"""File with DeepLIFT algorithm explainer classes.
Based on https://github.com/pytorch/captum/blob/master/captum/attr/_core/deep_lift.py
and https://github.com/pytorch/captum/blob/master/captum/attr/_core/layer/layer_deep_lift.py.
"""
from abc import abstractmethod
from typing import Any, Callable, Optional, Tuple, Union
import torch
from captum._utils.typing import TargetType
from captum.attr import DeepLift, LayerDeepLift
from foxai.array_utils import validate_result
from foxai.explainer.base_explainer import Explainer
from foxai.explainer.computer_vision.model_utils import (
get_last_conv_model_layer,
modify_modules,
)
[docs]class BaseDeepLIFTCVExplainer(Explainer):
"""Base DeepLIFT algorithm explainer."""
[docs] @abstractmethod
def create_explainer(
self,
model: torch.nn.Module,
multiply_by_inputs: bool = True,
**kwargs,
) -> Union[DeepLift, LayerDeepLift]:
"""Create explainer object.
Args:
model: The forward function of the model or any
modification of it.
multiply_by_inputs: Indicates whether to factor
model inputs' multiplier in the final attribution scores.
In the literature this is also known as local vs global
attribution. If inputs' multiplier isn't factored in
then that type of attribution method is also called local
attribution. If it is, then that type of attribution
method is called global.
More detailed can be found here:
https://arxiv.org/abs/1711.06104
In case of DeepLift, if `multiply_by_inputs`
is set to True, final sensitivity scores
are being multiplied by (inputs - baselines).
This flag applies only if `custom_attribution_func` is
set to None.
Returns:
Explainer object.
"""
[docs] def calculate_features(
self,
model: torch.nn.Module,
input_data: torch.Tensor,
pred_label_idx: TargetType = None,
baselines: Union[None, int, float, torch.Tensor] = None,
additional_forward_args: Any = None,
custom_attribution_func: Union[
None, Callable[..., Tuple[torch.Tensor, ...]]
] = None,
attribute_to_layer_input: bool = False,
**kwargs,
) -> torch.Tensor:
"""Generate model's attributes with DeepLIFT algorithm explainer.
Args:
model: The forward function of the model or any
modification of it.
input_data: Input for which
attributions are computed. If forward_func takes a single
tensor as input, a single input tensor should be provided.
pred_label_idx: 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
baselines: Baselines define reference samples that are compared with
the inputs. In order to assign attribution scores DeepLift
computes the differences between the inputs/outputs and
corresponding references.
Baselines can be provided as:
- a single tensor, if inputs is a single tensor, with
exactly the same dimensions as inputs or the first
dimension is one and the remaining dimensions match
with inputs.
- a single scalar, if inputs is a single tensor, which will
be broadcasted for each input value in 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: 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
custom_attribution_func: A custom function for
computing final attribution scores. This function can take
at least one and at most three arguments with the
following signature:
- custom_attribution_func(multipliers)
- custom_attribution_func(multipliers, inputs)
- custom_attribution_func(multipliers, inputs, baselines)
In case this function is not provided, we use the default
logic defined as: multipliers * (inputs - baselines)
It is assumed that all input arguments, `multipliers`,
`inputs` and `baselines` are provided in tuples of same
length. `custom_attribution_func` returns a tuple of
attribution tensors that have the same length as the
`inputs`.
Default: None
attribute_to_layer_input: 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:
Attribution score computed based on DeepLift rescale rule 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.
Raises:
RuntimeError: if attribution has shape (0).
"""
layer: Optional[torch.nn.Module] = kwargs.get("layer", None)
deeplift = self.create_explainer(model=model, layer=layer)
if baselines is None:
baselines = torch.randn(
input_data.shape,
requires_grad=True,
device=input_data.device,
)
if isinstance(deeplift, LayerDeepLift):
attributions = deeplift.attribute(
input_data,
target=pred_label_idx,
baselines=baselines,
return_convergence_delta=False,
additional_forward_args=additional_forward_args,
custom_attribution_func=custom_attribution_func,
attribute_to_layer_input=attribute_to_layer_input,
)
else:
attributions = deeplift.attribute(
input_data,
target=pred_label_idx,
baselines=baselines,
return_convergence_delta=False,
additional_forward_args=additional_forward_args,
custom_attribution_func=custom_attribution_func,
)
validate_result(attributions=attributions)
return attributions
[docs]class DeepLIFTCVExplainer(BaseDeepLIFTCVExplainer):
"""DeepLIFTC algorithm explainer."""
[docs] def create_explainer(
self,
model: torch.nn.Module,
multiply_by_inputs: bool = True,
eps: float = 1e-10,
**kwargs,
) -> Union[DeepLift, LayerDeepLift]:
"""Create explainer object.
Args:
model: The forward function of the model or any
modification of it.
multiply_by_inputs: Indicates whether to factor
model inputs' multiplier in the final attribution scores.
In the literature this is also known as local vs global
attribution. If inputs' multiplier isn't factored in
then that type of attribution method is also called local
attribution. If it is, then that type of attribution
method is called global.
More detailed can be found here:
https://arxiv.org/abs/1711.06104
In case of DeepLift, if `multiply_by_inputs`
is set to True, final sensitivity scores
are being multiplied by (inputs - baselines).
This flag applies only if `custom_attribution_func` is
set to None.
eps: A value at which to consider output/input change
significant when computing the gradients for non-linear layers.
This is useful to adjust, depending on your model's bit depth,
to avoid numerical issues during the gradient computation.
Default: 1e-10
Returns:
Explainer object.
"""
model = modify_modules(model)
return DeepLift(
model=model,
multiply_by_inputs=multiply_by_inputs,
eps=eps,
)
[docs]class LayerDeepLIFTCVExplainer(BaseDeepLIFTCVExplainer):
"""Layer DeepLIFT algorithm explainer."""
[docs] def create_explainer(
self,
model: torch.nn.Module,
multiply_by_inputs: bool = True,
layer: Optional[torch.nn.Module] = None,
**kwargs,
) -> Union[DeepLift, LayerDeepLift]:
"""Create explainer object.
Uses parameter `layer` from `kwargs`. If not provided function will call
`get_last_conv_model_layer` function to obtain last `torch.nn.Conv2d` layer
from provided model.
Args:
model: The forward function of the model or any
modification of it.
layer: Layer 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.
Default: None
multiply_by_inputs: Indicates whether to factor
model inputs' multiplier in the final attribution scores.
In the literature this is also known as local vs global
attribution. If inputs' multiplier isn't factored in
then that type of attribution method is also called local
attribution. If it is, then that type of attribution
method is called global.
More detailed can be found here:
https://arxiv.org/abs/1711.06104
In case of DeepLift, if `multiply_by_inputs`
is set to True, final sensitivity scores
are being multiplied by (inputs - baselines).
This flag applies only if `custom_attribution_func` is
set to None.
Returns:
Explainer object.
Raises:
ValueError: if model does not contain conv layers.
"""
if layer is None:
layer = get_last_conv_model_layer(model=model)
model = modify_modules(model)
return LayerDeepLift(
model=model,
layer=layer,
multiply_by_inputs=multiply_by_inputs,
)