# Source code for captum.attr._core.neuron.neuron_conductance

```
#!/usr/bin/env python3
import warnings
from typing import Any, Callable, List, Tuple, Union
import torch
from captum._utils.common import (
_expand_additional_forward_args,
_expand_target,
_format_additional_forward_args,
_format_output,
_is_tuple,
_verify_select_neuron,
)
from captum._utils.gradient import compute_layer_gradients_and_eval
from captum._utils.typing import BaselineType, TargetType, TensorOrTupleOfTensorsGeneric
from captum.attr._utils.approximation_methods import approximation_parameters
from captum.attr._utils.attribution import GradientAttribution, NeuronAttribution
from captum.attr._utils.batching import _batch_attribution
from captum.attr._utils.common import (
_format_input_baseline,
_reshape_and_sum,
_validate_input,
)
from captum.log import log_usage
from torch import Tensor
from torch.nn import Module
[docs]
class NeuronConductance(NeuronAttribution, GradientAttribution):
r"""
Computes conductance with respect to particular hidden neuron. The
returned output is in the shape of the input, showing the attribution
/ conductance of each input feature to the selected hidden layer neuron.
The details of the approach can be found here:
https://arxiv.org/abs/1805.12233
"""
def __init__(
self,
forward_func: Callable,
layer: Module,
device_ids: Union[None, List[int]] = None,
multiply_by_inputs: bool = True,
) -> None:
r"""
Args:
forward_func (Callable): The forward function of the model or any
modification of it
layer (torch.nn.Module): Layer for which neuron attributions are computed.
Attributions for a particular neuron in the input or output
of this layer are computed using the argument neuron_selector
in the attribute method.
Currently, only layers with a single tensor input or output
are supported.
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.
multiply_by_inputs (bool, optional): Indicates whether to factor
model inputs' multiplier in the final attribution scores.
In the literature this is also known as local vs global
attribution. If inputs' multiplier isn't factored in
then that type of attribution method is also called local
attribution. If it is, then that type of attribution
method is called global.
More detailed can be found here:
https://arxiv.org/abs/1711.06104
In case of Neuron Conductance,
if `multiply_by_inputs` is set to True, final
sensitivity scores are being multiplied
by (inputs - baselines).
"""
NeuronAttribution.__init__(self, forward_func, layer, device_ids)
GradientAttribution.__init__(self, forward_func)
self._multiply_by_inputs = multiply_by_inputs
[docs]
@log_usage()
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
neuron_selector: Union[int, Tuple[int, ...], Callable],
baselines: BaselineType = None,
target: TargetType = None,
additional_forward_args: Any = None,
n_steps: int = 50,
method: str = "riemann_trapezoid",
internal_batch_size: Union[None, int] = None,
attribute_to_neuron_input: bool = False,
) -> TensorOrTupleOfTensorsGeneric:
r"""
Args:
inputs (Tensor or tuple[Tensor, ...]): Input for which neuron
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.
neuron_selector (int, Callable, tuple[int], or slice):
Selector for neuron
in given layer for which attribution is desired.
Neuron selector can be provided as:
- a single integer, if the layer output is 2D. This integer
selects the appropriate neuron column in the layer input
or output
- a tuple of integers. Length of this
tuple must be one less than the number of dimensions
in the input / output of the given layer (since
dimension 0 corresponds to number of examples).
This can be used as long as the layer input / output
is a single tensor.
- a callable, which should
take the target layer as input (single tensor or tuple
if multiple tensors are in layer) and return a selected
neuron - output shape should be 1D with length equal to
batch_size (one scalar per input example)
NOTE: Callables applicable for neuron conductance are
less general than those of other methods and should
NOT aggregate values of the layer, only return a specific
output. This option should only be used in cases where the
layer input / output is a tuple of tensors, where the other
options would not suffice. This limitation is necessary since
neuron conductance, unlike other neuron methods, also utilizes
the gradient of output with respect to the intermedite neuron,
which cannot be computed for aggregations of multiple
intemediate neurons.
baselines (scalar, Tensor, tuple of scalar, or Tensor, 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 (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. 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 (str, 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 at most internal_batch_size,
which are computed (forward / backward passes)
sequentially. internal_batch_size must be at least equal to
#examples.
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_neuron_input (bool, optional): Indicates whether to
compute the attributions with respect to the neuron input
or output. If `attribute_to_neuron_input` is set to True
then the attributions will be computed with respect to
neuron's inputs, otherwise it will be computed with respect
to neuron's outputs.
Note that currently it is assumed that either the input
or the output of internal neuron, 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* or *tuple[Tensor, ...]* of **attributions**:
- **attributions** (*Tensor* or *tuple[Tensor, ...]*):
Conductance for
particular neuron 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::
>>> # 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()
>>> neuron_cond = NeuronConductance(net, net.conv1)
>>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
>>> # To compute neuron attribution, we need to provide the neuron
>>> # index for which attribution is desired. Since the layer output
>>> # is Nx12x32x32, we need a tuple in the form (0..11,0..31,0..31)
>>> # which indexes a particular neuron in the layer output.
