class_docstr: Computes GradCAM attribution for chosen layer. GradCAM is designed for
  convolutional neural networks, and is usually applied to the last
  convolutional layer.
  
  GradCAM computes the gradients of the target output with respect to
  the given layer, averages for each output channel (dimension 2 of
  output), and multiplies the average gradient for each channel by the
  layer activations. The results are summed over all channels.
  
  Note that in the original GradCAM algorithm described in the paper,
  ReLU is applied to the output, returning only non-negative attributions.
  For providing more flexibility to the user, we choose to not perform the
  ReLU internally by default and return the sign information. To match the
  original GradCAM algorithm, it is necessary to pass the parameter
  relu_attributions=True to apply ReLU on the final
  attributions or alternatively only visualize the positive attributions.
  
  Note: this procedure sums over the second dimension (# of channels),
  so the output of GradCAM attributions will have a second
  dimension of 1, but all other dimensions will match that of the layer
  output.
  
  GradCAM attributions are generally upsampled and can be viewed as a
  mask to the input, since a convolutional layer output generally
  matches the input image spatially. This upsampling can be performed
  using LayerAttribution.interpolate, as shown in the example below.
  
  More details regarding the GradCAM method can be found in the
  original paper here:
  https://arxiv.org/abs/1610.02391
class_docstr_tok: ["Computes","GradCAM","attribution","for","chosen","layer",".","GradCAM","is","designed","for","convolutional","neural","networks",",","and","is","usually","applied","to","the","last","convolutional","layer",".","GradCAM","computes","the","gradients","of","the","target","output","with","respect","to","the","given","layer",",","averages","for","each","output","channel","(","dimension","2","of","output",")",",","and","multiplies","the","average","gradient","for","each","channel","by","the","layer","activations",".","The","results","are","summed","over","all","channels",".","Note","that","in","the","original","GradCAM","algorithm","described","in","the","paper",",","ReLU","is","applied","to","the","output",",","returning","only","non-negative","attributions",".","For","providing","more","flexibility","to","the","user",",","we","choose","to","not","perform","the","ReLU","internally","by","default","and","return","the","sign","information",".","To","match","the","original","GradCAM","algorithm",",","it","is","necessary","to","pass","the","parameter","relu_attributions=True","to","apply","ReLU","on","the","final","attributions","or","alternatively","only","visualize","the","positive","attributions",".","Note",":","this","procedure","sums","over","the","second","dimension","(","#","of","channels",")",",","so","the","output","of","GradCAM","attributions","will","have","a","second","dimension","of","1",",","but","all","other","dimensions","will","match","that","of","the","layer","output",".","GradCAM","attributions","are","generally","upsampled","and","can","be","viewed","as","a","mask","to","the","input",",","since","a","convolutional","layer","output","generally","matches","the","input","image","spatially",".","This","upsampling","can","be","performed","using","LayerAttribution.interpolate",",","as","shown","in","the","example","below",".","More","details","regarding","the","GradCAM","method","can","be","found","in","the","original","paper","here",":","https",":","\/\/arxiv.org\/abs\/1610.02391"]
docstr: Args:
  
      inputs (Tensor or tuple[Tensor, ...]): Input for which 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.
      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.
                  Note that attributions are not computed with respect
                  to these arguments.
                  Default: None
      attribute_to_layer_input (bool, optional): Indicates whether to
                  compute the attributions with respect to the layer input
                  or output. If `attribute_to_layer_input` is set to True
                  then the attributions will be computed with respect to the
                  layer input, otherwise it will be computed with respect
                  to layer output.
                  Note that currently it is assumed that either the input
                  or the outputs of internal layers, depending on whether we
                  attribute to the input or output, are single tensors.
                  Support for multiple tensors will be added later.
                  Default: False
      relu_attributions (bool, optional): Indicates whether to
                  apply a ReLU operation on the final attribution,
                  returning only non-negative attributions. Setting this
                  flag to True matches the original GradCAM algorithm,
                  otherwise, by default, both positive and negative
                  attributions are returned.
                  Default: False
      attr_dim_summation (bool, optional): Indicates whether to
                  sum attributions along dimension 1 (usually channel).
                  The default (True) means to sum along dimension 1.
                  Default: True
  
  Returns:
      *Tensor* or *tuple[Tensor, ...]* of **attributions**:
      - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                  Attributions based on GradCAM method.
                  Attributions will be the same size as the
                  output of the given layer, except for dimension 2,
                  which will be 1 due to summing over channels.
                  Attributions are returned in a tuple if
                  the layer inputs / outputs contain multiple tensors,
                  otherwise a single tensor is returned.
  Examples::
  
