#!/usr/bin/env python3
import typing
from collections import defaultdict
from typing import Any, cast, List, Tuple, Union
import torch.nn as nn
from captum._utils.common import (
_format_output,
_format_tensor_into_tuples,
_is_tuple,
_register_backward_hook,
_run_forward,
)
from captum._utils.gradient import (
apply_gradient_requirements,
undo_gradient_requirements,
)
from captum._utils.typing import Literal, TargetType, TensorOrTupleOfTensorsGeneric
from captum.attr._utils.attribution import GradientAttribution
from captum.attr._utils.common import _sum_rows
from captum.attr._utils.custom_modules import Addition_Module
from captum.attr._utils.lrp_rules import EpsilonRule, PropagationRule
from captum.log import log_usage
from torch import Tensor
from torch.nn import Module
from torch.utils.hooks import RemovableHandle
class LRP(GradientAttribution):
r"""
Layer-wise relevance propagation is based on a backward propagation
mechanism applied sequentially to all layers of the model. Here, the
model output score represents the initial relevance which is decomposed
into values for each neuron of the underlying layers. The decomposition
is defined by rules that are chosen for each layer, involving its weights
and activations. Details on the model can be found in the original paper
[https://doi.org/10.1371/journal.pone.0130140]. The implementation is
inspired by the tutorial of the same group
[https://doi.org/10.1016/j.dsp.2017.10.011] and the publication by
Ancona et al. [https://openreview.net/forum?id=Sy21R9JAW].
"""
def __init__(self, model: Module) -> None:
r"""
Args:
model (module): The forward function of the model or any modification of
it. Custom rules for a given layer need to be defined as attribute
`module.rule` and need to be of type PropagationRule. If no rule is
specified for a layer, a pre-defined default rule for the module type
is used. Model cannot contain any in-place nonlinear submodules;
these are not supported by the register_full_backward_hook
PyTorch API starting from PyTorch v1.9.
"""
GradientAttribution.__init__(self, model)
self.model = model
self._check_rules()
@property
def multiplies_by_inputs(self) -> bool:
return True
@typing.overload
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
target: TargetType = None,
additional_forward_args: Any = None,
return_convergence_delta: Literal[False] = False,
verbose: bool = False,
) -> TensorOrTupleOfTensorsGeneric:
...
@typing.overload
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
target: TargetType = None,
additional_forward_args: Any = None,
*,
return_convergence_delta: Literal[True],
verbose: bool = False,
) -> Tuple[TensorOrTupleOfTensorsGeneric, Tensor]:
...
@log_usage()
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
target: TargetType = None,
additional_forward_args: Any = None,
return_convergence_delta: bool = False,
verbose: bool = False,
) -> Union[
TensorOrTupleOfTensorsGeneric, Tuple[TensorOrTupleOfTensorsGeneric, Tensor]
]:
r"""
Args:
inputs (tensor or tuple of tensors): Input for which relevance is
propagated. 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.
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 (tuple, 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
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
verbose (bool, optional): Indicates whether information on application
of rules is printed during propagation.
Returns:
*tensor* or tuple of *tensors* of **attributions**
or 2-element tuple of **attributions**, **delta**::
- **attributions** (*tensor* or tuple of *tensors*):
The propagated relevance values with respect to each
input feature. The values are normalized by the output score
value (sum(relevance)=1). To obtain values comparable to other
methods or implementations these values need to be multiplied
by the output score. 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. The sum of attributions
is one and not corresponding to the prediction score as in other
implementations.
- **delta** (*tensor*, returned if return_convergence_delta=True):
Delta is calculated per example, meaning that the number of
elements in returned delta tensor is equal to the number of
of examples in the inputs.
Examples::
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
>>> # and returns an Nx10 tensor of class probabilities. It has one
>>> # Conv2D and a ReLU layer.