>>> # Computes neuron conductance for neuron with
>>> # index (4,1,2).
>>> attribution = neuron_cond.attribute(input, (4,1,2))
"""
if callable(neuron_selector):
warnings.warn(
"The neuron_selector provided is a callable. Please ensure that this"
" function only selects neurons from the given layer; aggregating"
" or performing other operations on the tensor may lead to inaccurate"
" results."
)
is_inputs_tuple = _is_tuple(inputs)
inputs, baselines = _format_input_baseline(inputs, baselines)
_validate_input(inputs, baselines, n_steps, method)
num_examples = inputs[0].shape[0]
if internal_batch_size is not None:
num_examples = inputs[0].shape[0]
attrs = _batch_attribution(
self,
num_examples,
internal_batch_size,
n_steps,
inputs=inputs,
baselines=baselines,
neuron_selector=neuron_selector,
target=target,
additional_forward_args=additional_forward_args,
method=method,
attribute_to_neuron_input=attribute_to_neuron_input,
)
else:
attrs = self._attribute(
inputs=inputs,
neuron_selector=neuron_selector,
baselines=baselines,
target=target,
additional_forward_args=additional_forward_args,
n_steps=n_steps,
method=method,
attribute_to_neuron_input=attribute_to_neuron_input,
)
return _format_output(is_inputs_tuple, attrs)
def _attribute(
self,
inputs: Tuple[Tensor, ...],
neuron_selector: Union[int, Tuple[int, ...], Callable],
baselines: Tuple[Union[Tensor, int, float], ...],
target: TargetType = None,
additional_forward_args: Any = None,
n_steps: int = 50,
method: str = "riemann_trapezoid",
attribute_to_neuron_input: bool = False,
step_sizes_and_alphas: Union[None, Tuple[List[float], List[float]]] = None,
) -> Tuple[Tensor, ...]:
num_examples = inputs[0].shape[0]
total_batch = num_examples * n_steps
if step_sizes_and_alphas is None:
# retrieve step size and scaling factor for specified approximation method
step_sizes_func, alphas_func = approximation_parameters(method)
step_sizes, alphas = step_sizes_func(n_steps), alphas_func(n_steps)
else:
step_sizes, alphas = step_sizes_and_alphas
# Compute scaled inputs from baseline to final input.
scaled_features_tpl = tuple(
torch.cat(
[baseline + alpha * (input - baseline) for alpha in alphas], dim=0
).requires_grad_()
for input, baseline in zip(inputs, baselines)
)
additional_forward_args = _format_additional_forward_args(
additional_forward_args
)
# apply number of steps to additional forward args
# currently, number of steps is applied only to additional forward arguments
# that are nd-tensors. It is assumed that the first dimension is
# the number of batches.
# dim -> (#examples * #steps x additional_forward_args[0].shape[1:], ...)
input_additional_args = (
_expand_additional_forward_args(additional_forward_args, n_steps)
if additional_forward_args is not None
else None
)
expanded_target = _expand_target(target, n_steps)
# Conductance Gradients - Returns gradient of output with respect to
# hidden layer and hidden layer evaluated at each input.
layer_gradients, layer_eval, input_grads = compute_layer_gradients_and_eval(
forward_fn=self.forward_func,
layer=self.layer,
inputs=scaled_features_tpl,
target_ind=expanded_target,
additional_forward_args=input_additional_args,
gradient_neuron_selector=neuron_selector,
device_ids=self.device_ids,
attribute_to_layer_input=attribute_to_neuron_input,
)
mid_grads = _verify_select_neuron(layer_gradients, neuron_selector)
scaled_input_gradients = tuple(
input_grad
* mid_grads.reshape((total_batch,) + (1,) * (len(input_grad.shape) - 1))
for input_grad in input_grads
)
# Mutliplies by appropriate step size.
scaled_grads = tuple(
scaled_input_gradient.contiguous().view(n_steps, -1)
* torch.tensor(step_sizes).view(n_steps, 1).to(scaled_input_gradient.device)
for scaled_input_gradient in scaled_input_gradients
)
# Aggregates across all steps for each tensor in the input tuple
total_grads = tuple(
_reshape_and_sum(scaled_grad, n_steps, num_examples, input_grad.shape[1:])
for (scaled_grad, input_grad) in zip(scaled_grads, input_grads)
)
if self.multiplies_by_inputs:
# computes attribution for each tensor in input tuple
# attributions has the same dimensionality as inputs
attributions = tuple(
total_grad * (input - baseline)
for total_grad, input, baseline in zip(total_grads, inputs, baselines)
)
else:
attributions = total_grads
return attributions
@property
def multiplies_by_inputs(self):
return self._multiply_by_inputs
```