#!/usr/bin/env python3
# pyre-strict
from typing import Callable, Optional
import torch
from captum._utils.common import _format_output, _format_tensor_into_tuples, _is_tuple
from captum._utils.gradient import (
apply_gradient_requirements,
undo_gradient_requirements,
)
from captum._utils.typing import TargetType, TensorOrTupleOfTensorsGeneric
from captum.attr._utils.attribution import GradientAttribution
from captum.log import log_usage
from torch import Tensor
[docs]
class Saliency(GradientAttribution):
r"""
A baseline approach for computing input attribution. It returns
the gradients with respect to inputs. If `abs` is set to True, which is
the default, the absolute value of the gradients is returned.
More details about the approach can be found in the following paper:
https://arxiv.org/abs/1312.6034
"""
def __init__(self, forward_func: Callable[..., Tensor]) -> None:
r"""
Args:
forward_func (Callable): The forward function of the model or
any modification of it.
"""
GradientAttribution.__init__(self, forward_func)
[docs]
@log_usage()
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
target: TargetType = None,
abs: bool = True,
additional_forward_args: Optional[object] = None,
) -> TensorOrTupleOfTensorsGeneric:
r"""
Args:
inputs (Tensor or tuple[Tensor, ...]): Input for which saliency
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 (aka batch size), 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
abs (bool, optional): Returns absolute value of gradients if set
to True, otherwise returns the (signed) gradients if
False.
Default: True
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
Returns:
*Tensor* or *tuple[Tensor, ...]* of **attributions**:
- **attributions** (*Tensor* or *tuple[Tensor, ...]*):
The gradients 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.
>>> net = ImageClassifier()
>>> # Generating random input with size 2x3x3x32
>>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
>>> # Defining Saliency interpreter
>>> saliency = Saliency(net)
>>> # Computes saliency maps for class 3.
>>> attribution = saliency.attribute(input, target=3)
"""
# Keeps track whether original input is a tuple or not before
# converting it into a tuple.
is_inputs_tuple = _is_tuple(inputs)
inputs_tuple = _format_tensor_into_tuples(inputs)
gradient_mask = apply_gradient_requirements(inputs_tuple)
# No need to format additional_forward_args here.
# They are being formated in the `_run_forward` function in `common.py`
gradients = self.gradient_func(
self.forward_func, inputs_tuple, target, additional_forward_args
)
if abs:
attributions = tuple(torch.abs(gradient) for gradient in gradients)
else:
attributions = gradients
undo_gradient_requirements(inputs_tuple, gradient_mask)
# pyre-fixme[7]: Expected `TensorOrTupleOfTensorsGeneric` but got
# `Tuple[Tensor, ...]`.
return _format_output(is_inputs_tuple, attributions)
# pyre-fixme[24] Generic type `Callable` expects 2 type parameters.
[docs]
def attribute_future(self) -> Callable:
r"""
This method is not implemented for Saliency.
"""
raise NotImplementedError("attribute_future is not implemented for Saliency")
