Source code for captum.attr._core.neuron.neuron_feature_ablation

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

import torch
from captum._utils.common import _verify_select_neuron
from captum._utils.gradient import _forward_layer_eval
from captum._utils.typing import BaselineType, TensorOrTupleOfTensorsGeneric
from captum.attr._core.feature_ablation import FeatureAblation
from captum.attr._utils.attribution import NeuronAttribution, PerturbationAttribution
from captum.log import log_usage
from torch.nn import Module

[docs] class NeuronFeatureAblation(NeuronAttribution, PerturbationAttribution): r""" A perturbation based approach to computing neuron attribution, involving replacing each input feature with a given baseline / reference, and computing the difference in the neuron's input / output. By default, each scalar value within each input tensor is taken as a feature and replaced independently. Passing a feature mask, allows grouping features to be ablated together. This can be used in cases such as images, where an entire segment or region can be ablated, measuring the importance of the segment (feature group). Each input scalar in the group will be given the same attribution value equal to the change in target as a result of ablating the entire feature group. """ def __init__( self, forward_func: Callable, layer: Module, device_ids: Union[None, List[int]] = None, ) -> None: r""" Args: forward_func (Callable): The forward function of the model or any modification of it layer (torch.nn.Module): Layer for which attributions are computed. Attributions for a particular neuron in the input or output of this layer are computed using the argument neuron_selector in the attribute method. Currently, it is assumed that the inputs or the outputs of the layer, depending on which one is used for attribution, can only be a single tensor. device_ids (list[int]): Device ID list, necessary only if forward_func applies a DataParallel model. This allows reconstruction of intermediate outputs from batched results across devices. If forward_func is given as the DataParallel model itself, then it is not necessary to provide this argument. """ NeuronAttribution.__init__(self, forward_func, layer, device_ids) PerturbationAttribution.__init__(self, forward_func)
[docs] @log_usage() def attribute( self, inputs: TensorOrTupleOfTensorsGeneric, neuron_selector: Union[int, Tuple[Union[int, slice], ...], Callable], baselines: BaselineType = None, additional_forward_args: Any = None, feature_mask: Union[None, TensorOrTupleOfTensorsGeneric] = None, attribute_to_neuron_input: bool = False, perturbations_per_eval: int = 1, ) -> TensorOrTupleOfTensorsGeneric: r""" Args: inputs (Tensor or tuple[Tensor, ...]): Input for which neuron attributions are computed. 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. neuron_selector (int, Callable, tuple[int], or slice): Selector for neuron in given layer for which attribution is desired. Neuron selector can be provided as: - a single integer, if the layer output is 2D. This integer selects the appropriate neuron column in the layer input or output - a tuple of integers or slice objects. Length of this tuple must be one less than the number of dimensions in the input / output of the given layer (since dimension 0 corresponds to number of examples). The elements of the tuple can be either integers or slice objects (slice object allows indexing a range of neurons rather individual ones). If any of the tuple elements is a slice object, the indexed output tensor is used for attribution. Note that specifying a slice of a tensor would amount to computing the attribution of the sum of the specified neurons, and not the individual neurons independently. - a callable, which should take the target layer as input (single tensor or tuple if multiple tensors are in layer) and return a neuron or aggregate of the layer's neurons for attribution. For example, this function could return the sum of the neurons in the layer or sum of neurons with activations in a particular range. It is expected that this function returns either a tensor with one element or a 1D tensor with length equal to batch_size (one scalar per input example) baselines (scalar, Tensor, tuple of scalar, or Tensor, optional): Baselines define reference value which replaces each feature when ablated. Baselines can be provided as: - a single tensor, if inputs is a single tensor, with exactly the same dimensions as inputs or broadcastable to match the dimensions of inputs - a single scalar, if inputs is a single tensor, which will be broadcasted for each input value in input tensor. - a tuple of tensors or scalars, the baseline corresponding to each tensor in the inputs' tuple can be: - either a tensor with matching dimensions to corresponding tensor in the inputs' tuple or the