LAP/torchlap/interpreting/attention_sum_interpreter.py

import torch
import torch.nn.functional as F
from .attention_interpreter import AttentionInterpreter, AttentionInterpretableModel

class AttentionSumInterpreter(AttentionInterpreter):

    def __init__(self, model: AttentionInterpretableModel):
        assert isinstance(model, AttentionInterpretableModel),\
            f"Expected AttentionInterpretableModel, got {type(model)}"
        super().__init__(model)

    def _process_attention(self, result: torch.Tensor, attention: torch.Tensor) -> torch.Tensor:
        """ Process current attention level.

        Args:
            result (torch.Tensor): Attention map till the previous attention level. (B, 1, H1, W1)
            attention (torch.Tensor): Current level's attention map. (B, 1, H2, W2)

        Returns:
            torch.Tensor: New attention map. (B, 1, H2, W2)
        """
        self._impact *= self._impact_decay

        # smallest attention layer
        if result is None:
            return attention

        # attention is larger than result

        result = F.interpolate(result, attention.shape[2:], mode='bilinear', align_corners=True)
        return result + attention