#!/usr/bin/env python3
from typing import Any, Callable, List, Tuple, Union
import torch
from captum._utils.common import _format_output
from captum._utils.gradient import _forward_layer_eval
from captum._utils.typing import ModuleOrModuleList
from captum.attr._utils.attribution import LayerAttribution
from captum.log import log_usage
from torch import Tensor
from torch.nn import Module
[docs]
class LayerActivation(LayerAttribution):
r"""
Computes activation of selected layer for given input.
"""
def __init__(
self,
forward_func: Callable,
layer: ModuleOrModuleList,
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 or list of torch.nn.Module): Layer or layers
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. If multiple layers are provided, attributions
are returned as a list, each element corresponding to the
activations of the corresponding 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)
[docs]
@log_usage()
def attribute(
self,
inputs: Union[Tensor, Tuple[Tensor, ...]],
additional_forward_args: Any = None,
attribute_to_layer_input: bool = False,
) -> Union[Tensor, Tuple[Tensor, ...], List[Union[Tensor, Tuple[Tensor, ...]]]]:
r"""
Args:
inputs (Tensor or tuple[Tensor, ...]): 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 (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 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:
*Tensor* or *tuple[Tensor, ...]* or list of **attributions**:
- **attributions** (*Tensor*, *tuple[Tensor, ...]*, or *list*):
Activation of each neuron in given layer output.
Attributions will always be the same size as the
output of the given layer.
Attributions are returned in a tuple if
the layer inputs / outputs contain multiple tensors,
otherwise a single tensor is returned.
If multiple layers are provided, attributions
are returned as a list, each element corresponding to the
activations of the corresponding layer.
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_act.attribute(input)
"""
with torch.no_grad():
layer_eval = _forward_layer_eval(
self.forward_func,
inputs,
self.layer,
additional_forward_args,
device_ids=self.device_ids,
attribute_to_layer_input=attribute_to_layer_input,
)
if isinstance(self.layer, Module):
return _format_output(len(layer_eval) > 1, layer_eval)
else:
return [
_format_output(len(single_layer_eval) > 1, single_layer_eval)
for single_layer_eval in layer_eval
]
@property
def multiplies_by_inputs(self):
return True
