def default_perturb_func(inputs: TensorOrTupleOfTensorsGeneric,
baselines: BaselineType = None):
r""""""
inputs_perturbed = (pertub_func(inputs, baselines) if baselines
is not None else pertub_func(inputs))
inputs_perturbed = _format_input(inputs_perturbed)
inputs = _format_input(inputs)
baselines = _format_baseline(baselines, inputs)
if baselines is None:
perturbations = tuple(
safe_div(
input - input_perturbed,
input,
torch.tensor(1.0, device=input.device),
) if multipy_by_inputs else input - input_perturbed for
input, input_perturbed in zip(inputs, inputs_perturbed))
else:
perturbations = tuple(
safe_div(
input - input_perturbed,
input - baseline,
torch.tensor(1.0, device=input.device),
) if multipy_by_inputs else input - input_perturbed
for input, input_perturbed, baseline in zip(
inputs, inputs_perturbed, baselines))
return perturbations, inputs_perturbed
def default_perturb_func(inputs: TensorOrTupleOfTensorsGeneric,
perturb_radius: float = 0.02) -> Tuple[Tensor, ...]:
r"""A default function for generating perturbations of `inputs`
within perturbation radius of `perturb_radius`.
This function samples uniformly random from the L_Infinity ball
with `perturb_radius` radius.
The users can override this function if they prefer to use a
different perturbation function.
Args:
inputs (tensor or a tuple of tensors): The input tensors that we'd
like to perturb by adding a random noise sampled unifromly
random from an L_infinity ball with a radius `perturb_radius`.
radius (float): A radius used for sampling from
an L_infinity ball.
Returns:
perturbed_input (tuple(tensor)): A list of perturbed inputs that
are createed by adding noise sampled uniformly random
from L_infiniy ball with a radius `perturb_radius` to the
original inputs.
"""
inputs = _format_input(inputs)
perturbed_input = tuple(
input + torch.FloatTensor(input.size()) # type: ignore
.uniform_(-perturb_radius, perturb_radius).to(input.device)
for input in inputs)
return perturbed_input
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_input(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 _format_callable_baseline(
baselines: Union[None, Callable[..., Union[Tensor, Tuple[Tensor, ...]]],
Tensor, int, float, Tuple[Union[Tensor, int, float],
...], ],
inputs: Union[Tensor, Tuple[Tensor, ...]],
) -> Tuple[Union[Tensor, int, float], ...]:
if callable(baselines):
# Note: this assumes that if baselines is a function and if it takes
# arguments, then the first argument is the `inputs`.
# This can be expanded in the future with better type checks
baseline_parameters = signature(baselines).parameters
if len(baseline_parameters) == 0:
baselines = baselines()
else:
baselines = baselines(inputs)
return _format_baseline(baselines, _format_input(inputs))
def _batched_generator(
inputs: TensorOrTupleOfTensorsGeneric,
additional_forward_args: Any = None,
target_ind: TargetType = None,
internal_batch_size: Union[None, int] = None,
) -> Iterator[Tuple[Tuple[Tensor, ...], Any, TargetType]]:
"""
Returns a generator which returns corresponding chunks of size internal_batch_size
for both inputs and additional_forward_args. If batch size is None,
generator only includes original inputs and additional args.
"""
assert internal_batch_size is None or (
isinstance(internal_batch_size, int)
and internal_batch_size > 0), "Batch size must be greater than 0."
inputs = _format_input(inputs)
additional_forward_args = _format_additional_forward_args(
additional_forward_args)
num_examples = inputs[0].shape[0]
# TODO Reconsider this check if _batched_generator is used for non gradient-based
# attribution algorithms
if not (inputs[0] * 1).requires_grad:
warnings.warn(
"""It looks like that the attribution for a gradient-based method is
computed in a `torch.no_grad` block or perhaps the inputs have no
requires_grad.""")
