# Source code for captum.attr._core.guided_backprop_deconvnet

```
#!/usr/bin/env python3
import warnings
from typing import Any, List, Tuple, Union
import torch
import torch.nn.functional as F
from captum._utils.common import (
_format_output,
_format_tensor_into_tuples,
_is_tuple,
_register_backward_hook,
)
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
from torch.nn import Module
from torch.utils.hooks import RemovableHandle
class ModifiedReluGradientAttribution(GradientAttribution):
def __init__(self, model: Module, use_relu_grad_output: bool = False) -> None:
r"""
Args:
model (nn.Module): The reference to PyTorch model instance.
"""
GradientAttribution.__init__(self, model)
self.model = model
self.backward_hooks: List[RemovableHandle] = []
self.use_relu_grad_output = use_relu_grad_output
assert isinstance(self.model, torch.nn.Module), (
"Given model must be an instance of torch.nn.Module to properly hook"
" ReLU layers."
)
@log_usage()
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
target: TargetType = None,
additional_forward_args: Any = None,
) -> TensorOrTupleOfTensorsGeneric:
r"""
Computes attribution by overriding relu gradients. Based on constructor
flag use_relu_grad_output, performs either GuidedBackpropagation if False
and Deconvolution if True. This class is the parent class of both these
methods, more information on usage can be found in the docstrings for each
implementing class.
"""
# Keeps track whether original input is a tuple or not before
# converting it into a tuple.
is_inputs_tuple = _is_tuple(inputs)
inputs = _format_tensor_into_tuples(inputs)
gradient_mask = apply_gradient_requirements(inputs)
# set hooks for overriding ReLU gradients
warnings.warn(
"Setting backward hooks on ReLU activations."
"The hooks will be removed after the attribution is finished"
)
try:
self.model.apply(self._register_hooks)
gradients = self.gradient_func(
self.forward_func, inputs, target, additional_forward_args
)
finally:
self._remove_hooks()
undo_gradient_requirements(inputs, gradient_mask)
return _format_output(is_inputs_tuple, gradients)
def _register_hooks(self, module: Module):
if isinstance(module, torch.nn.ReLU):
hooks = _register_backward_hook(module, self._backward_hook, self)
self.backward_hooks.extend(hooks)
def _backward_hook(
self,
module: Module,
grad_input: Union[Tensor, Tuple[Tensor, ...]],
grad_output: Union[Tensor, Tuple[Tensor, ...]],
):
to_override_grads = grad_output if self.use_relu_grad_output else grad_input
if isinstance(to_override_grads, tuple):
return tuple(
F.relu(to_override_grad) for to_override_grad in to_override_grads
)
else:
return F.relu(to_override_grads)
def _remove_hooks(self):
for hook in self.backward_hooks:
hook.remove()
[docs]
class GuidedBackprop(ModifiedReluGradientAttribution):
r"""
Computes attribution using guided backpropagation. Guided backpropagation
computes the gradient of the target output with respect to the input,
but gradients of ReLU functions are overridden so that only
non-negative gradients are backpropagated.
More details regarding the guided backpropagation algorithm can be found
in the original paper here:
https://arxiv.org/abs/1412.6806
Warning: Ensure that all ReLU operations in the forward function of the
given model are performed using a module (nn.module.ReLU).
If nn.functional.ReLU is used, gradients are not overridden appropriately.
"""
def __init__(self, model: Module) -> None:
r"""
Args:
model (nn.Module): The reference to PyTorch model instance.
"""
ModifiedReluGradientAttribution.__init__(
self, model, use_relu_grad_output=False
)
[docs]
@log_usage()
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
target: TargetType = None,
additional_forward_args: Any = None,
) -> TensorOrTupleOfTensorsGeneric:
r"""
Args:
inputs (Tensor or tuple[Tensor, ...]): Input for which
attributions are computed. If model takes a single
tensor as input, a single input tensor should be provided.
If model 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
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
model 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 guided backprop 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()
>>> gbp = GuidedBackprop(net)
>>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
>>> # Computes Guided Backprop attribution scores for class 3.
>>> attribution = gbp.attribute(input, target=3)
"""
return super().attribute.__wrapped__(
self, inputs, target, additional_forward_args
)
[docs]
class Deconvolution(ModifiedReluGradientAttribution):
r"""
Computes attribution using deconvolution. Deconvolution
computes the gradient of the target output with respect to the input,
but gradients of ReLU functions are overridden so that the gradient
of the ReLU input is simply computed taking ReLU of the output gradient,
essentially only propagating non-negative gradients (without
dependence on the sign of the ReLU input).
More details regarding the deconvolution algorithm can be found
in these papers:
https://arxiv.org/abs/1311.2901
https://link.springer.com/chapter/10.1007/978-3-319-46466-4_8
Warning: Ensure that all ReLU operations in the forward function of the
given model are performed using a module (nn.module.ReLU).
If nn.functional.ReLU is used, gradients are not overridden appropriately.
"""
def __init__(self, model: Module) -> None:
r"""
Args:
model (nn.Module): The reference to PyTorch model instance.
"""
ModifiedReluGradientAttribution.__init__(self, model, use_relu_grad_output=True)
[docs]
@log_usage()
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
target: TargetType = None,
additional_forward_args: Any = None,
) -> TensorOrTupleOfTensorsGeneric:
r"""
Args:
inputs (Tensor or tuple[Tensor, ...]): Input for which
attributions are computed. If model takes a single
tensor as input, a single input tensor should be provided.
If model 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
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
model 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 deconvolution attributions 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()
>>> deconv = Deconvolution(net)
>>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
>>> # Computes Deconvolution attribution scores for class 3.
>>> attribution = deconv.attribute(input, target=3)
"""
return super().attribute.__wrapped__(
self, inputs, target, additional_forward_args
)
```