LAP/torchlap/interpreting/guided_gradcam.py

"""
Guided Grad Cam Interpretation
"""
from typing import Dict, Union

import torch
from captum import attr

from . import Interpreter, CamInterpretableModel, InterpretableWrapper


class GuidedGradCam(Interpreter): 
    """ Produces class activation map """

    def __init__(self, model: CamInterpretableModel):
        super().__init__(model)
        self._model_wrapper = InterpretableWrapper(model)
        self._interpreters = [
            attr.GuidedGradCam(self._model_wrapper, conv)
            for conv in model.target_conv_layers
        ]

    def interpret(self, labels: Union[int, torch.Tensor], **inputs: torch.Tensor) -> Dict[str, torch.Tensor]:
        """ Interprets given input and target class

        Args:
            y (Union[int, torch.Tensor]): target class
            **inputs (torch.Tensor): model inputs

        Returns:
            Dict[str, torch.Tensor]: Interpretation results
        """

        labels = self._get_target(labels, **inputs)
        separated_inputs = self._model_wrapper.convert_inputs_to_args(**inputs)
        gradcams = [interpreter.attribute(
            separated_inputs.inputs,
            target=labels,
            additional_forward_args=separated_inputs.additional_inputs)[0]
            for interpreter in self._interpreters]
        gradcams = torch.stack(gradcams).sum(dim=0)

        return {
            'default': gradcams,
        }  # TODO: must return and support tuple