class_docstr: A perturbation based approach to computing attribution, involving
  replacing each input feature with a given baseline / reference, and
  computing the difference in 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.
  
  The forward function can either return a scalar per example or a tensor
  of a fixed sized tensor (or scalar value) for the full batch, i.e. the
  output does not grow as the batch size increase. If the output is fixed
  we consider this model to be an "aggregation" of the inputs. In the fixed
  sized output mode we require `perturbations_per_eval == 1` and the
  `feature_mask` to be either `None` or for all of them to have 1 as their
  first dimension (i.e. a feature mask requires to be applied to all inputs).
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docstr: Args:
  
      inputs (Tensor or tuple[Tensor, ...]): Input for which ablation
                  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 (aka batch size), and if
                  multiple input tensors are provided, the examples must
                  be aligned appropriately.
      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
      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 (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.
                  For a tensor, the first dimension of the tensor must
                  correspond to the number of examples. For all other types,
                  the given argument is used for all forward evaluations.
                  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 the forward function returns a single scalar per batch,
                  we enforce that the first dimension of each mask must be 1,
                  since attributions are returned batch-wise rather than per
                  example, so the attributions must correspond to the
                  same features (indices) in each input example.
                  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
      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.
                  If the forward function's number of outputs does not
                  change as the batch size grows (e.g. if it outputs a
                  scalar value), you must set perturbations_per_eval to 1
                  and use a single feature mask to describe the features
                  for all examples in the batch.
                  Default: 1
      show_progress (bool, optional): Displays the progress of computation.
                  It will try to use tqdm if available for advanced features
                  (e.g. time estimation). Otherwise, it will fallback to
                  a simple output of progress.
                  Default: False
      **kwargs (Any, optional): Any additional arguments used by child
                  classes of FeatureAblation (such as Occlusion) to construct
                  ablations. These arguments are ignored when using
                  FeatureAblation directly.
                  Default: None
  
  Returns:
      *Tensor* or *tuple[Tensor, ...]* of **attributions**:
      - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                  The attributions with respect to each input feature.
                  If the forward function returns
                  a scalar value per example, attributions will be
                  the same size as the provided inputs, with each value
                  providing the attribution of the corresponding input index.
                  If the forward function returns a scalar per batch, then
                  attribution tensor(s) will have first dimension 1 and
                  the remaining dimensions will match the input.
                  If a single tensor is provided as inputs, a single tensor is
                  returned. If a tuple of tensors 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.
      >>> net = SimpleClassifier()
      >>> # Generating random input with size 2 x 4 x 4
      >>> input = torch.randn(2, 4, 4)
      >>> # Defining FeatureAblation interpreter
      >>> ablator = FeatureAblation(net)
      >>> # Computes ablation attribution, ablating each of the 16
      >>> # scalar input independently.
      >>> attr = ablator.attribute(input, target=1)
  
      >>> # 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, target=1, feature_mask=feature_mask)
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code: def attribute(
      self,
      inputs: TensorOrTupleOfTensorsGeneric,
      baselines: BaselineType = None,
      target: TargetType = None,
      additional_forward_args: Any = None,
      feature_mask: Union[None, Tensor, Tuple[Tensor, ...]] = None,
      perturbations_per_eval: int = 1,
      show_progress: bool = False,
      **kwargs: Any,
  ) -> TensorOrTupleOfTensorsGeneric:
      r"""
      Args:
  
          inputs (Tensor or tuple[Tensor, ...]): Input for which ablation
                      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 (aka batch size), and if
                      multiple input tensors are provided, the examples must
                      be aligned appropriately.
          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
          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 (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.
                      For a tensor, the first dimension of the tensor must
                      correspond to the number of examples. For all other types,
                      the given argument is used for all forward evaluations.
                      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 the forward function returns a single scalar per batch,
                      we enforce that the first dimension of each mask must be 1,
                      since attributions are returned batch-wise rather than per
                      example, so the attributions must correspond to the
                      same features (indices) in each input example.
                      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
          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.
                      If the forward function's number of outputs does not
                      change as the batch size grows (e.g. if it outputs a
                      scalar value), you must set perturbations_per_eval to 1
                      and use a single feature mask to describe the features
                      for all examples in the batch.
                      Default: 1
          show_progress (bool, optional): Displays the progress of computation.
                      It will try to use tqdm if available for advanced features
                      (e.g. time estimation). Otherwise, it will fallback to
                      a simple output of progress.
                      Default: False
          **kwargs (Any, optional): Any additional arguments used by child
                      classes of FeatureAblation (such as Occlusion) to construct
                      ablations. These arguments are ignored when using
                      FeatureAblation directly.
                      Default: None
  
