revelionn 1.0.2

Retrospective Extraction of Visual and Logical Insights for Ontology-based interpretation of Neural Networks

Retrospective Extraction of Visual and Logical Insights for Ontology-based interpretation of Neural Networks

RevelioNN is an open-source library of post-hoc algorithms for explaining predictions of deep convolutional neural networks of binary classification using ontologies. The algorithms are based on the construction of mapping networks linking the internal representations of a convolutional neural network with ontology concepts. The inspiration for the development of this library was a paper in which this approach to the interpretation of neural networks was proposed:

• M. de Sousa Ribeiro and J. Leite, “Aligning Artificial Neural Networks and Ontologies towards Explainable AI,” in 35th AAAI Conference on Artificial Intelligence, AAAI 2021, May 2021, vol. 6A, no. 6, pp. 4932–4940. doi: 10.1609/aaai.v35i6.16626.

How the library works

The convolutional neural network, whose predictions need to be explained, is called “main” network. When an image is passed through it, the output is a probability of some target class, which at the same time is a concept of ontology. Activations of the “main” network produced as a result of image passing represent input data for the mapping networks. The outputs of the mapping networks are the probabilities of each of the concepts relevant to the target class, that is, the concepts that are involved in its definition. Knowing the probabilities of each of the concepts, it becomes possible to form logical and visual explanations.

Logical Explanations

By extracting relevant concepts, it is possible to form logical explanations about the belonging of the sample to the target concept, accompanied by a set of axioms of ontology. The input image presented in the scheme was taken from the SCDB dataset, with which the “main” network and mapping networks were trained. This image belongs to class C1. The image is classified as C1 if the concepts Hexagon ⊓ Star or Ellipse ⊓ Star or Triangle ⊓ Ellipse ⊓ Starmarker are present. An example of a logical explanation by ontological inference for this sample is given below.

 The image is classified as ['C1'].

 The following concepts were extracted from the image:
 ['HexStar', 'EllStar', 'NotTEStarmarker', 'Hexagon', 'Star', 'Ellipse', 'NotTriangle', 'NotStarmarker']
 with the following probabilities:
 [0.99938893, 0.99976605, 0.9937676684930921, 0.99947304, 0.9999995, 0.99962604, 0.9861229043453932, 0.9810010809451342]

 Justification for '__input__ Type C1':	(Degree of Belief: 0.99963)
 	__input__ Type has some Star	("0.9999995")
 	__input__ Type has some Ellipse	("0.99962604")
 	(has some Ellipse) and (has some Star) SubClassOf EllStar
 	C1 EquivalentTo EllStar or HexStar or TEStarmarker

Each of the extracted concepts corresponds to a certain probability, which is then used to calculate the degree of confidence of the justifications. The list of possible justifications is ranked by the degree of trust. If any concept has not been extracted, then we can say that the opposite concept has been extracted, the name of which is automatically formed by adding the prefix 'Not'.

The above example shows one of the explanations from the list of possible explanations. It can be interpreted as follows. The concepts of Star and Ellipse were extracted from the image. Therefore, based on the axiom of ontology that the conjunction of the concepts Star and Ellipse is a subclass of EllStar, we can conclude that the image also represents EllStar. And according to another axiom, the C1 target concept is equivalent to EllStar. Thus, the prediction of the neural network was confirmed by ontological reasoning.

Visual Explanations

Visual explanations mean highlighting positively extracted concepts in the image. Currently, visual explanations are formed using the occlusion method. Its essence lies in the fact that the input image is systematically overlapped by a square of a given size with a given step. At each step, the overlapped image is run through the “main” network, and its activations are run through the mapping network. Thus, by obtaining output probabilities at each step, a saliency map can be formed.

RevelioNN Features

Mapping Networks

The library implements two types of mapping networks whose parameters can be flexibly customized by the user.

Type of mapping network	Features
Single mapping network	It is a full-connected neural network, the number of input neurons of which is determined by the number of activations of neurons of the specified convolutional network layers. It has a ReLU activation function in its hidden layers and a sigmoid in its output. It is reasonable to use it to extract only one concept from one or more given convolutional network layers. The user can vary the number of layers and the number of neurons in each layer of this mapping network.
Simultaneous mapping network	Due to the features of its architecture, it allows you to extract many concepts simultaneously, receiving activations of all specified layers of the convolutional network at once. It takes into account the features of the 2D image structure and is less prone to overfitting compared to single mapping networks. It also shows good results in semi-supervised learning using semantic loss, which strengthens the relationship between concepts.

Extraction Algorithms

Extraction algorithm	Type of mapping network	What it does
Exhaustive search	Single mapping network	Trains and evaluates mapping networks based on the activations of each of the specified layers of the convolutional network
Heuristic search	Single mapping network	Due to the heuristic reduction of the set of specified layers, mapping networks are not trained for every combination of layer-concept
Simultaneous extraction	Simultaneous mapping network	Trains a mapping network that can simultaneously extract a set of relevant concepts from the entire set of layers of specified types

How to Use

RevelioNN can interpret convolutional binary classification networks that have already been trained without using this library. It is worth noting that the network class must be inherited from the nn.Module class, that is, your network must be implemented using PyTorch. The specified model must be converted to RevelioNN format.

To use the API, follow these steps:

1. In order to convert your model to RevelioNN format, your network class must be described in a separate file in which the following variables must also be declared:

   • variable storing the number of channels of the image fed to the network;
   • variable storing the size of the image fed to the network;
   • the torchvision.transforms module object, which represents a transformation over images.

   Examples of network descriptions are given in the main_net_classes directory.