>>> net = ImageClassifier()
>>> lrp = LRP(net)
>>> input = torch.randn(3, 3, 32, 32)
>>> # Attribution size matches input size: 3x3x32x32
>>> attribution = lrp.attribute(input, target=5)
"""
self.verbose = verbose
self._original_state_dict = self.model.state_dict()
self.layers: List[Module] = []
self._get_layers(self.model)
self._check_and_attach_rules()
self.backward_handles: List[RemovableHandle] = []
self.forward_handles: List[RemovableHandle] = []
is_inputs_tuple = _is_tuple(inputs)
inputs = _format_tensor_into_tuples(inputs)
gradient_mask = apply_gradient_requirements(inputs)
try:
# 1. Forward pass: Change weights of layers according to selected rules.
output = self._compute_output_and_change_weights(
inputs, target, additional_forward_args
)
# 2. Forward pass + backward pass: Register hooks to configure relevance
# propagation and execute back-propagation.
self._register_forward_hooks()
normalized_relevances = self.gradient_func(
self._forward_fn_wrapper, inputs, target, additional_forward_args
)
relevances = tuple(
normalized_relevance
* output.reshape((-1,) + (1,) * (normalized_relevance.dim() - 1))
for normalized_relevance in normalized_relevances
)
finally:
self._restore_model()
undo_gradient_requirements(inputs, gradient_mask)
if return_convergence_delta:
return (
_format_output(is_inputs_tuple, relevances),
self.compute_convergence_delta(relevances, output),
)
else:
return _format_output(is_inputs_tuple, relevances) # type: ignore
def has_convergence_delta(self) -> bool:
return True
def compute_convergence_delta(
self, attributions: Union[Tensor, Tuple[Tensor, ...]], output: Tensor
) -> Tensor:
"""
Here, we use the completeness property of LRP: The relevance is conserved
during the propagation through the models' layers. Therefore, the difference
between the sum of attribution (relevance) values and model output is taken as
the convergence delta. It should be zero for functional attribution. However,
when rules with an epsilon value are used for stability reasons, relevance is
absorbed during propagation and the convergence delta is non-zero.
Args:
attributions (tensor or tuple of tensors): Attribution scores that
are precomputed by an attribution algorithm.
Attributions can be provided in form of a single tensor
or a tuple of those. It is assumed that attribution
tensor's dimension 0 corresponds to the number of
examples, and if multiple input tensors are provided,
the examples must be aligned appropriately.
output (tensor with single element): The output value with respect to which
the attribution values are computed. This value corresponds to
the target score of a classification model.
Returns:
*tensor*:
- **delta** Difference of relevance in output layer and input layer.
"""
if isinstance(attributions, tuple):
for attr in attributions:
summed_attr = cast(
Tensor, sum(_sum_rows(attr) for attr in attributions)
)
else:
summed_attr = _sum_rows(attributions)
return output.flatten() - summed_attr.flatten()
def _get_layers(self, model: Module) -> None:
for layer in model.children():
if len(list(layer.children())) == 0:
self.layers.append(layer)
else:
self._get_layers(layer)
def _check_and_attach_rules(self) -> None:
for layer in self.layers:
if hasattr(layer, "rule"):
layer.activations = {} # type: ignore
layer.rule.relevance_input = defaultdict(list) # type: ignore
layer.rule.relevance_output = {} # type: ignore
pass
elif type(layer) in SUPPORTED_LAYERS_WITH_RULES.keys():
layer.activations = {} # type: ignore
layer.rule = SUPPORTED_LAYERS_WITH_RULES[type(layer)]() # type: ignore
layer.rule.relevance_input = defaultdict(list) # type: ignore
layer.rule.relevance_output = {} # type: ignore
elif type(layer) in SUPPORTED_NON_LINEAR_LAYERS:
layer.rule = None # type: ignore
else:
raise TypeError(
(
f"Module of type {type(layer)} has no rule defined and no"
"default rule exists for this module type. Please, set a rule"
"explicitly for this module and assure that it is appropriate"
"for this type of layer."
