LAP/torchlap/interpreting/gradcam.py

from typing import Dict, Union

import torch
import torch.nn.functional as F
from captum import attr

from . import Interpreter, CamInterpretableModel, InterpretableWrapper


class GradCam(Interpreter):

    def __init__(self, model: CamInterpretableModel):
        super().__init__(model)
        self.__model_wrapper = InterpretableWrapper(model)
        self.__interpreters = [
            attr.LayerGradCam(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]:
        inp_shape = inputs[self._model.ordered_placeholder_names_to_be_interpreted[0]].shape[2:]
        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)
            for interpreter in self.__interpreters]
        gradcams = [
            cam if torch.is_tensor(cam) else cam[0]
            for cam in gradcams
        ]
        gradcams = torch.stack(gradcams).sum(dim=0)
        gradcams = F.interpolate(gradcams, size=inp_shape, mode='bilinear', align_corners=True)

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