class_docstr: Computes element-wise product of guided backpropagation attributions
  with upsampled (non-negative) GradCAM attributions.
  GradCAM attributions are computed with respect to the layer
  provided in the constructor, and attributions
  are upsampled to match the input size. GradCAM is designed for
  convolutional neural networks, and is usually applied to the last
  convolutional layer.
  
  Note that if multiple input tensors are provided, attributions for
  each input tensor are computed by upsampling the GradCAM
  attributions to match that input's dimensions. If interpolation is
  not possible for the input tensor dimensions and interpolation mode,
  then an empty tensor is returned in the attributions for the
  corresponding position of that input tensor. This can occur if the
  input tensor does not have the same number of dimensions as the chosen
  layer's output or is not either 3D, 4D or 5D.
  
  Note that attributions are only meaningful for input tensors
  which are spatially alligned with the chosen layer, e.g. an input
  image tensor for a convolutional layer.
  
  More details regarding GuidedGradCAM can be found in the original
  GradCAM paper here:
  https://arxiv.org/abs/1610.02391
  
  Warning: Ensure that all ReLU operations in the forward function of the
  given model are performed using a module (nn.module.ReLU).
  If nn.functional.ReLU is used, gradients are not overridden appropriately.
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docstr: Args:
  
      inputs (Tensor or tuple[Tensor, ...]): Input for which attributions
                  are computed. If model takes a single
                  tensor as input, a single input tensor should be provided.
                  If model 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 (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 model in order following the
                  arguments in inputs.
                  Note that attributions are not computed with respect
                  to these arguments.
                  Default: None
      interpolate_mode (str, optional): Method for interpolation, which
                  must be a valid input interpolation mode for
                  torch.nn.functional. These methods are
                  "nearest", "area", "linear" (3D-only), "bilinear"
                  (4D-only), "bicubic" (4D-only), "trilinear" (5D-only)
                  based on the number of dimensions of the chosen layer
                  output (which must also match the number of
                  dimensions for the input tensor). Note that
                  the original GradCAM paper uses "bilinear"
                  interpolation, but we default to "nearest" for
                  applicability to any of 3D, 4D or 5D tensors.
                  Default: "nearest"
      attribute_to_layer_input (bool, optional): Indicates whether to
                  compute the attribution with respect to the layer input
                  or output in `LayerGradCam`.
                  If `attribute_to_layer_input` is set to True
                  then the attributions will be computed with respect to
                  layer inputs, otherwise it will be computed with respect
                  to layer outputs.
                  Note that currently it is assumed that either the input
                  or the output of internal layer, depending on whether we
                  attribute to the input or output, is a single tensor.
                  Support for multiple tensors will be added later.
                  Default: False
  
  Returns:
      *Tensor* of **attributions**:
      - **attributions** (*Tensor*):
              Element-wise product of (upsampled) GradCAM
              and Guided Backprop attributions.
              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.
              Attributions will be the same size as the provided inputs,
              with each value providing the attribution of the
              corresponding input index.
              If the GradCAM attributions cannot be upsampled to the shape
              of a given input tensor, None is returned in the corresponding
              index position.
  
  
  Examples::
  
      >>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
      >>> # and returns an Nx10 tensor of class probabilities.
      >>> # It contains an attribute conv4, which is an instance of nn.conv2d,
      >>> # and the output of this layer has dimensions Nx50x8x8.
      >>> # It is the last convolution layer, which is the recommended
      >>> # use case for GuidedGradCAM.
      >>> net = ImageClassifier()
      >>> guided_gc = GuidedGradCam(net, net.conv4)
      >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
      >>> # Computes guided GradCAM attributions for class 3.
      >>> # attribution size matches input size, Nx3x32x32
      >>> attribution = guided_gc.attribute(input, 3)
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code: def attribute(
      self,
      inputs: TensorOrTupleOfTensorsGeneric,
      target: TargetType = None,
      additional_forward_args: Any = None,
      interpolate_mode: str = "nearest",
      attribute_to_layer_input: bool = False,
  ) -> TensorOrTupleOfTensorsGeneric:
      r"""
      Args:
  
