layer¶
LayerConductance¶

class
captum.attr._core.layer_conductance.
LayerConductance
(forward_func, layer, device_ids=None)[source]¶  Parameters
forward_func (callable) – The forward function of the model or any modification of it
layer (torch.nn.Module) – Layer for which attributions are computed. Output size of attribute matches this layer’s input or output dimensions, depending on whether we attribute to the inputs or outputs of the layer, corresponding to attribution of each neuron in the input or output of this layer. Currently, it is assumed that the inputs or the outputs of the layer, depending on which one is used for attribution, can only be a single tensor.
device_ids (list(int)) – Device ID list, necessary only if forward_func applies a DataParallel model. This allows reconstruction of intermediate outputs from batched results across devices. If forward_func is given as the DataParallel model itself, then it is not necessary to provide this argument.

attribute
(inputs, baselines=None, target=None, additional_forward_args=None, n_steps=50, method='riemann_trapezoid', internal_batch_size=None, return_convergence_delta=False, attribute_to_layer_input=False)[source]¶ Computes conductance with respect to the given layer. The returned output is in the shape of the layer’s output, showing the total conductance of each hidden layer neuron.
The details of the approach can be found here: https://arxiv.org/abs/1805.12233 https://arxiv.org/pdf/1807.09946.pdf
Note that this provides the total conductance of each neuron in the layer’s output. To obtain the breakdown of a neuron’s conductance by input features, utilize NeuronConductance instead, and provide the target neuron index.
 Parameters
inputs (tensor or tuple of tensors) – Input for which layer conductance is 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.
baselines (scalar, tensor, tuple of scalars or tensors, optional) –
Baselines define the starting point from which integral is computed and can be provided as:
 a single tensor, if inputs is a single tensor, with
exactly the same dimensions as inputs or the first dimension is one and the remaining dimensions match with 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 (tuple, 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 (nontuple) 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. It will be repeated for each of n_steps along the integrated path. 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
n_steps (int, optional) – The number of steps used by the approximation method. Default: 50.
method (string, optional) – Method for approximating the integral, one of riemann_right, riemann_left, riemann_middle, riemann_trapezoid or gausslegendre. Default: gausslegendre if no method is provided.
internal_batch_size (int, optional) – Divides total #steps * #examples data points into chunks of size internal_batch_size, which are computed (forward / backward passes) sequentially. For DataParallel models, each batch is split among the available devices, so evaluations on each available device contain internal_batch_size / num_devices examples. If internal_batch_size is None, then all evaluations are processed in one batch. Default: None
return_convergence_delta (bool, optional) – Indicates whether to return convergence delta or not. If return_convergence_delta is set to True convergence delta will be returned in a tuple following attributions. Default: False
attribute_to_layer_input (bool, optional) – Indicates whether to compute the attribution 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 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
 attributions (tensor):
Conductance of each neuron in given layer input or output. Attributions will always be the same size as the input or output of the given layer, depending on whether we attribute to the inputs or outputs of the layer which is decided by the input flag attribute_to_layer_input.
 delta (tensor, returned if return_convergence_delta=True):
The difference between the total approximated and true conductance. This is computed using the property that the total sum of forward_func(inputs)  forward_func(baselines) must equal the total sum of the attributions. Delta is calculated per example, meaning that the number of elements in returned delta tensor is equal to the number of of examples in inputs.
 Return type
attributions or 2element tuple of attributions, delta
Examples:
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. >>> # It contains an attribute conv1, which is an instance of nn.conv2d, >>> # and the output of this layer has dimensions Nx12x32x32. >>> net = ImageClassifier() >>> layer_cond = LayerConductance(net, net.conv1) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # Computes layer conductance for class 3. >>> # attribution size matches layer output, Nx12x32x32 >>> attribution = layer_cond.attribute(input, target=3)

has_convergence_delta
()[source]¶ This method informs the user whether the attribution algorithm provides a convergence delta (aka an approximation error) or not. Convergence delta may serve as a proxy of correctness of attribution algorithm’s approximation. If deriving attribution class provides a compute_convergence_delta method, it should override both compute_convergence_delta and has_convergence_delta methods.
 Returns
Returns whether the attribution algorithm provides a convergence delta (aka approximation error) or not.
 Return type
LayerActivation¶

class
captum.attr._core.layer_activation.
LayerActivation
(forward_func, layer, device_ids=None)[source]¶  Parameters
forward_func (callable) – The forward function of the model or any modification of it
layer (torch.nn.Module) – Layer for which attributions are computed. Output size of attribute matches this layer’s input or output dimensions, depending on whether we attribute to the inputs or outputs of the layer, corresponding to attribution of each neuron in the input or output of this layer. Currently, it is assumed that the inputs or the outputs of the layer, depending on which one is used for attribution, can only be a single tensor.
device_ids (list(int)) – Device ID list, necessary only if forward_func applies a DataParallel model. This allows reconstruction of intermediate outputs from batched results across devices. If forward_func is given as the DataParallel model itself, then it is not necessary to provide this argument.