if internal_batch_size is None:
yield inputs, additional_forward_args, target_ind
else:
for current_total in range(0, num_examples, internal_batch_size):
with torch.autograd.set_grad_enabled(True):
inputs_splice = _tuple_splice_range(
inputs, current_total, current_total + internal_batch_size)
yield inputs_splice, _tuple_splice_range(
additional_forward_args,
current_total,
current_total + internal_batch_size,
), target_ind[current_total:current_total +
internal_batch_size] if isinstance(
target_ind, list) or (
isinstance(target_ind, torch.Tensor)
and target_ind.numel() > 1) else target_ind
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
target: TargetType = None,
) -> TensorOrTupleOfTensorsGeneric:
r"""
Args:
inputs (tensor or tuple of tensors): Input for which
attributions are 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, i.e., batch size, and if
multiple input tensors are provided, the examples must
be aligned appropriately.
target (int, tuple, tensor or list, optional):
"""
is_inputs_tuple = _is_tuple(inputs)
inputs = _format_input(inputs)
def attribute(self, inputs: Union[Tensor, Tuple[Tensor, ...]], target: TargetType = None,
additional_forward_args: Any = None, attribute_to_layer_input: bool = False,
relu_attributions: bool = False) -> Union[Tensor, Tuple[Tensor, ...]]:
inputs = _format_input(inputs)
additional_forward_args = _format_additional_forward_args(
additional_forward_args
)
gradient_mask = apply_gradient_requirements(inputs)
# Returns gradient of output with respect to
# hidden layer and hidden layer evaluated at each input.
layer_gradients, layer_evals = compute_layer_gradients_and_eval(
self.forward_func,
self.layer,
inputs,
target,
additional_forward_args,
device_ids=self.device_ids,
attribute_to_layer_input=attribute_to_layer_input,
)
undo_gradient_requirements(inputs, gradient_mask)
summed_grads = tuple(
torch.mean(
layer_grad,
dim=0,
keepdim=True,
)
for layer_grad in layer_gradients
)
scaled_acts = tuple(
torch.sum(summed_grad * layer_eval, dim=1, keepdim=True)
for summed_grad, layer_eval in zip(summed_grads, layer_evals)
)
if relu_attributions:
scaled_acts = tuple(F.relu(scaled_act) for scaled_act in scaled_acts)
return _format_output(len(scaled_acts) > 1, scaled_acts)
def perturb(
self,
inputs: TensorOrTupleOfTensorsGeneric,
epsilon: float,
target: Any,
additional_forward_args: Any = None,
targeted: bool = False,
) -> TensorOrTupleOfTensorsGeneric:
r"""
This method computes and returns the perturbed input for each input tensor.
It supports both targeted and non-targeted attacks.
Args:
inputs (tensor or tuple of tensors): Input for which adversarial
attack is computed. It can be provided as a single
tensor or a tuple of multiple tensors. If multiple
input tensors are provided, the batch sizes must be
aligned accross all tensors.
epsilon (float): Step size of perturbation.
target (any): True labels of inputs if non-targeted attack is
desired. Target class of inputs if targeted attack
is desired. Target will be passed to the loss function
to compute loss, so the type needs to match the
argument type of the loss function.
If using the default negative log as loss function,
labels should be of type int, tuple, tensor or list.
For general 2D outputs, labels 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 label for the corresponding example.
For outputs with > 2 dimensions, labels can be either:
- A single tuple, which contains #output_dims - 1
elements. This label 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
label for the corresponding example.
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. These arguments are provided to
forward_func in order following the arguments in inputs.
Default: None.
targeted (bool, optional): If attack should be targeted.
Default: False.
Returns:
- **perturbed inputs** (*tensor* or tuple of *tensors*):
Perturbed input for each
input tensor. The perturbed inputs have the same shape and
dimensionality as the inputs.
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.
"""
is_inputs_tuple = _is_tuple(inputs)
inputs: Tuple[Tensor, ...] = _format_input(inputs)
gradient_mask = apply_gradient_requirements(inputs)
def _forward_with_loss() -> Tensor:
additional_inputs = _format_additional_forward_args(additional_forward_args)
outputs = self.forward_func( # type: ignore
*(*inputs, *additional_inputs) # type: ignore
if additional_inputs is not None
else inputs
)
if self.loss_func is not None:
return self.loss_func(outputs, target)
else:
loss = -torch.log(outputs)
return _select_targets(loss, target)
grads = compute_gradients(_forward_with_loss, inputs)
undo_gradient_requirements(inputs, gradient_mask)
perturbed_inputs = self._perturb(inputs, grads, epsilon, targeted)
perturbed_inputs = tuple(
self.bound(perturbed_inputs[i]) for i in range(len(perturbed_inputs))
)
return _format_output(is_inputs_tuple, perturbed_inputs)
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_input(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 attribute( # type: ignore
self,
inputs: TensorOrTupleOfTensorsGeneric,
sliding_window_shapes: Union[Tuple[int, ...], Tuple[Tuple[int, ...], ...]],
strides: Union[
None, int, Tuple[int, ...], Tuple[Union[int, Tuple[int, ...]], ...]
