class_docstr: A perturbation based approach to compute attribution, involving
  replacing each contiguous rectangular region with a given baseline /
  reference, and computing the difference in output. For features located
  in multiple regions (hyperrectangles), the corresponding output differences
  are averaged to compute the attribution for that feature.
  
  The first patch is applied with the corner aligned with all indices 0,
  and strides are applied until the entire dimension range is covered. Note
  that this may cause the final patch applied in a direction to be cut-off
  and thus smaller than the target occlusion shape.
  
  More details regarding the occlusion (or grey-box / sliding window)
  method can be found in the original paper and in the DeepExplain
  implementation.
  https://arxiv.org/abs/1311.2901
  https://github.com/marcoancona/DeepExplain/blob/master/deepexplain\
  /tensorflow/methods.py#L401
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docstr: Args:
  
          inputs (Tensor or tuple[Tensor, ...]): Input for which occlusion
                      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.
          sliding_window_shapes (tuple or tuple[tuple]): Shape of patch
                      (hyperrectangle) to occlude each input. For a single
                      input tensor, this must be a tuple of length equal to the
                      number of dimensions of the input tensor - 1, defining
                      the dimensions of the patch. If the input tensor is 1-d,
                      this should be an empty tuple. For multiple input tensors,
                      this must be a tuple containing one tuple for each input
                      tensor defining the dimensions of the patch for that
                      input tensor, as described for the single tensor case.
          strides (int, tuple, tuple[int], or tuple[tuple], optional):
                      This defines the step by which the occlusion hyperrectangle
                      should be shifted by in each direction for each iteration.
                      For a single tensor input, this can be either a single
                      integer, which is used as the step size in each direction,
                      or a tuple of integers matching the number of dimensions
                      in the occlusion shape, defining the step size in the
                      corresponding dimension. For multiple tensor inputs, this
                      can be either a tuple of integers, one for each input
                      tensor (used for all dimensions of the corresponding
                      tensor), or a tuple of tuples, providing the stride per
                      dimension for each tensor.
                      To ensure that all inputs are covered by at least one
                      sliding window, the stride for any dimension must be
                      <= the corresponding sliding window dimension if the
                      sliding window dimension is less than the input
                      dimension.
                      If None is provided, a stride of 1 is used for each
                      dimension of each input tensor.
                      Default: None
          baselines (scalar, Tensor, tuple of scalar, or Tensor, optional):
                      Baselines define reference value which replaces each
                      feature when occluded.
                      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 difference is 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
          perturbations_per_eval (int, optional): Allows multiple occlusions
                      to be included in one batch (one call to forward_fn).
                      By default, perturbations_per_eval is 1, so each occlusion
                      is processed individually.
                      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
          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
  
  Returns:
          *Tensor* or *tuple[Tensor, ...]* of **attributions**:
          - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                      The attributions 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.
      >>> net = SimpleClassifier()
      >>> # Generating random input with size 2 x 4 x 4
      >>> input = torch.randn(2, 4, 4)
      >>> # Defining Occlusion interpreter
      >>> ablator = Occlusion(net)
      >>> # Computes occlusion attribution, ablating each 3x3 patch,
      >>> # shifting in each direction by the default of 1.
      >>> attr = ablator.attribute(input, target=1, sliding_window_shapes=(3,3))
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code: def attribute(  # type: ignore
      self,
      inputs: TensorOrTupleOfTensorsGeneric,
      sliding_window_shapes: Union[
          Tuple[int, ...], Tuple[Tuple[int, ...], ...]
      ],
      strides: Union[
          None,
          int,
          Tuple[int, ...],
          Tuple[Union[int, Tuple[int, ...]], ...],
      ] = None,
      baselines: BaselineType = None,
      target: TargetType = None,
      additional_forward_args: Any = None,
      perturbations_per_eval: int = 1,
      show_progress: bool = False,
  ) -> TensorOrTupleOfTensorsGeneric:
      r"""
      Args:
  