      Returns:
          *Tensor* or *tuple[Tensor, ...]* of **attributions**:
          - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                      The attributions with respect to each input feature.
                      If the forward function returns
                      a scalar value per example, attributions will be
                      the same size as the provided inputs, with each value
                      providing the attribution of the corresponding input index.
                      If the forward function returns a scalar per batch, then
                      attribution tensor(s) will have first dimension 1 and
                      the remaining dimensions will match the input.
                      If a single tensor is provided as inputs, a single tensor is
                      returned. If a tuple of tensors 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.
          >>> net = SimpleClassifier()
          >>> # Generating random input with size 2 x 4 x 4
          >>> input = torch.randn(2, 4, 4)
          >>> # Defining FeatureAblation interpreter
          >>> ablator = FeatureAblation(net)
          >>> # Computes ablation attribution, ablating each of the 16
          >>> # scalar input independently.
          >>> attr = ablator.attribute(input, target=1)
  
          >>> # 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, target=1, feature_mask=feature_mask)
      """
      # Keeps track whether original input is a tuple or not before
      # converting it into a tuple.
      is_inputs_tuple = _is_tuple(inputs)
      inputs, baselines = _format_input_baseline(inputs, baselines)
      additional_forward_args = _format_additional_forward_args(
          additional_forward_args
      )
      num_examples = inputs[0].shape[0]
      feature_mask = _format_feature_mask(feature_mask, inputs)
  
      assert (
          isinstance(perturbations_per_eval, int)
          and perturbations_per_eval >= 1
      ), "Perturbations per evaluation must be an integer and at least 1."
      with torch.no_grad():
          if show_progress:
              feature_counts = self._get_feature_counts(
                  inputs, feature_mask, **kwargs
              )
              total_forwards = (
                  sum(
                      math.ceil(count / perturbations_per_eval)
                      for count in feature_counts
                  )
                  + 1
              )  # add 1 for the initial eval
              attr_progress = progress(
                  desc=f"{self.get_name()} attribution",
                  total=total_forwards,
              )
              attr_progress.update(0)
  
          # Computes initial evaluation with all features, which is compared
          # to each ablated result.
          initial_eval = self._strict_run_forward(
              self.forward_func, inputs, target, additional_forward_args
          )
  
          if show_progress:
              attr_progress.update()
  
          # number of elements in the output of forward_func
          n_outputs = (
              initial_eval.numel()
              if isinstance(initial_eval, Tensor)
              else 1
          )
  
          # flatten eval outputs into 1D (n_outputs)
          # add the leading dim for n_feature_perturbed
          flattened_initial_eval = initial_eval.reshape(1, -1)
  
          # Initialize attribution totals and counts
          attrib_type = cast(dtype, flattened_initial_eval.dtype)
  
          total_attrib = [
              # attribute w.r.t each output element
              torch.zeros(
                  (n_outputs,) + input.shape[1:],
                  dtype=attrib_type,
                  device=input.device,
              )
              for input in inputs
          ]
  
          # Weights are used in cases where ablations may be overlapping.
          if self.use_weights:
              weights = [
                  torch.zeros(
                      (n_outputs,) + input.shape[1:],
                      device=input.device,
                  ).float()
                  for input in inputs
              ]
  
