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

#!/usr/bin/env python3
from typing import Any, Callable, List, Tuple, Union

import torch
from captum._utils.common import _verify_select_neuron
from captum._utils.typing import BaselineType, TensorOrTupleOfTensorsGeneric
from captum.attr._core.feature_ablation import FeatureAblation
from captum.log import log_usage
from torch.nn import Module

r"""
A perturbation based approach to computing neuron attribution,
involving replacing each input feature with a given baseline /
reference, and computing the difference in the neuron's input / output.
By default, each scalar value within
each input tensor is taken as a feature and replaced independently. Passing
a feature mask, allows grouping features to be ablated together. This can
be used in cases such as images, where an entire segment or region
can be ablated, measuring the importance of the segment (feature group).
Each input scalar in the group will be given the same attribution value
equal to the change in target as a result of ablating the entire feature
group.
"""

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.
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, 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.
"""

[docs]    @log_usage()
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
neuron_selector: Union[int, Tuple[Union[int, slice], ...], Callable],
baselines: BaselineType = None,
attribute_to_neuron_input: bool = False,
perturbations_per_eval: int = 1,
) -> TensorOrTupleOfTensorsGeneric:
r"""
Args:

inputs (Tensor or tuple[Tensor, ...]): Input for which neuron
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.
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 or slice objects. 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).
The elements of the tuple can be either integers or
slice objects (slice object allows indexing a
range of neurons rather individual ones).

If any of the tuple elements is a slice object, the
indexed output tensor is used for attribution. Note
that specifying a slice of a tensor would amount to
computing the attribution of the sum of the specified
neurons, and not the individual neurons independently.

- a callable, which should
take the target layer as input (single tensor or tuple
if multiple tensors are in layer) and return a neuron or
aggregate of the layer's neurons for attribution.
For example, this function could return the
sum of the neurons in the layer or sum of neurons with
activations in a particular range. It is expected that
this function returns either a tensor with one element
or a 1D tensor with length equal to batch_size (one scalar
per input example)

baselines (scalar, Tensor, tuple of scalar, or Tensor, optional):
Baselines define reference value which replaces each
feature when ablated.
Baselines can be provided as:

- a single tensor, if inputs is a single tensor, with
exactly the same dimensions as inputs or
broadcastable to match the dimensions of 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
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
feature_mask (Tensor or tuple[Tensor, ...], optional):
features which should be ablated together. feature_mask
should contain the same number of tensors as inputs.
Each tensor should
be the same size as the corresponding input or
broadcastable to match the input tensor. Each tensor
should contain integers in the range 0 to num_features
- 1, and indices corresponding to the same feature should
have the same value.
Note that features within each input tensor are ablated
independently (not across tensors).
If None, then a feature mask is constructed which assigns
each scalar within a tensor as a separate feature, which
is ablated independently.
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 neurons, depending on whether we
attribute to the input or output, is a single tensor.
Support for multiple tensors will be added later.
Default: False
perturbations_per_eval (int, optional): Allows ablation of multiple
features to be processed simultaneously in one call to
forward_fn.
Each forward pass will contain a maximum of
perturbations_per_eval * #examples samples.
For DataParallel models, each batch is split among the
available devices, so evaluations on each available
device contain at most
(perturbations_per_eval * #examples) / num_devices
samples.
Default: 1

Returns:
*Tensor* or *tuple[Tensor, ...]* of **attributions**:
- **attributions** (*Tensor* or *tuple[Tensor, ...]*):
Attributions of 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::

>>> # SimpleClassifier takes a single input tensor of size Nx4x4,
>>> # and returns an Nx3 tensor of class probabilities.
>>> # It contains an attribute conv1, which is an instance of nn.conv2d,
>>> # and the output of this layer has dimensions Nx12x3x3.
>>> net = SimpleClassifier()
>>> # Generating random input with size 2 x 4 x 4
>>> input = torch.randn(2, 4, 4)
>>> # Defining NeuronFeatureAblation interpreter
>>> ablator = NeuronFeatureAblation(net, net.conv1)
>>> # To compute neuron attribution, we need to provide the neuron
>>> # index for which attribution is desired. Since the layer output
>>> # is Nx12x3x3, we need a tuple in the form (0..11,0..2,0..2)
>>> # which indexes a particular neuron in the layer output.
>>> # For this example, we choose the index (4,1,2).
>>> # Computes neuron gradient for neuron with
>>> # index (4,1,2).
>>> # Computes ablation attribution, ablating each of the 16
>>> # scalar inputs independently.
>>> attr = ablator.attribute(input, neuron_selector=(4,1,2))

>>> # Alternatively, we may want to ablate features in groups, e.g.
>>> # grouping each 2x2 square of the inputs and ablating them together.
>>> # This can be done by creating a feature mask as follows, which
>>> # defines the feature groups, e.g.:
>>> # +---+---+---+---+
>>> # | 0 | 0 | 1 | 1 |
>>> # +---+---+---+---+
>>> # | 0 | 0 | 1 | 1 |
>>> # +---+---+---+---+
>>> # | 2 | 2 | 3 | 3 |
>>> # +---+---+---+---+
>>> # | 2 | 2 | 3 | 3 |
>>> # +---+---+---+---+
>>> # With this mask, all inputs with the same value are ablated
>>> # simultaneously, and the attribution for each input in the same
>>> # group (0, 1, 2, and 3) per example are the same.
>>> # The attributions can be calculated as follows:
>>> # feature mask has dimensions 1 x 4 x 4
>>>                             [2,2,3,3],[2,2,3,3]]])
>>> attr = ablator.attribute(input, neuron_selector=(4,1,2),
"""

def neuron_forward_func(*args: Any):
layer_eval = _forward_layer_eval(
self.forward_func,
args,
self.layer,
device_ids=self.device_ids,
attribute_to_layer_input=attribute_to_neuron_input,
)
return _verify_select_neuron(layer_eval, neuron_selector)

ablator = FeatureAblation(neuron_forward_func)

# NOTE: using __wrapped__ to not log
return ablator.attribute.__wrapped__(
ablator,  # self
inputs,
baselines=baselines,