2. Next, you need to initialize your convolutional neural network model.

   from main_net_classes.resnet18_scdb import ResNet18, NUM_CHANNELS, IMG_SIDE_SIZE, transformation
   
   main_net = ResNet18()
   main_net.load_state_dict(torch.load('SCDB_ResNet18_C1.pt'))
   

3. Import convert_to_rvl_format() function:

   from revelionn.utils.model import convert_to_rvl_format
   

   Call this function by passing the data of the previously declared network model as parameters:

   convert_to_rvl_format(main_net, 'SCDB_ResNet18_C1', 'C1', 'resnet18_scdb', 'ResNet18', 'transformation', IMG_SIDE_SIZE, NUM_CHANNELS)
   

4. After the main network has been successfully converted to the RevelioNN format, mapping networks can be trained. Here is an example for training a simultaneous extraction network. Here activations were extracted from all batch normalization layers (the value is 'bn').

   from revelionn.mapping_trainer import MappingTrainer
   
   device = torch.device('cuda')
   trainer = MappingTrainer('SCDB_ResNet18_C1.rvl', os.path.join(root_path, 'main_net_classes'), ['bn'], 20, 100, 
                            os.path.join(root_path, 'trained_models', 'mapping_models'),
                            device, os.path.join(root_path, 'data', 'scdb_custom', 'images'),
                            'C1_mapping_train.csv', 'C1_mapping_val.csv', 'name', 100, 6, None)
   
   trainer.train_simultaneous_model(['HexStar', 'EllStar', 'TEStarmarker', 'Hexagon', 
                                    'Star', 'Ellipse', 'Triangle', 'Starmarker'], 
                                    20, [160, 80, 40, 20], [20, 1])
   

5. Once the mapping network is trained, you can form logical and visual explanations. To do this, you must first load the trained network model via load_mapping_model().

   from revelionn.utils.model import load_mapping_model
   
   main_module, mapping_module, activation_extractor, transformation, img_size = load_mapping_model(
       os.path.join(root_path, 'trained_models', 'mapping_models', 'C1_20_[160, 80, 40, 20]_[20, 1].rvl'), 
       cur_path, os.path.join(root_path, 'main_net_classes'), device)
   

6. To form logical explanations using an ontology, one must first extract the concepts relevant to the target concept from the image, and then transfer the extracted concepts and their probabilities to the reasoning module along with the ontology. This can be done as follows:

   from revelionn.utils.explanation import extract_concepts_from_img, explain_target_concept
   from ontologies.scdb_ontology import concepts_map
   from PIL import Image
   
   image_path = os.path.join(root_path, 'data', 'scdb_custom', 'images', '001236.png')
   
   image = Image.open(image_path)
   main_concepts, extracted_concepts, mapping_probabilities = extract_concepts_from_img(main_module,
                                                                                        mapping_module,
                                                                                        image,
                                                                                        transformation)
   print(f'\nThe image is classified as {main_concepts}.')
   print('\nThe following concepts were extracted from the image:')
   print(extracted_concepts)
   print('with the following probabilities:')
   print(f'{mapping_probabilities}\n')
      
   justifications = explain_target_concept(extracted_concepts, mapping_probabilities, concepts_map, 'C1',
                                           os.path.join(root_path, 'ontologies', 'SCDB.owl'), 
                                           os.path.join(root_path, 'temp'))
   print(justifications)
   

7. Visual explanations can be formed as follows:

   import matplotlib.pyplot as plt
   from revelionn.occlusion import perform_occlusion
   
   perform_occlusion(main_module, mapping_module, activation_extractor, transformation, img_size,
                    image_path, window_size=20, stride=5, threads=0)
   plt.show()
   

The execution of the listed steps is shown in basic_example.ipynb.

RevelioNN also supports a command line-based interface, i.e. interaction through scripts. A detailed description of how to use each of the scripts can be found in the documentation.

Installation

The simplest way to install RevelioNN is using pip:

pip install revelionn
pip install git+https://github.com/lucadiliello/semantic-loss-pytorch.git

You can view a list of required dependencies in the requirements.txt file. You can also install them as follows:

pip install -r requirements.txt

It is also worth noting that Java SE 8 must be installed to form logical explanations.

Project Structure

The repository includes the following directories:

• Package main_net_classes contains various convolutional neural network architectures that can serve as examples for initializing your network in RevelioNN;
• Package ontologies contains examples of ontology files in OWL format, as well as examples of the dictionary of relations of dataset attributes to ontology concepts and examples of the class representing the ontology as a graph;
• Package examples includes notebooks that contain practical examples of RevelioNN use;
• All unit and integration tests can be observed in the tests directory;
• The sources of the documentation are in the docs directory.

Documentation

A detailed RevelioNN description is available in Read the Docs.

Tests

To run tests, you can use:

pytest tests

Publications

The library was used in the following publications:

• Agafonov A., Ponomarev A. An Experiment on Localization of Ontology Concepts in Deep Convolutional Neural Networks // In the 11th International Symposium on Information and Communication Technology (SoICT 2022), 82–87. DOI: 10.1145/3568562.3568602
• Ponomarev A., Agafonov A. Ontology Concept Extraction Algorithm for Deep Neural Networks // Proceedings of the 32nd Conference of Open Innovations Association FRUCT, 221-226. DOI: 10.23919/FRUCT56874.2022.9953838
• Agafonov A., Ponomarev A. Localization of Ontology Concepts in Deep Convolutional Neural Networks // 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON), 160-165. DOI: 10.1109/SIBIRCON56155.2022.10016932

Funding

The RevelioNN library was developed in the scope of the project 22-11-00214, funded by the Russian Science Foundation (RSF).

Acknowledgements

We thank the developers of xaitk-saliency, semantic-loss-pytorch, nxontology and BUNDLE, thanks to whom the development of RevelioNN became possible!

Special thanks to the creators of the XTRAINS dataset for providing the ontology and for inspiring the development of this library!