] = None,
baselines: BaselineType = None,
target: TargetType = None,
additional_forward_args: Any = None,
perturbations_per_eval: int = 1,
show_progress: bool = False,
) -> TensorOrTupleOfTensorsGeneric:
r"""
Args:
inputs (tensor or tuple of tensors): Input for which occlusion
attributions are 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.
sliding_window_shapes (tuple or tuple of tuples): Shape of patch
(hyperrectangle) to occlude each input. For a single
input tensor, this must be a tuple of length equal to the
number of dimensions of the input tensor - 1, defining
the dimensions of the patch. If the input tensor is 1-d,
this should be an empty tuple. For multiple input tensors,
this must be a tuple containing one tuple for each input
tensor defining the dimensions of the patch for that
input tensor, as described for the single tensor case.
strides (int or tuple or tuple of ints or tuple of tuples, optional):
This defines the step by which the occlusion hyperrectangle
should be shifted by in each direction for each iteration.
For a single tensor input, this can be either a single
integer, which is used as the step size in each direction,
or a tuple of integers matching the number of dimensions
in the occlusion shape, defining the step size in the
corresponding dimension. For multiple tensor inputs, this
can be either a tuple of integers, one for each input
tensor (used for all dimensions of the corresponding
tensor), or a tuple of tuples, providing the stride per
dimension for each tensor.
To ensure that all inputs are covered by at least one
sliding window, the stride for any dimension must be
<= the corresponding sliding window dimension if the
sliding window dimension is less than the input
dimension.
If None is provided, a stride of 1 is used for each
dimension of each input tensor.
Default: None
baselines (scalar, tensor, tuple of scalars or tensors, optional):
Baselines define reference value which replaces each
feature when occluded.
Baselines can be provided as:
- a single tensor, if inputs is a single tensor, with
exactly the same dimensions as inputs or
broadcastable to match the dimensions of inputs
- a single scalar, if inputs is a single tensor, which will
be broadcasted for each input value in input tensor.
- a tuple of tensors or scalars, the baseline corresponding
to each tensor in the inputs' tuple can be:
- either a tensor with matching dimensions to
corresponding tensor in the inputs' tuple
or the first dimension is one and the remaining
dimensions match with the corresponding
input tensor.
- or a scalar, corresponding to a tensor in the
inputs' tuple. This scalar value is broadcasted
for corresponding input tensor.
In the cases when `baselines` is not provided, we internally
use zero scalar corresponding to each input tensor.
Default: None
target (int, tuple, tensor or list, optional): Output indices for
which difference is 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 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. 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
perturbations_per_eval (int, optional): Allows multiple occlusions
to be included in one batch (one call to forward_fn).
By default, perturbations_per_eval is 1, so each occlusion
is processed individually.
Each forward pass will contain a maximum of
perturbations_per_eval * #examples samples.
For DataParallel models, each batch is split among the
available devices, so evaluations on each available
device contain at most
(perturbations_per_eval * #examples) / num_devices
samples.
Default: 1
show_progress (bool, optional): Displays the progress of computation.
It will try to use tqdm if available for advanced features
(e.g. time estimation). Otherwise, it will fallback to
a simple output of progress.
Default: False
Returns:
*tensor* or tuple of *tensors* of **attributions**:
- **attributions** (*tensor* or tuple of *tensors*):
The 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::
>>> # SimpleClassifier takes a single input tensor of size Nx4x4,
>>> # and returns an Nx3 tensor of class probabilities.
>>> net = SimpleClassifier()
>>> # Generating random input with size 2 x 4 x 4
>>> input = torch.randn(2, 4, 4)
>>> # Defining Occlusion interpreter
>>> ablator = Occlusion(net)
>>> # Computes occlusion attribution, ablating each 3x3 patch,
>>> # shifting in each direction by the default of 1.