          # Iterate through each feature tensor for ablation
          for i in range(len(inputs)):
              # Skip any empty input tensors
              if torch.numel(inputs[i]) == 0:
                  continue
  
              for (
                  current_inputs,
                  current_add_args,
                  current_target,
                  current_mask,
              ) in self._ith_input_ablation_generator(
                  i,
                  inputs,
                  additional_forward_args,
                  target,
                  baselines,
                  feature_mask,
                  perturbations_per_eval,
                  **kwargs,
              ):
                  # modified_eval has (n_feature_perturbed * n_outputs) elements
                  # shape:
                  #   agg mode: (*initial_eval.shape)
                  #   non-agg mode:
                  #     (feature_perturbed * batch_size, *initial_eval.shape[1:])
                  modified_eval = self._strict_run_forward(
                      self.forward_func,
                      current_inputs,
                      current_target,
                      current_add_args,
                  )
  
                  if show_progress:
                      attr_progress.update()
  
                  # if perturbations_per_eval > 1, the output shape must grow with
                  # input and not be aggregated
                  if (
                      perturbations_per_eval > 1
                      and not self._is_output_shape_valid
                  ):
                      current_batch_size = current_inputs[0].shape[0]
  
                      # number of perturbation, which is not the same as
                      # perturbations_per_eval when not enough features to perturb
                      n_perturb = current_batch_size / num_examples
  
                      current_output_shape = modified_eval.shape
  
                      # use initial_eval as the forward of perturbations_per_eval = 1
                      initial_output_shape = initial_eval.shape
  
                      assert (
                          # check if the output is not a scalar
                          current_output_shape
                          and initial_output_shape
                          # check if the output grow in same ratio, i.e., not agg
                          and current_output_shape[0]
                          == n_perturb * initial_output_shape[0]
                      ), (
                          "When perturbations_per_eval > 1, forward_func's output "
                          "should be a tensor whose 1st dim grow with the input "
                          f"batch size: when input batch size is {num_examples}, "
                          f"the output shape is {initial_output_shape}; "
                          f"when input batch size is {current_batch_size}, "
                          f"the output shape is {current_output_shape}"
                      )
  
                      self._is_output_shape_valid = True
  
                  # reshape the leading dim for n_feature_perturbed
                  # flatten each feature's eval outputs into 1D of (n_outputs)
                  modified_eval = modified_eval.reshape(-1, n_outputs)
                  # eval_diff in shape (n_feature_perturbed, n_outputs)
                  eval_diff = flattened_initial_eval - modified_eval
  
                  # append the shape of one input example
                  # to make it broadcastable to mask
                  eval_diff = eval_diff.reshape(
                      eval_diff.shape + (inputs[i].dim() - 1) * (1,)
                  )
                  eval_diff = eval_diff.to(total_attrib[i].device)
  
                  if self.use_weights:
                      weights[i] += current_mask.float().sum(dim=0)
  
                  total_attrib[i] += (
                      eval_diff * current_mask.to(attrib_type)
                  ).sum(dim=0)
  
          if show_progress:
              attr_progress.close()
  
          # Divide total attributions by counts and return formatted attributions
          if self.use_weights:
              attrib = tuple(
                  single_attrib.float() / weight
                  for single_attrib, weight in zip(
                      total_attrib, weights
                  )
              )
          else:
              attrib = tuple(total_attrib)
          _result = _format_output(is_inputs_tuple, attrib)
      return _result
  
file_path: captum/captum/attr/_core/feature_ablation.py
imports: import math
  from typing import Any, Callable, cast, Tuple, Union
  import torch
  from captum._utils.common import _expand_additional_forward_args, _expand_target, _format_additional_forward_args, _format_feature_mask, _format_output, _is_tuple, _run_forward
  from captum._utils.progress import progress
  from captum._utils.typing import BaselineType, TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._utils.attribution import PerturbationAttribution
  from captum.attr._utils.common import _format_input_baseline
  from captum.log import log_usage
  from torch import dtype, Tensor
label_desc: Use image node_id 2 for calling the FeatureAblation obj's underlying member method code with example usage: obj.attribute(inputs, baselines, target, additional_forward_args, feature_mask, perturbations_per_eval, show_progress) and returns: _result