Source code for captum.attr._core.layer.layer_lrp

#!/usr/bin/env python3
import typing
from typing import Any, List, Tuple, Union, cast

from captum._utils.common import _format_input, _reduce_list, _sort_key_list
from captum._utils.gradient import (
    apply_gradient_requirements,
    compute_gradients,
    undo_gradient_requirements,
)
from captum._utils.typing import (
    Literal,
    ModuleOrModuleList,
    TargetType,
    TensorOrTupleOfTensorsGeneric,
)
from captum.attr._core.lrp import LRP
from captum.attr._utils.attribution import LayerAttribution
from torch import Tensor
from torch.nn import Module


[docs]class LayerLRP(LRP, LayerAttribution): r""" Layer-wise relevance propagation is based on a backward propagation mechanism applied sequentially to all layers of the model. Here, the model output score represents the initial relevance which is decomposed into values for each neuron of the underlying layers. The decomposition is defined by rules that are chosen for each layer, involving its weights and activations. Details on the model can be found in the original paper [https://doi.org/10.1371/journal.pone.0130140]. The implementation is inspired by the tutorial of the same group [https://doi.org/10.1016/j.dsp.2017.10.011] and the publication by Ancona et al. [https://openreview.net/forum?id=Sy21R9JAW]. """ def __init__(self, model: Module, layer: ModuleOrModuleList) -> None: """ Args: model (module): The forward function of the model or any modification of it. Custom rules for a given layer need to be defined as attribute `module.rule` and need to be of type PropagationRule. layer (torch.nn.Module or list(torch.nn.Module)): Layer or layers for which attributions are computed. The size and dimensionality of the attributions corresponds to the size and dimensionality of the layer's input or output depending on whether we attribute to the inputs or outputs of the layer. If value is None, the relevance for all layers is returned in attribution. """ LayerAttribution.__init__(self, model, layer) LRP.__init__(self, model) if hasattr(self.model, "device_ids"): self.device_ids = cast(List[int], self.model.device_ids) @typing.overload # type: ignore def attribute( self, inputs: TensorOrTupleOfTensorsGeneric, target: TargetType = None, additional_forward_args: Any = None, return_convergence_delta: Literal[False] = False, attribute_to_layer_input: bool = False, verbose: bool = False, ) -> Union[Tensor, Tuple[Tensor, ...], List[Union[Tensor, Tuple[Tensor, ...]]]]: ... @typing.overload def attribute( self, inputs: TensorOrTupleOfTensorsGeneric, target: TargetType = None, additional_forward_args: Any = None, *, return_convergence_delta: Literal[True], attribute_to_layer_input: bool = False, verbose: bool = False, ) -> Tuple[ Union[Tensor, Tuple[Tensor, ...], List[Union[Tensor, Tuple[Tensor, ...]]]], Union[Tensor, List[Tensor]], ]: ...
[docs] def attribute( self, inputs: TensorOrTupleOfTensorsGeneric, target: TargetType = None, additional_forward_args: Any = None, return_convergence_delta: bool = False, attribute_to_layer_input: bool = False, verbose: bool = False, ) -> Union[ Tensor, Tuple[Tensor, ...], List[Union[Tensor, Tuple[Tensor, ...]]], Tuple[ Union[Tensor, Tuple[Tensor, ...], List[Union[Tensor, Tuple[Tensor, ...]]]], Union[Tensor, List[Tensor]], ], ]: r""" Args: inputs (tensor or tuple of tensors): Input for which relevance is propagated. If forward_func takes a single tensor as input, a single input tensor should be provided. If forward_func takes multiple tensors as input, a tuple of the input tensors should be provided. It is assumed that for all given input tensors, dimension 0 corresponds to the number of examples, and if multiple input tensors are provided, the examples must be aligned appropriately. target (int, tuple, tensor or list, optional): Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, no target index is necessary. For general 2D outputs, targets can be either: - a single integer or a tensor containing a single integer, which is applied to all input examples - a list of integers or a 1D tensor, with length matching the number of examples in inputs (dim 0). Each integer is applied as the target for the corresponding example. For outputs with > 2 dimensions, targets can be either: - A single tuple, which contains #output_dims - 1 elements. This target index is applied to all examples. - A list of tuples with length equal to the number of examples in inputs (dim 0), and each tuple containing #output_dims - 1 elements. Each tuple is applied as the target for the corresponding example. Default: None additional_forward_args (tuple, optional): If the forward function requires additional arguments other than the inputs for which attributions should not be computed, this argument