>>> attr = ablator.attribute(input, target=1, sliding_window_shapes=(3,3))
"""
formatted_inputs = _format_input(inputs)
# Formatting strides
strides = _format_and_verify_strides(strides, formatted_inputs)
# Formatting sliding window shapes
sliding_window_shapes = _format_and_verify_sliding_window_shapes(
sliding_window_shapes, formatted_inputs
)
# Construct tensors from sliding window shapes
sliding_window_tensors = tuple(
torch.ones(window_shape, device=formatted_inputs[i].device)
for i, window_shape in enumerate(sliding_window_shapes)
)
# Construct counts, defining number of steps to make of occlusion block in
# each dimension.
shift_counts = []
for i, inp in enumerate(formatted_inputs):
current_shape = np.subtract(inp.shape[1:], sliding_window_shapes[i])
# Verify sliding window doesn't exceed input dimensions.
assert (np.array(current_shape) >= 0).all(), (
"Sliding window dimensions {} cannot exceed input dimensions" "{}."
).format(sliding_window_shapes[i], tuple(inp.shape[1:]))
# Stride cannot be larger than sliding window for any dimension where
# the sliding window doesn't cover the entire input.
assert np.logical_or(
np.array(current_shape) == 0,
np.array(strides[i]) <= sliding_window_shapes[i],
).all(), (
"Stride dimension {} cannot be larger than sliding window "
"shape dimension {}."
).format(
strides[i], sliding_window_shapes[i]
)
shift_counts.append(
tuple(
np.add(np.ceil(np.divide(current_shape, strides[i])).astype(int), 1)
)
)
# Use ablation attribute method
return super().attribute.__wrapped__(
self,
inputs,
baselines=baselines,
target=target,
additional_forward_args=additional_forward_args,
perturbations_per_eval=perturbations_per_eval,
sliding_window_tensors=sliding_window_tensors,
shift_counts=tuple(shift_counts),
strides=strides,
show_progress=show_progress,
)
def attribute(
self,
inputs: Union[Tensor, Tuple[Tensor, ...]],
target: TargetType = None,
additional_forward_args: Any = None,
attribute_to_layer_input: bool = False,
relu_attributions: bool = False,
) -> Union[Tensor, Tuple[Tensor, ...]]:
r"""
Args:
inputs (tensor or tuple of tensors): Input for which attributions
are 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, 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 forward_func in order following the
arguments in inputs.
Note that attributions are not computed with respect
to these arguments.
Default: None
attribute_to_layer_input (bool, optional): Indicates whether to
compute the attributions 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 the
layer input, otherwise it will be computed with respect
to layer output.
Note that currently it is assumed that either the input
or the outputs of internal layers, depending on whether we
attribute to the input or output, are single tensors.
Support for multiple tensors will be added later.
Default: False
relu_attributions (bool, optional): Indicates whether to
apply a ReLU operation on the final attribution,
returning only non-negative attributions. Setting this
flag to True matches the original GradCAM algorithm,
otherwise, by default, both positive and negative
attributions are returned.
Default: False
Returns:
*tensor* or tuple of *tensors* of **attributions**:
- **attributions** (*tensor* or tuple of *tensors*):
Attributions based on GradCAM method.
Attributions will be the same size as the
output of the given layer, except for dimension 2,
which will be 1 due to summing over channels.
Attributions are returned in a tuple if
the layer inputs / outputs contain multiple tensors,
otherwise a single tensor is returned.
Examples::
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
>>> # and returns an Nx10 tensor of class probabilities.
>>> # It contains a layer conv4, which is an instance of nn.conv2d,
>>> # and the output of this layer has dimensions Nx50x8x8.
>>> # It is the last convolution layer, which is the recommended
>>> # use case for GradCAM.
>>> net = ImageClassifier()
>>> layer_gc = LayerGradCam(net, net.conv4)
>>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
>>> # Computes layer GradCAM for class 3.
>>> # attribution size matches layer output except for dimension
>>> # 1, so dimensions of attr would be Nx1x8x8.