first dimension is one and the remaining dimensions match with the corresponding input tensor. - or a scalar, corresponding to a tensor in the inputs' tuple. This scalar value is broadcasted for corresponding input tensor. In the cases when `baselines` is not provided, we internally use zero scalar corresponding to each input tensor. Default: None additional_forward_args (Any, 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 feature_mask (Tensor or tuple[Tensor, ...], optional): feature_mask defines a mask for the input, grouping features which should be ablated together. feature_mask should contain the same number of tensors as inputs. Each tensor should be the same size as the corresponding input or broadcastable to match the input tensor. Each tensor should contain integers in the range 0 to num_features - 1, and indices corresponding to the same feature should have the same value. Note that features within each input tensor are ablated independently (not across tensors). If None, then a feature mask is constructed which assigns each scalar within a tensor as a separate feature, which is ablated independently. Default: None attribute_to_neuron_input (bool, optional): Indicates whether to compute the attributions with respect to the neuron input or output. If `attribute_to_neuron_input` is set to True then the attributions will be computed with respect to neuron's inputs, otherwise it will be computed with respect to neuron's outputs. Note that currently it is assumed that either the input or the output of internal neurons, depending on whether we attribute to the input or output, is a single tensor. Support for multiple tensors will be added later. Default: False perturbations_per_eval (int, optional): Allows ablation of multiple features to be processed simultaneously in one call to forward_fn. Each forward pass will contain a maximum of perturbations_per_eval * #examples samples. For DataParallel models, each batch is split among the available devices, so evaluations on each available device contain at most (perturbations_per_eval * #examples) / num_devices samples. Default: 1 Returns: *Tensor* or *tuple[Tensor, ...]* of **attributions**: - **attributions** (*Tensor* or *tuple[Tensor, ...]*): Attributions of particular neuron 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. Examples:: >>> # SimpleClassifier takes a single input tensor of size Nx4x4, >>> # and returns an Nx3 tensor of class probabilities. >>> # It contains an attribute conv1, which is an instance of nn.conv2d, >>> # and the output of this layer has dimensions Nx12x3x3. >>> net = SimpleClassifier() >>> # Generating random input with size 2 x 4 x 4 >>> input = torch.randn(2, 4, 4) >>> # Defining NeuronFeatureAblation interpreter >>> ablator = NeuronFeatureAblation(net, net.conv1) >>> # To compute neuron attribution, we need to provide the neuron >>> # index for which attribution is desired. Since the layer output >>> # is Nx12x3x3, we need a tuple in the form (0..11,0..2,0..2) >>> # which indexes a particular neuron in the layer output. >>> # For this example, we choose the index (4,1,2). >>> # Computes neuron gradient for neuron with >>> # index (4,1,2). >>> # Computes ablation attribution, ablating each of the 16 >>> # scalar inputs independently. >>> attr = ablator.attribute(input, neuron_selector=(4,1,2)) >>> # Alternatively, we may want to ablate features in groups, e.g. >>> # grouping each 2x2 square of the inputs and ablating them together. >>> # This can be done by creating a feature mask as follows, which >>> # defines the feature groups, e.g.: >>> # +---+---+---+---+ >>> # | 0 | 0 | 1 | 1 | >>> # +---+---+---+---+ >>> # | 0 | 0 | 1 | 1 | >>> # +---+---+---+---+ >>> # | 2 | 2 | 3 | 3 | >>> # +---+---+---+---+ >>> # | 2 | 2 | 3 | 3 | >>> # +---+---+---+---+ >>> # With this mask, all inputs with the same value are ablated >>> # simultaneously, and the attribution for each input in the same >>> # group (0, 1, 2, and 3) per example are the same. >>> # The attributions can be calculated as follows: >>> # feature mask has dimensions 1 x 4 x 4 >>> feature_mask = torch.tensor([[[0,0,1,1],[0,0,1,1], >>> [2,2,3,3],[2,2,3,3]]]) >>> attr = ablator.attribute(input, neuron_selector=(4,1,2), >>> feature_mask=feature_mask) """ def neuron_forward_func(*args: Any): with torch.no_grad(): layer_eval = _forward_layer_eval( self.forward_func, args, self.layer, device_ids=self.device_ids, attribute_to_layer_input=attribute_to_neuron_input, ) return _verify_select_neuron(layer_eval, neuron_selector) ablator = FeatureAblation(neuron_forward_func) # NOTE: using __wrapped__ to not log return ablator.attribute.__wrapped__( ablator, # self inputs, baselines=baselines, additional_forward_args=additional_forward_args, feature_mask=feature_mask, perturbations_per_eval=perturbations_per_eval, )