# Source code for captum.attr._core.layer.layer_conductance

```
#!/usr/bin/env python3
import typing
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,
)
from captum._utils.gradient import compute_layer_gradients_and_eval
from captum._utils.typing import BaselineType, Literal, TargetType
from captum.attr._utils.approximation_methods import approximation_parameters
from captum.attr._utils.attribution import GradientAttribution, LayerAttribution
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 LayerConductance(LayerAttribution, GradientAttribution):
r"""
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/abs/1807.09946
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.
"""
def __init__(
self,
forward_func: Callable,
layer: Module,
device_ids: Union[None, List[int]] = None,
) -> None:
r"""
Args:
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.
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.
"""
LayerAttribution.__init__(self, forward_func, layer, device_ids)
GradientAttribution.__init__(self, forward_func)
@typing.overload
def attribute(
self,
inputs: Union[Tensor, Tuple[Tensor, ...]],
baselines: BaselineType = None,
target: TargetType = None,
additional_forward_args: Any = None,
n_steps: int = 50,
method: str = "gausslegendre",
internal_batch_size: Union[None, int] = None,
*,
return_convergence_delta: Literal[True],
attribute_to_layer_input: bool = False,
) -> Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor]:
...
@typing.overload
def attribute(
self,
inputs: Union[Tensor, Tuple[Tensor, ...]],
baselines: BaselineType = None,
target: TargetType = None,
additional_forward_args: Any = None,
n_steps: int = 50,
method: str = "gausslegendre",
internal_batch_size: Union[None, int] = None,
return_convergence_delta: Literal[False] = False,
attribute_to_layer_input: bool = False,
) -> Union[Tensor, Tuple[Tensor, ...]]:
...
[docs]
@log_usage()
def attribute(
self,
inputs: Union[Tensor, Tuple[Tensor, ...]],
baselines: Union[
None, int, float, Tensor, Tuple[Union[int, float, Tensor], ...]
] = None,
target: TargetType = None,
additional_forward_args: Any = None,
n_steps: int = 50,
method: str = "gausslegendre",
internal_batch_size: Union[None, int] = None,
return_convergence_delta: bool = False,
attribute_to_layer_input: bool = False,
) -> Union[
Tensor, Tuple[Tensor, ...], Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor]
]:
r"""
Args:
inputs (Tensor or tuple[Tensor, ...]): 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 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
2 * #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
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** or 2-element tuple of **attributions**, **delta**:
- **attributions** (*Tensor* or *tuple[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`.
Attributions are returned in a tuple if
the layer inputs / outputs contain multiple tensors,
otherwise a single tensor is returned.
- **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
examples in inputs.
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)
"""
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 + 1,
include_endpoint=True,
inputs=inputs,
baselines=baselines,
target=target,
additional_forward_args=additional_forward_args,
method=method,
attribute_to_layer_input=attribute_to_layer_input,
)
else:
attrs = self._attribute(
inputs=inputs,
baselines=baselines,
target=target,
additional_forward_args=additional_forward_args,
n_steps=n_steps,
method=method,
attribute_to_layer_input=attribute_to_layer_input,
)
is_layer_tuple = isinstance(attrs, tuple)
attributions = attrs if is_layer_tuple else (attrs,)
if return_convergence_delta:
start_point, end_point = baselines, inputs
delta = self.compute_convergence_delta(
attributions,
start_point,
end_point,
target=target,
additional_forward_args=additional_forward_args,
)
return _format_output(is_layer_tuple, attributions), delta
return _format_output(is_layer_tuple, attributions)
def _attribute(
self,
inputs: Tuple[Tensor, ...],
baselines: Tuple[Union[Tensor, int, float], ...],
target: TargetType = None,
additional_forward_args: Any = None,
n_steps: int = 50,
method: str = "gausslegendre",
attribute_to_layer_input: bool = False,
step_sizes_and_alphas: Union[None, Tuple[List[float], List[float]]] = None,
) -> Union[Tensor, Tuple[Tensor, ...]]:
num_examples = inputs[0].shape[0]
if step_sizes_and_alphas is None:
# Retrieve scaling factors for specified approximation method
step_sizes_func, alphas_func = approximation_parameters(method)
alphas = alphas_func(n_steps + 1)
else:
_, 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 + 1)
if additional_forward_args is not None
else None
)
expanded_target = _expand_target(target, n_steps + 1)
# Conductance Gradients - 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(
forward_fn=self.forward_func,
layer=self.layer,
inputs=scaled_features_tpl,
additional_forward_args=input_additional_args,
target_ind=expanded_target,
device_ids=self.device_ids,
attribute_to_layer_input=attribute_to_layer_input,
)
# Compute differences between consecutive evaluations of layer_eval.
# This approximates the total input gradient of each step multiplied
# by the step size.
grad_diffs = tuple(
layer_eval[num_examples:] - layer_eval[:-num_examples]
for layer_eval in layer_evals
)
# Element-wise multiply gradient of output with respect to hidden layer
# and summed gradients with respect to input (chain rule) and sum
# across stepped inputs.
attributions = tuple(
_reshape_and_sum(
grad_diff * layer_gradient[:-num_examples],
n_steps,
num_examples,
layer_eval.shape[1:],
)
for layer_gradient, layer_eval, grad_diff in zip(
layer_gradients, layer_evals, grad_diffs
)
)
return _format_output(len(attributions) > 1, attributions)
@property
def multiplies_by_inputs(self):
return True
```