>>> attr = layer_gc.attribute(input, 3)
>>> # GradCAM attributions are often upsampled and viewed as a
>>> # mask to the input, since the convolutional layer output
>>> # spatially matches the original input image.
>>> # This can be done with LayerAttribution's interpolate method.
>>> upsampled_attr = LayerAttribution.interpolate(attr, (32, 32))
"""
inputs = _format_input(inputs)
additional_forward_args = _format_additional_forward_args(
additional_forward_args)
# Returns gradient of output with respect to
# hidden layer and hidden layer evaluated at each input.
layer_gradients, layer_evals = compute_layer_gradients_and_eval(
self.forward_func,
self.layer,
inputs,
target,
additional_forward_args,
device_ids=self.device_ids,
attribute_to_layer_input=attribute_to_layer_input,
)
summed_grads = tuple(
torch.mean(
layer_grad,
dim=tuple(x for x in range(2, len(layer_grad.shape))),
keepdim=True,
) if len(layer_grad.shape) > 2 else layer_grad
for layer_grad in layer_gradients)
scaled_acts = tuple(
torch.sum(summed_grad * layer_eval, dim=1, keepdim=True)
for summed_grad, layer_eval in zip(summed_grads, layer_evals))
if relu_attributions:
scaled_acts = tuple(
F.relu(scaled_act) for scaled_act in scaled_acts)
return _format_output(len(scaled_acts) > 1, scaled_acts)
def attribute( # type: ignore
self,
inputs: TensorOrTupleOfTensorsGeneric,
baselines: BaselineType = None,
target: TargetType = None,
additional_forward_args: Any = None,
return_convergence_delta: bool = False,
custom_attribution_func: Union[None, Callable[..., Tuple[Tensor, ...]]] = None,
) -> Union[
TensorOrTupleOfTensorsGeneric, Tuple[TensorOrTupleOfTensorsGeneric, Tensor]
]:
r"""
Args:
inputs (tensor or tuple of tensors): Input for which
attributions are 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.
baselines (scalar, tensor, tuple of scalars or tensors, optional):
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.
- a tuple of tensors or scalars, the baseline corresponding
to each tensor in the inputs' tuple can be:
- either a tensor with matching dimensions to
corresponding tensor in the inputs' tuple
or the first dimension is one and the remaining
dimensions match with the corresponding
input tensor.
- or a scalar, corresponding to a tensor in the
inputs' tuple. This scalar value is broadcasted
for corresponding input tensor.
In the cases when `baselines` is not provided, we internally
use zero scalar corresponding to each input tensor.
Default: None
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
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
custom_attribution_func (callable, optional): 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
Returns:
**attributions** or 2-element tuple of **attributions**, **delta**:
- **attributions** (*tensor* or tuple of *tensors*):
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.
- **delta** (*tensor*, returned if return_convergence_delta=True):
This is computed using the property that
the total sum of forward_func(inputs) - forward_func(baselines)
must equal the total sum of the attributions computed
based on DeepLift's rescale rule.
Delta is calculated per example, meaning that the number of
elements in returned delta tensor is equal to the number of
of examples in input.
Note that the logic described for deltas is guaranteed when the
default logic for attribution computations is used, meaning that the
`custom_attribution_func=None`, otherwise it is not guaranteed and
depends on the specifics of the `custom_attribution_func`.
Examples::
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
>>> # and returns an Nx10 tensor of class probabilities.
>>> net = ImageClassifier()
>>> dl = DeepLift(net)
>>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
>>> # Computes deeplift attribution scores for class 3.