          inputs (Tensor or tuple[Tensor, ...]): Input for which attributions
                      are computed. If model takes a single
                      tensor as input, a single input tensor should be provided.
                      If model 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 (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 model in order following the
                      arguments in inputs.
                      Note that attributions are not computed with respect
                      to these arguments.
                      Default: None
          interpolate_mode (str, optional): Method for interpolation, which
                      must be a valid input interpolation mode for
                      torch.nn.functional. These methods are
                      "nearest", "area", "linear" (3D-only), "bilinear"
                      (4D-only), "bicubic" (4D-only), "trilinear" (5D-only)
                      based on the number of dimensions of the chosen layer
                      output (which must also match the number of
                      dimensions for the input tensor). Note that
                      the original GradCAM paper uses "bilinear"
                      interpolation, but we default to "nearest" for
                      applicability to any of 3D, 4D or 5D tensors.
                      Default: "nearest"
          attribute_to_layer_input (bool, optional): Indicates whether to
                      compute the attribution with respect to the layer input
                      or output in `LayerGradCam`.
                      If `attribute_to_layer_input` is set to True
                      then the attributions will be computed with respect to
                      layer inputs, otherwise it will be computed with respect
                      to layer outputs.
                      Note that currently it is assumed that either the input
                      or the output of internal layer, depending on whether we
                      attribute to the input or output, is a single tensor.
                      Support for multiple tensors will be added later.
                      Default: False
  
      Returns:
          *Tensor* of **attributions**:
          - **attributions** (*Tensor*):
                  Element-wise product of (upsampled) GradCAM
                  and Guided Backprop attributions.
                  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.
                  Attributions will be the same size as the provided inputs,
                  with each value providing the attribution of the
                  corresponding input index.
                  If the GradCAM attributions cannot be upsampled to the shape
                  of a given input tensor, None is returned in the corresponding
                  index position.
  
  
      Examples::
  
          >>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
          >>> # and returns an Nx10 tensor of class probabilities.
          >>> # It contains an attribute conv4, which is an instance of nn.conv2d,
          >>> # and the output of this layer has dimensions Nx50x8x8.
          >>> # It is the last convolution layer, which is the recommended
          >>> # use case for GuidedGradCAM.
          >>> net = ImageClassifier()
          >>> guided_gc = GuidedGradCam(net, net.conv4)
          >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
          >>> # Computes guided GradCAM attributions for class 3.
          >>> # attribution size matches input size, Nx3x32x32
          >>> attribution = guided_gc.attribute(input, 3)
      """
      is_inputs_tuple = _is_tuple(inputs)
      inputs = _format_tensor_into_tuples(inputs)
      grad_cam_attr = self.grad_cam.attribute.__wrapped__(
          self.grad_cam,  # self
          inputs=inputs,
          target=target,
          additional_forward_args=additional_forward_args,
          attribute_to_layer_input=attribute_to_layer_input,
          relu_attributions=True,
      )
      if isinstance(grad_cam_attr, tuple):
          assert len(grad_cam_attr) == 1, (
              "GuidedGradCAM attributions for layer with multiple inputs / "
              "outputs is not supported."
          )
          grad_cam_attr = grad_cam_attr[0]
  
      guided_backprop_attr = self.guided_backprop.attribute.__wrapped__(
          self.guided_backprop,  # self
          inputs=inputs,
          target=target,
          additional_forward_args=additional_forward_args,
      )
      output_attr: List[Tensor] = []
      for i in range(len(inputs)):
          try:
              output_attr.append(
                  guided_backprop_attr[i]
                  * LayerAttribution.interpolate(
                      grad_cam_attr,
                      inputs[i].shape[2:],
                      interpolate_mode=interpolate_mode,
                  )
              )
          except Exception:
              warnings.warn(
                  "Couldn't appropriately interpolate GradCAM attributions for some "
                  "input tensors, returning empty tensor for corresponding "
                  "attributions."
              )
              output_attr.append(torch.empty(0))
  
      return _format_output(is_inputs_tuple, tuple(output_attr))
  
file_path: captum/captum/attr/_core/guided_grad_cam.py
imports: import warnings
  from typing import Any, List, Union
  import torch
  from captum._utils.common import _format_output, _format_tensor_into_tuples, _is_tuple
  from captum._utils.typing import TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.guided_backprop_deconvnet import GuidedBackprop
  from captum.attr._core.layer.grad_cam import LayerGradCam
  from captum.attr._utils.attribution import GradientAttribution, LayerAttribution
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 2 for calling the GuidedGradCam obj's underlying member method code with example usage: obj.attribute(inputs, target, additional_forward_args, interpolate_mode, attribute_to_layer_input) and returns: _format_output