class_docstr: 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.
docstr: 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)
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code: 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,
      )
  
file_path: captum/captum/attr/_core/neuron/neuron_feature_ablation.py
imports: 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
label_desc: Use image node_id 2 for calling the NeuronFeatureAblation obj's underlying member method code with example usage: obj.attribute(inputs, neuron_selector, baselines, additional_forward_args, feature_mask, attribute_to_neuron_input, perturbations_per_eval) and returns: ablator, _verify_select_neuron