LRP#

class txv.exp.LRP(model: Module)#

Layer-wise Relevance Propagation(LRP). Link to the paper: On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

__init__(model: Module) → None#
Parameters:

model (torch.nn.Module) – A model from txv.vit

Caution

The model must be an LRP model. You can use the LRP version of a model by passing lrp=True in the model function.

explain(input: Tensor, index: int | None = None, alpha: float = 0.5, abm: bool = True) → Tensor#

Explain the model prediction using Layer-wise Relevance Propagation(LRP)

Parameters:

  • input (torch.Tensor) – Input tensor

  • index (int, optional) – Index of the class to explain, by default the predicted class is explained

  • alpha (float, optional) – Alpha value for LRP, by default 0.5

  • abm (bool, optional) – Architecture based modification, by default True