"""File with Conductance algorithm explainer classes.
Based on https://github.com/pytorch/captum/blob/master/captum/attr/_core/layer/layer_conductance.py.
"""
from typing import Any, Optional, Union
import torch
from captum._utils.typing import TargetType
from captum.attr import LayerConductance
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
[docs]class LayerConductanceCVExplainer(Explainer):
"""Layer Conductance algorithm explainer."""
# pylint: disable = unused-argument
[docs] def create_explainer(
self,
model: torch.nn.Module,
layer: torch.nn.Module,
) -> LayerConductance:
"""Create explainer object.
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.
Returns:
Explainer object.
"""
conductance = LayerConductance(forward_func=model, layer=layer)
return conductance
[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,
n_steps: int = 50,
method: str = "gausslegendre",
internal_batch_size: Union[None, int] = None,
attribute_to_layer_input: bool = False,
**kwargs,
) -> torch.Tensor:
"""Generate model's attributes with Layer Conductance algorithm explainer.
Args:
model: The forward function of the model or any
modification of it.
input_data: Input for which layer
conductance is 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 the starting point from which integral
is computed and 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.
For a tensor, the first dimension of the tensor must
correspond to the number of examples. It will be repeated
for each of `n_steps` along the integrated path.
For all other types, the given argument is used for
all forward evaluations.
Note that attributions are not computed with respect
to these arguments.
Default: None
n_steps: The number of steps used by the approximation
method. Default: 50.
method: Method for approximating the integral,
one of `riemann_right`, `riemann_left`, `riemann_middle`,
`riemann_trapezoid` or `gausslegendre`.
Default: `gausslegendre` if no method is provided.
internal_batch_size: Divides total #steps * #examples
data points into chunks of size at most internal_batch_size,
which are computed (forward / backward passes)
sequentially. internal_batch_size must be at least equal to
2 * #examples.
For DataParallel models, each batch is split among the
available devices, so evaluations on each available
device contain internal_batch_size / num_devices examples.
If internal_batch_size is None, then all evaluations are
processed in one batch.
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 inputs, otherwise it will be computed with respect
to layer outputs.
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:
Conductance of each neuron in given layer input or
output. Attributions will always be the same size as
the input or output of the given layer, depending on
whether we attribute to the inputs or outputs
of the layer which is decided by the input flag
`attribute_to_layer_input`.
Attributions are returned in a tuple if
the layer inputs / outputs contain multiple tensors,
otherwise a single tensor is returned.
Raises:
ValueError: if model does not contain conv layers.
RuntimeError: if attribution has shape (0)
"""
layer: Optional[torch.nn.Module] = kwargs.get("layer", None)
if layer is None:
layer = get_last_conv_model_layer(model=model)
conductance = self.create_explainer(model=model, layer=layer)
attributions = conductance.attribute(
input_data,
baselines=baselines,
target=pred_label_idx,
additional_forward_args=additional_forward_args,
n_steps=n_steps,
method=method,
internal_batch_size=internal_batch_size,
return_convergence_delta=False,
attribute_to_layer_input=attribute_to_layer_input,
)
validate_result(attributions=attributions)
return attributions