signxai.utils package
Submodules
signxai.utils.utils module
Framework-agnostic utility functions for SignXAI2.
This module contains utility functions that work independently of any deep learning framework. Framework-specific utilities have been moved to: - signxai/tf_signxai/tf_utils.py for TensorFlow utilities - signxai/torch_signxai/torch_utils.py for PyTorch utilities
- signxai.utils.utils.get_examples_data_dir()[source]
Get the path to the examples/data directory regardless of current working directory
- Returns:
Path to the examples/data directory
- Return type:
- signxai.utils.utils.aggregate_and_normalize_relevancemap_rgb(relevancemap)[source]
Aggregate and normalize a RGB relevance map
- Parameters:
relevancemap – RGB relevance map
- Returns:
Normalized relevance map
- signxai.utils.utils.normalize_heatmap(heatmap)[source]
Normalize a heatmap to the range [-1, 1]
- Parameters:
heatmap – Heatmap to normalize
- Returns:
Normalized heatmap
- signxai.utils.utils.remove_softmax(model)[source]
Remove the softmax activation from the last layer of a model.
This function delegates to the appropriate framework-specific implementation.
- Parameters:
model – TensorFlow or PyTorch model
- Returns:
Model with softmax removed
- signxai.utils.utils.calculate_explanation_innvestigate(model, x, method='lrp.epsilon', neuron_selection=None, batchmode=False, **kwargs)[source]
Calculate an explanation using the innvestigate backend (TensorFlow only).
This function has been moved to tf_utils.py as it’s TensorFlow-specific. This wrapper is kept for backward compatibility.
- signxai.utils.utils.load_image(img_path, target_size=(224, 224), expand_dims=False, use_original_preprocessing=True)[source]
Load an image from a file path and preprocess it.
This function has been moved to tf_utils.py as it uses TensorFlow preprocessing. This wrapper is kept for backward compatibility.