can be provided. It must be either a single additional argument of a Tensor or arbitrary (non-tuple) type or a tuple containing multiple additional arguments including tensors or any arbitrary python types. These arguments are provided to forward_func in order, following the arguments in inputs. Note that attributions are not computed with respect to these arguments. Default: None return_convergence_delta (bool, optional): Indicates whether to return convergence delta or not. If `return_convergence_delta` is set to True convergence delta will be returned in a tuple following attributions. Default: False attribute_to_layer_input (bool, optional): Indicates whether to compute the attribution with respect to the layer input or output. If `attribute_to_layer_input` is set to True then the attributions will be computed with respect to layer input, otherwise it will be computed with respect to layer output. verbose (bool, optional): Indicates whether information on application of rules is printed during propagation. Default: False Returns: *tensor* or tuple of *tensors* of **attributions** or 2-element tuple of **attributions**, **delta** or lists of **attributions** and **delta**: - **attributions** (*tensor* or tuple of *tensors*): The propagated relevance values with respect to each input feature. Attributions will always be the same size as the provided inputs, with each value providing the attribution of the corresponding input index. If a single tensor is provided as inputs, a single tensor is returned. If a tuple is provided for inputs, a tuple of corresponding sized tensors is returned. The sum of attributions is one and not corresponding to the prediction score as in other implementations. If attributions for all layers are returned (layer=None) a list of tensors or tuples of tensors is returned with entries for each layer. - **delta** (*tensor* or list of *tensors* returned if return_convergence_delta=True): Delta is calculated per example, meaning that the number of elements in returned delta tensor is equal to the number of of examples in input. If attributions for all layers are returned (layer=None) a list of tensors is returned with entries for each layer. Examples:: >>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. It has one >>> # Conv2D and a ReLU layer. >>> net = ImageClassifier() >>> lrp = LRP(net, net.conv1) >>> input = torch.randn(3, 3, 32, 32) >>> # Attribution size matches input size: 3x3x32x32 >>> attribution = lrp.attribute(input, target=5) """ self.verbose = verbose self._original_state_dict = self.model.state_dict() self.layers = [] self._get_layers(self.model) self._check_and_attach_rules() self.attribute_to_layer_input = attribute_to_layer_input self.backward_handles = [] self.forward_handles = [] inputs = _format_input(inputs) gradient_mask = apply_gradient_requirements(inputs) try: # 1. Forward pass output = self._compute_output_and_change_weights( inputs, target, additional_forward_args ) self._register_forward_hooks() # 2. Forward pass + backward pass _ = compute_gradients( self._forward_fn_wrapper, inputs, target, additional_forward_args ) relevances = self._get_output_relevance(output) finally: self._restore_model() undo_gradient_requirements(inputs, gradient_mask) if return_convergence_delta: delta: Union[Tensor, List[Tensor]] if isinstance(self.layer, list): delta = [] for relevance_layer in relevances: delta.append( self.compute_convergence_delta(relevance_layer, output) ) else: delta = self.compute_convergence_delta( cast(Tuple[Tensor, ...], relevances), output ) return relevances, delta # type: ignore else: return relevances # type: ignore
def _get_single_output_relevance(self, layer, output): if self.attribute_to_layer_input: normalized_relevances = layer.rule.relevance_input else: normalized_relevances = layer.rule.relevance_output key_list = _sort_key_list(list(normalized_relevances.keys()), self.device_ids) normalized_relevances = _reduce_list( [normalized_relevances[device_id] for device_id in key_list] ) if isinstance(normalized_relevances, tuple): return tuple( normalized_relevance * output.reshape((-1,) + (1,) * (normalized_relevance.dim() - 1)) for normalized_relevance in normalized_relevances ) else: return normalized_relevances * output.reshape( (-1,) + (1,) * (normalized_relevances.dim() - 1) ) def _get_output_relevance(self, output): if isinstance(self.layer, list): relevances = [] for layer in self.layer: relevances.append(self._get_single_output_relevance(layer, output)) return relevances else: return self._get_single_output_relevance(self.layer, output) @staticmethod def _convert_list_to_tuple( relevances: Union[List[Any], Tuple[Any, ...]] ) -> Tuple[Any, ...]: if isinstance(relevances, list): return tuple(relevances) else: return relevances