      >>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
      >>> # and returns an Nx10 tensor of class probabilities.
      >>> # It contains a layer 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 GradCAM.
      >>> net = ImageClassifier()
      >>> layer_gc = LayerGradCam(net, net.conv4)
      >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
      >>> # Computes layer GradCAM for class 3.
      >>> # attribution size matches layer output except for dimension
      >>> # 1, so dimensions of attr would be Nx1x8x8.
      >>> attr = layer_gc.attribute(input, 3)
      >>> # GradCAM attributions are often upsampled and viewed as a
      >>> # mask to the input, since the convolutional layer output
      >>> # spatially matches the original input image.
      >>> # This can be done with LayerAttribution's interpolate method.
      >>> upsampled_attr = LayerAttribution.interpolate(attr, (32, 32))
docstr_tok: ["Args",":","inputs","(","Tensor","or","tuple","[","Tensor",",","...","]",")",":","Input","for","which","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",".","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",".","Note","that","attributions","are","not","computed","with","respect","to","these","arguments",".","Default",":","None","attribute_to_layer_input","(","bool",",","optional",")",":","Indicates","whether","to","compute","the","attributions","with","respect","to","the","layer","input","or","output",".","If","`","attribute_to_layer_input","`","is","set","to","True","then","the","attributions","will","be","computed","with","respect","to","the","layer","input",",","otherwise","it","will","be","computed","with","respect","to","layer","output",".","Note","that","currently","it","is","assumed","that","either","the","input","or","the","outputs","of","internal","layers",",","depending","on","whether","we","attribute","to","the","input","or","output",",","are","single","tensors",".","Support","for","multiple","tensors","will","be","added","later",".","Default",":","False","relu_attributions","(","bool",",","optional",")",":","Indicates","whether","to","apply","a","ReLU","operation","on","the","final","attribution",",","returning","only","non-negative","attributions",".","Setting","this","flag","to","True","matches","the","original","GradCAM","algorithm",",","otherwise",",","by","default",",","both","positive","and","negative","attributions","are","returned",".","Default",":","False","attr_dim_summation","(","bool",",","optional",")",":","Indicates","whether","to","sum","attributions","along","dimension","1","(","usually","channel",")",".","The","default","(","True",")","means","to","sum","along","dimension","1",".","Default",":","True","Returns",":","*","Tensor","*","or","*","tuple","[","Tensor",",","...","]","*","of","*","*","attributions","*","*",":","-","*","*","attributions","*","*","(","*","Tensor","*","or","*","tuple","[","Tensor",",","...","]","*",")",":","Attributions","based","on","GradCAM","method",".","Attributions","will","be","the","same","size","as","the","output","of","the","given","layer",",","except","for","dimension","2",",","which","will","be","1","due","to","summing","over","channels",".","Attributions","are","returned","in","a","tuple","if","the","layer","inputs","\/","outputs","contain","multiple","tensors",",","otherwise","a","single","tensor","is","returned",".","Examples",":",":",">",">",">","#","ImageClassifier","takes","a","single","input","tensor","of","images","Nx3x32x32",",",">",">",">","#","and","returns","an","Nx10","tensor","of","class","probabilities",".",">",">",">","#","It","contains","a","layer","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","GradCAM",".",">",">",">","net","=","ImageClassifier","(",")",">",">",">","layer_gc","=","LayerGradCam","(","net",",","net.conv4",")",">",">",">","input","=","torch.randn","(","2",",","3",",","32",",","32",",","requires_grad=True",")",">",">",">","#","Computes","layer","GradCAM","for","class","3",".",">",">",">","#","attribution","size","matches","layer","output","except","for","dimension",">",">",">","#","1",",","so","dimensions","of","attr","would","be","Nx1x8x8",".",">",">",">","attr","=","layer_gc.attribute","(","input",",","3",")",">",">",">","#","GradCAM","attributions","are","often","upsampled","and","viewed","as","a",">",">",">","#","mask","to","the","input",",","since","the","convolutional","layer","output",">",">",">","#","spatially","matches","the","original","input","image",".",">",">",">","#","This","can","be","done","with","LayerAttribution","'s","interpolate","method",".",">",">",">","upsampled_attr","=","LayerAttribution.interpolate","(","attr",",","(","32",",","32",")",")"]
code: def attribute(
      self,
      inputs: Union[Tensor, Tuple[Tensor, ...]],
      target: TargetType = None,
      additional_forward_args: Any = None,
      attribute_to_layer_input: bool = False,
      relu_attributions: bool = False,
      attr_dim_summation: bool = True,
  ) -> Union[Tensor, Tuple[Tensor, ...]]:
      r"""
      Args:
  
