LAP/torchlap/interpreting/interpretable.py

"""
An Interpretable Pytorch Model
"""
from abc import abstractmethod, ABC
from typing import Iterable, List

import torch
from torch import nn

from ..models.model import Model


class InterpretableModel(Model, ABC):
    """
    An Interpretable Pytorch Model
    """

    @property
    @abstractmethod
    def ordered_placeholder_names_to_be_interpreted(self) -> Iterable[str]:
        """
        Returns:
            Input module for interpretation
        """

    @abstractmethod
    def get_categorical_probabilities(self, *inputs, **kwargs) -> torch.Tensor:
        """
        A method to get probabilities assigned to all the classes in the model's forward,
        with shape (B, C), in which B is the batch size and C is number of classes

        Args:
            *inputs: Inputs to the model
            **kwargs: Additional arguments

        Returns:
            Tensor of categorical probabilities
        """


class CamInterpretableModel(InterpretableModel, ABC):
    """
    A model interpretable by gradcam
    """

    @property
    @abstractmethod
    def target_conv_layers(self) -> List[nn.Module]:
        """ 
        Returns:
            The convolutional layers to be interpreted. The result of
            the interpretation will be sum of the grad-cam of these layers
        """