              inputs (Tensor or tuple[Tensor, ...]): Input for which occlusion
                          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.
              sliding_window_shapes (tuple or tuple[tuple]): Shape of patch
                          (hyperrectangle) to occlude each input. For a single
                          input tensor, this must be a tuple of length equal to the
                          number of dimensions of the input tensor - 1, defining
                          the dimensions of the patch. If the input tensor is 1-d,
                          this should be an empty tuple. For multiple input tensors,
                          this must be a tuple containing one tuple for each input
                          tensor defining the dimensions of the patch for that
                          input tensor, as described for the single tensor case.
              strides (int, tuple, tuple[int], or tuple[tuple], optional):
                          This defines the step by which the occlusion hyperrectangle
                          should be shifted by in each direction for each iteration.
                          For a single tensor input, this can be either a single
                          integer, which is used as the step size in each direction,
                          or a tuple of integers matching the number of dimensions
                          in the occlusion shape, defining the step size in the
                          corresponding dimension. For multiple tensor inputs, this
                          can be either a tuple of integers, one for each input
                          tensor (used for all dimensions of the corresponding
                          tensor), or a tuple of tuples, providing the stride per
                          dimension for each tensor.
                          To ensure that all inputs are covered by at least one
                          sliding window, the stride for any dimension must be
                          <= the corresponding sliding window dimension if the
                          sliding window dimension is less than the input
                          dimension.
                          If None is provided, a stride of 1 is used for each
                          dimension of each input tensor.
                          Default: None
              baselines (scalar, Tensor, tuple of scalar, or Tensor, optional):
                          Baselines define reference value which replaces each
                          feature when occluded.
                          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 difference is 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
              perturbations_per_eval (int, optional): Allows multiple occlusions
                          to be included in one batch (one call to forward_fn).
                          By default, perturbations_per_eval is 1, so each occlusion
                          is processed individually.
                          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
              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
  
      Returns:
              *Tensor* or *tuple[Tensor, ...]* of **attributions**:
              - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                          The attributions 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.
          >>> net = SimpleClassifier()
          >>> # Generating random input with size 2 x 4 x 4
          >>> input = torch.randn(2, 4, 4)
          >>> # Defining Occlusion interpreter
          >>> ablator = Occlusion(net)
          >>> # Computes occlusion attribution, ablating each 3x3 patch,
          >>> # shifting in each direction by the default of 1.
          >>> attr = ablator.attribute(input, target=1, sliding_window_shapes=(3,3))
      """
      formatted_inputs = _format_tensor_into_tuples(inputs)
  
      # Formatting strides
      strides = _format_and_verify_strides(strides, formatted_inputs)
  
      # Formatting sliding window shapes
      sliding_window_shapes = _format_and_verify_sliding_window_shapes(
          sliding_window_shapes, formatted_inputs
      )
  
      # Construct tensors from sliding window shapes
      sliding_window_tensors = tuple(
          torch.ones(window_shape, device=formatted_inputs[i].device)
          for i, window_shape in enumerate(sliding_window_shapes)
      )
  
      # Construct counts, defining number of steps to make of occlusion block in
      # each dimension.
      shift_counts = []
      for i, inp in enumerate(formatted_inputs):
          current_shape = np.subtract(
              inp.shape[1:], sliding_window_shapes[i]
          )
          # Verify sliding window doesn't exceed input dimensions.
          assert (np.array(current_shape) >= 0).all(), (
              "Sliding window dimensions {} cannot exceed input dimensions"
              "{}."
          ).format(sliding_window_shapes[i], tuple(inp.shape[1:]))
          # Stride cannot be larger than sliding window for any dimension where
          # the sliding window doesn't cover the entire input.
          assert np.logical_or(
              np.array(current_shape) == 0,
              np.array(strides[i]) <= sliding_window_shapes[i],
          ).all(), (
              "Stride dimension {} cannot be larger than sliding window "
              "shape dimension {}."
          ).format(
              strides[i], sliding_window_shapes[i]
          )
          shift_counts.append(
              tuple(
                  np.add(
                      np.ceil(
                          np.divide(current_shape, strides[i])
                      ).astype(int),
                      1,
                  )
              )
          )
  
      # Use ablation attribute method
      return super().attribute.__wrapped__(
          self,
          inputs,
          baselines=baselines,
          target=target,
          additional_forward_args=additional_forward_args,
          perturbations_per_eval=perturbations_per_eval,
          sliding_window_tensors=sliding_window_tensors,
          shift_counts=tuple(shift_counts),
          strides=strides,
          show_progress=show_progress,
      )
  
file_path: captum/captum/attr/_core/occlusion.py
imports: from typing import Any, Callable, Tuple, Union
  import numpy
  import torch
  from captum._utils.common import _format_tensor_into_tuples
  from captum._utils.typing import BaselineType, TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.feature_ablation import FeatureAblation
  from captum.attr._utils.common import _format_and_verify_sliding_window_shapes, _format_and_verify_strides
  from captum.log import log_usage
  from torch import Tensor
label_desc: Use image node_id 2 for calling the Occlusion obj's underlying member method code with example usage: obj.attribute(inputs, sliding_window_shapes, strides, baselines, target, additional_forward_args, perturbations_per_eval, show_progress) and returns: super