          inputs (Tensor or tuple[Tensor, ...]): Input for which 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.
          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.
                      Note that attributions are not computed with respect
                      to these arguments.
                      Default: None
          attribute_to_layer_input (bool, optional): Indicates whether to
                      compute the attributions with respect to the layer input
                      or output. If `attribute_to_layer_input` is set to True
                      then the attributions will be computed with respect to the
                      layer input, otherwise it will be computed with respect
                      to layer output.
                      Note that currently it is assumed that either the input
                      or the outputs of internal layers, depending on whether we
                      attribute to the input or output, are single tensors.
                      Support for multiple tensors will be added later.
                      Default: False
          relu_attributions (bool, optional): Indicates whether to
                      apply a ReLU operation on the final attribution,
                      returning only non-negative attributions. Setting this
                      flag to True matches the original GradCAM algorithm,
                      otherwise, by default, both positive and negative
                      attributions are returned.
                      Default: False
          attr_dim_summation (bool, optional): Indicates whether to
                      sum attributions along dimension 1 (usually channel).
                      The default (True) means to sum along dimension 1.
                      Default: True
  
      Returns:
          *Tensor* or *tuple[Tensor, ...]* of **attributions**:
          - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                      Attributions based on GradCAM method.
                      Attributions will be the same size as the
                      output of the given layer, except for dimension 2,
                      which will be 1 due to summing over channels.
                      Attributions are returned in a tuple if
                      the layer inputs / outputs contain multiple tensors,
                      otherwise a single tensor is returned.
      Examples::
  
          >>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
          >>> # and returns an Nx10 tensor of class probabilities.
          >>> # It contains a layer 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 GradCAM.
          >>> net = ImageClassifier()
          >>> layer_gc = LayerGradCam(net, net.conv4)
          >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
          >>> # Computes layer GradCAM for class 3.
          >>> # attribution size matches layer output except for dimension
          >>> # 1, so dimensions of attr would be Nx1x8x8.
          >>> attr = layer_gc.attribute(input, 3)
          >>> # GradCAM attributions are often upsampled and viewed as a
          >>> # mask to the input, since the convolutional layer output
          >>> # spatially matches the original input image.
          >>> # This can be done with LayerAttribution's interpolate method.
          >>> upsampled_attr = LayerAttribution.interpolate(attr, (32, 32))
      """
      inputs = _format_tensor_into_tuples(inputs)
      additional_forward_args = _format_additional_forward_args(
          additional_forward_args
      )
      # Returns gradient of output with respect to
      # hidden layer and hidden layer evaluated at each input.
      layer_gradients, layer_evals = compute_layer_gradients_and_eval(
          self.forward_func,
          self.layer,
          inputs,
          target,
          additional_forward_args,
          device_ids=self.device_ids,
          attribute_to_layer_input=attribute_to_layer_input,
      )
  
      summed_grads = tuple(
          torch.mean(
              layer_grad,
              dim=tuple(x for x in range(2, len(layer_grad.shape))),
              keepdim=True,
          )
          if len(layer_grad.shape) > 2
          else layer_grad
          for layer_grad in layer_gradients
      )
  
      if attr_dim_summation:
          scaled_acts = tuple(
              torch.sum(summed_grad * layer_eval, dim=1, keepdim=True)
              for summed_grad, layer_eval in zip(
                  summed_grads, layer_evals
              )
          )
      else:
          scaled_acts = tuple(
              summed_grad * layer_eval
              for summed_grad, layer_eval in zip(
                  summed_grads, layer_evals
              )
          )
  
      if relu_attributions:
          scaled_acts = tuple(
              F.relu(scaled_act) for scaled_act in scaled_acts
          )
      return _format_output(len(scaled_acts) > 1, scaled_acts)
  
file_path: captum/captum/attr/_core/layer/grad_cam.py
imports: from typing import Any, Callable, List, Tuple, Union
  import torch
  import torch.nn.functional
  from captum._utils.common import _format_additional_forward_args, _format_output, _format_tensor_into_tuples
  from captum._utils.gradient import compute_layer_gradients_and_eval
  from captum._utils.typing import TargetType
  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 LayerGradCam obj's underlying member method code with example usage: obj.attribute(inputs, target, additional_forward_args, attribute_to_layer_input, relu_attributions, attr_dim_summation) and returns: _format_output