attribute
(inputs, additional_forward_args=None, attribute_to_layer_input=False)[source]¶ Computes activation of selected layer for given input.
 Parameters
inputs (tensor or tuple of tensors) – Input for which layer activation is 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.
additional_forward_args (tuple, 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 (nontuple) 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 attribution 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 layer input, otherwise it will be computed with respect to layer output. 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
 attributions (tensor):
Activation of each neuron in given layer output. Attributions will always be the same size as the output of the given layer.
 Return type
tensor of attributions
Examples:
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. >>> # It contains an attribute conv1, which is an instance of nn.conv2d, >>> # and the output of this layer has dimensions Nx12x32x32. >>> net = ImageClassifier() >>> layer_act = LayerActivation(net, net.conv1) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # Computes layer activation. >>> # attribution is layer output, with size Nx12x32x32 >>> attribution = layer_cond.attribute(input)
InternalInfluence¶

class
captum.attr._core.internal_influence.
InternalInfluence
(forward_func, layer, device_ids=None)[source]¶  Parameters
forward_func (callable) – The forward function of the model or any modification of it
layer (torch.nn.Module) – Layer for which attributions are computed. Output size of attribute matches this layer’s input or output dimensions, depending on whether we attribute to the inputs or outputs of the layer, corresponding to attribution of each neuron in the input or output of this layer. Currently, it is assumed that the inputs or the outputs of the layer, depending on which one is used for attribution can only be a single tensor.
device_ids (list(int)) – Device ID list, necessary only if forward_func applies a DataParallel model. This allows reconstruction of intermediate outputs from batched results across devices. If forward_func is given as the DataParallel model itself, then it is not necessary to provide this argument.

attribute
(inputs, baselines=None, target=None, additional_forward_args=None, n_steps=50, method='gausslegendre', internal_batch_size=None, attribute_to_layer_input=False)[source]¶ Computes internal influence by approximating the integral of gradients for a particular layer along the path from a baseline input to the given input. If no baseline is provided, the default baseline is the zero tensor. More details on this approach can be found here: https://arxiv.org/pdf/1802.03788.pdf
Note that this method is similar to applying integrated gradients and taking the layer as input, integrating the gradient of the layer with respect to the output.
 Parameters
inputs (tensor or tuple of tensors) – Input for which internal influence is 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.
scalar, tensor, tuple of scalars or tensors, optional) (baselines) –
Baselines define a starting point from which integral is computed and can be provided as:
 a single tensor, if inputs is a single tensor, with
exactly the same dimensions as inputs or the first dimension is one and the remaining dimensions match with 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 (tuple, 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 (nontuple) 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. It will be repeated for each of n_steps along the integrated path. 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
n_steps (int, optional) – The number of steps used by the approximation method. Default: 50.
method (string, optional) – Method for approximating the integral, one of riemann_right, riemann_left, riemann_middle, riemann_trapezoid or gausslegendre. Default: gausslegendre if no method is provided.
internal_batch_size (int, optional) – Divides total #steps * #examples data points into chunks of size internal_batch_size, which are computed (forward / backward passes) sequentially. For DataParallel models, each batch is split among the available devices, so evaluations on each available device contain internal_batch_size / num_devices examples. If internal_batch_size is None, then all evaluations are processed in one batch. Default: None
attribute_to_layer_input (bool, optional) – Indicates whether to compute the attribution 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 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
 attributions (tensor):
Internal influence of each neuron in given layer output. Attributions will always be the same size as the output or input of the given layer depending on whether attribute_to_layer_input is set to False or `True`respectively.
 Return type
tensor of attributions
Examples:
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. >>> # It contains an attribute conv1, which is an instance of nn.conv2d, >>> # and the output of this layer has dimensions Nx12x32x32. >>> net = ImageClassifier() >>> layer_int_inf = InternalInfluence(net, net.conv1) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # Computes layer internal influence. >>> # attribution size matches layer output, Nx12x32x32 >>> attribution = layer_int_inf.attribute(input)
LayerGradientXActivation¶

class
captum.attr._core.layer_gradient_x_activation.
LayerGradientXActivation
(forward_func, layer, device_ids=None)[source]¶  Parameters
forward_func (callable) – The forward function of the model or any modification of it
layer (torch.nn.Module) – Layer for which attributions are computed. Output size of attribute matches this layer’s input or output dimensions, depending on whether we attribute to the inputs or outputs of the layer, corresponding to attribution of each neuron in the input or output of this layer. Currently, it is assumed that the inputs or the outputs of the layer, depending on which one is used for attribution, can only be a single tensor.
device_ids (list(int)) – Device ID list, necessary only if forward_func applies a DataParallel model. This allows reconstruction of intermediate outputs from batched results across devices. If forward_func is given as the DataParallel model itself, then it is not necessary to provide this argument.

attribute
(inputs, target=None, additional_forward_args=None, attribute_to_layer_input=False)[source]¶ Computes elementwise product of gradient and activation for selected layer on given inputs.
 Parameters
inputs (tensor or tuple of tensors) – 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 (tuple, 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 (nontuple) 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 attribution 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 layer input, otherwise it will be computed with respect to layer output. 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
 attributions (tensor):
Product of gradient and activation for each neuron in given layer output. Attributions will always be the same size as the output of the given layer.
 Return type
tensor of attributions
Examples:
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. >>> # It contains an attribute conv1, which is an instance of nn.conv2d, >>> # and the output of this layer has dimensions Nx12x32x32. >>> net = ImageClassifier() >>> layer_ga = LayerGradientXActivation(net, net.conv1) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # Computes layer activation x gradient for class 3. >>> # attribution size matches layer output, Nx12x32x32 >>> attribution = layer_ga.attribute(input, 3)