Class SaliencyInferer

monai/inferers/inferer.py:697–741

SaliencyInferer is inference with activation maps. Args: cam_name: expected CAM method name, should be: "CAM", "GradCAM" or "GradCAMpp". target_layers: name of the model layer to generate the feature map. class_idx: index of the class to be visualized. if None, defa

class SaliencyInferer(Inferer):
    """
    SaliencyInferer is inference with activation maps.

    Args:
        cam_name: expected CAM method name, should be: "CAM", "GradCAM" or "GradCAMpp".
        target_layers: name of the model layer to generate the feature map.
        class_idx: index of the class to be visualized. if None, default to argmax(logits).
        args: other optional args to be passed to the `__init__` of cam.
        kwargs: other optional keyword args to be passed to `__init__` of cam.

    """

    def __init__(
        self, cam_name: str, target_layers: str, class_idx: int | None = None, *args: Any, **kwargs: Any
    ) -> None:
        Inferer.__init__(self)
        if cam_name.lower() not in ("cam", "gradcam", "gradcampp"):
            raise ValueError("cam_name should be: 'CAM', 'GradCAM' or 'GradCAMpp'.")
        self.cam_name = cam_name.lower()
        self.target_layers = target_layers
        self.class_idx = class_idx
        self.args = args
        self.kwargs = kwargs

    def __call__(self, inputs: torch.Tensor, network: nn.Module, *args: Any, **kwargs: Any):  # type: ignore
        """Unified callable function API of Inferers.

        Args:
            inputs: model input data for inference.
            network: target model to execute inference.
                supports callables such as ``lambda x: my_torch_model(x, additional_config)``
            args: other optional args to be passed to the `__call__` of cam.
            kwargs: other optional keyword args to be passed to `__call__` of cam.

        """
        cam: CAM | GradCAM | GradCAMpp
        if self.cam_name == "cam":
            cam = CAM(network, self.target_layers, *self.args, **self.kwargs)
        elif self.cam_name == "gradcam":
            cam = GradCAM(network, self.target_layers, *self.args, **self.kwargs)
        else:
            cam = GradCAMpp(network, self.target_layers, *self.args, **self.kwargs)

        return cam(inputs, self.class_idx, *args, **kwargs)


class SliceInferer(SlidingWindowInferer):