) (revelionn.datasets.SemiSupervisedImagesDataset static method)

set_layers_for_research() (revelionn.activation_extraction.ActivationExtractor method), [1] sigmoid (revelionn.mapping_nets.simultaneous_mapping_net.MappingModule attribute) simultaneous_extraction() (revelionn.concept_extraction.ConceptExtractor method) SimultaneousMappingNet (class in revelionn.mapping_nets.simultaneous_mapping_net) SingleMappingNet (class in revelionn.mapping_nets.single_mapping_net)

T

to_main_observation() (in module revelionn.utils.explanation) to_mapping_observation() (in module revelionn.utils.explanation) trace_func (revelionn.early_stopping.EarlyStopping attribute) train_model() (revelionn.main_module.MainModelProcessing method) train_model_semisupervised() (revelionn.mapping_module.MappingModelProcessing method) train_model_simultaneous() (revelionn.mapping_module.MappingModelProcessing method)

train_model_single() (revelionn.mapping_module.MappingModelProcessing method) train_simultaneous_model() (revelionn.mapping_trainer.MappingTrainer method), [1] train_simultaneous_model_semisupervised() (revelionn.mapping_trainer.MappingTrainer method) train_single_model() (revelionn.mapping_trainer.MappingTrainer method), [1] trainer (revelionn.concept_extraction.ConceptExtractor attribute) transform (revelionn.datasets.MultiLabeledImagesDataset attribute) (revelionn.datasets.SemiSupervisedImagesDataset attribute)

U

unlabeled_idx (revelionn.datasets.SemiSupervisedImagesDataset attribute)

V

val_loss_min (revelionn.early_stopping.EarlyStopping attribute)

verbose (revelionn.early_stopping.EarlyStopping attribute)