)
)
def _check_rules(self) -> None:
for module in self.model.modules():
if hasattr(module, "rule"):
if (
not isinstance(module.rule, PropagationRule)
and module.rule is not None
):
raise TypeError(
(
f"Please select propagation rules inherited from class "
f"PropagationRule for module: {module}"
)
)
def _register_forward_hooks(self) -> None:
for layer in self.layers:
if type(layer) in SUPPORTED_NON_LINEAR_LAYERS:
backward_handle = _register_backward_hook(
layer, PropagationRule.backward_hook_activation, self
)
self.backward_handles.append(backward_handle)
else:
forward_handle = layer.register_forward_hook(
layer.rule.forward_hook # type: ignore
)
self.forward_handles.append(forward_handle)
if self.verbose:
print(f"Applied {layer.rule} on layer {layer}")
def _register_weight_hooks(self) -> None:
for layer in self.layers:
if layer.rule is not None:
forward_handle = layer.register_forward_hook(
layer.rule.forward_hook_weights # type: ignore
)
self.forward_handles.append(forward_handle)
def _register_pre_hooks(self) -> None:
for layer in self.layers:
if layer.rule is not None:
forward_handle = layer.register_forward_pre_hook(
layer.rule.forward_pre_hook_activations # type: ignore
)
self.forward_handles.append(forward_handle)
def _compute_output_and_change_weights(
self,
inputs: Tuple[Tensor, ...],
target: TargetType,
additional_forward_args: Any,
) -> Tensor:
try:
self._register_weight_hooks()
output = _run_forward(self.model, inputs, target, additional_forward_args)
finally:
self._remove_forward_hooks()
# Register pre_hooks that pass the initial activations from before weight
# adjustments as inputs to the layers with adjusted weights. This procedure
# is important for graph generation in the 2nd forward pass.
self._register_pre_hooks()
return output
def _remove_forward_hooks(self) -> None:
for forward_handle in self.forward_handles:
forward_handle.remove()
def _remove_backward_hooks(self) -> None:
for backward_handle in self.backward_handles:
backward_handle.remove()
for layer in self.layers:
if hasattr(layer.rule, "_handle_input_hooks"):
for handle in layer.rule._handle_input_hooks: # type: ignore
handle.remove()
if hasattr(layer.rule, "_handle_output_hook"):
layer.rule._handle_output_hook.remove() # type: ignore
def _remove_rules(self) -> None:
for layer in self.layers:
if hasattr(layer, "rule"):
del layer.rule
def _clear_properties(self) -> None:
for layer in self.layers:
if hasattr(layer, "activation"):
del layer.activation
def _restore_state(self) -> None:
self.model.load_state_dict(self._original_state_dict) # type: ignore
def _restore_model(self) -> None:
self._restore_state()
self._remove_backward_hooks()
self._remove_forward_hooks()
self._remove_rules()
self._clear_properties()
def _forward_fn_wrapper(self, *inputs: Tensor) -> Tensor:
"""
Wraps a forward function with addition of zero as a workaround to
https://github.com/pytorch/pytorch/issues/35802 discussed in
https://github.com/pytorch/captum/issues/143#issuecomment-611750044
#TODO: Remove when bugs are fixed
"""
adjusted_inputs = tuple(
input + 0 if input is not None else input for input in inputs
)
return self.model(*adjusted_inputs)
SUPPORTED_LAYERS_WITH_RULES = {
nn.MaxPool1d: EpsilonRule,
nn.MaxPool2d: EpsilonRule,
nn.MaxPool3d: EpsilonRule,
nn.Conv2d: EpsilonRule,
nn.AvgPool2d: EpsilonRule,
nn.AdaptiveAvgPool2d: EpsilonRule,
nn.Linear: EpsilonRule,
nn.BatchNorm2d: EpsilonRule,
Addition_Module: EpsilonRule,
}
SUPPORTED_NON_LINEAR_LAYERS = [nn.ReLU, nn.Dropout, nn.Tanh]