>>> attribution = dl.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 = _format_input(inputs)
baselines = _format_baseline(baselines, inputs)
gradient_mask = apply_gradient_requirements(inputs)
_validate_input(inputs, baselines)
# set hooks for baselines
warnings.warn(
"""Setting forward, backward hooks and attributes on non-linear
activations. The hooks and attributes will be removed
after the attribution is finished"""
)
baselines = _tensorize_baseline(inputs, baselines)
main_model_hooks = []
try:
main_model_hooks = self._hook_main_model()
self.model.apply(self._register_hooks)
additional_forward_args = _format_additional_forward_args(
additional_forward_args
)
expanded_target = _expand_target(
target, 2, expansion_type=ExpansionTypes.repeat
)
wrapped_forward_func = self._construct_forward_func(
self.model,
(inputs, baselines),
expanded_target,
additional_forward_args,
)
gradients = self.gradient_func(wrapped_forward_func, inputs)
if custom_attribution_func is None:
if self.multiplies_by_inputs:
attributions = tuple(
(input - baseline) * gradient
for input, baseline, gradient in zip(
inputs, baselines, gradients
)
)
else:
attributions = gradients
else:
attributions = _call_custom_attribution_func(
custom_attribution_func, gradients, inputs, baselines
)
finally:
# Even if any error is raised, remove all hooks before raising
self._remove_hooks(main_model_hooks)
undo_gradient_requirements(inputs, gradient_mask)
return _compute_conv_delta_and_format_attrs(
self,
return_convergence_delta,
attributions,
baselines,
inputs,
additional_forward_args,
target,
is_inputs_tuple,
)
def attribute(
self,
inputs: Union[Tensor, Tuple[Tensor, ...]],
baselines: BaselineType = None,
target: TargetType = None,
additional_forward_args: Any = None,
return_convergence_delta: bool = False,
attribute_to_layer_input: bool = False,
custom_attribution_func: Union[None, Callable[..., Tuple[Tensor,
...]]] = None,
) -> Union[Tensor, Tuple[Tensor, ...], Tuple[Union[Tensor, Tuple[
Tensor, ...]], Tensor]]:
r"""
Args:
inputs (tensor or tuple of tensors): Input for which layer
attributions are 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.
baselines (scalar, tensor, tuple of scalars or tensors, optional):
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.
- a tuple of tensors or scalars, the baseline corresponding
to each tensor in the inputs' tuple can be:
- either a tensor with matching dimensions to
corresponding tensor in the inputs' tuple
or the first dimension is one and the remaining
dimensions match with the corresponding
input tensor.
- or a scalar, corresponding to a tensor in the
inputs' tuple. This scalar value is broadcasted
for corresponding input tensor.
In the cases when `baselines` is not provided, we internally
use zero scalar corresponding to each input tensor.
Default: None
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
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
attribute_to_layer_input (bool, optional): 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
custom_attribution_func (callable, optional): 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
Returns:
**attributions** or 2-element tuple of **attributions**, **delta**:
- **attributions** (*tensor* or tuple of *tensors*):
Attribution score computed based on DeepLift's rescale rule with
respect to layer's inputs or outputs. Attributions will always be the
same size as the provided layer's inputs or outputs, depending on
whether we attribute to the inputs or outputs of the layer.
If the layer input / output is a single tensor, then
just a tensor is returned; if the layer input / output
has multiple tensors, then a corresponding tuple
of tensors is returned.
- **delta** (*tensor*, returned if return_convergence_delta=True):
This is computed using the property that the total sum of
forward_func(inputs) - forward_func(baselines) must equal the
total sum of the attributions computed based on DeepLift's
rescale rule.
Delta is calculated per example, meaning that the number of
elements in returned delta tensor is equal to the number of
of examples in input.
Note that the logic described for deltas is guaranteed
when the default logic for attribution computations is used,
meaning that the `custom_attribution_func=None`, otherwise
it is not guaranteed and depends on the specifics of the
`custom_attribution_func`.
Examples::
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
>>> # and returns an Nx10 tensor of class probabilities.
>>> net = ImageClassifier()
>>> # creates an instance of LayerDeepLift to interpret target
>>> # class 1 with respect to conv4 layer.
>>> dl = LayerDeepLift(net, net.conv4)
>>> input = torch.randn(1, 3, 32, 32, requires_grad=True)
>>> # Computes deeplift attribution scores for conv4 layer and class 3.
>>> attribution = dl.attribute(input, target=1)
"""
inputs = _format_input(inputs)
baselines = _format_baseline(baselines, inputs)
gradient_mask = apply_gradient_requirements(inputs)
_validate_input(inputs, baselines)
baselines = _tensorize_baseline(inputs, baselines)
main_model_hooks = []
try:
main_model_hooks = self._hook_main_model()
self.model.apply(lambda mod: self._register_hooks(
mod, attribute_to_layer_input=attribute_to_layer_input))
additional_forward_args = _format_additional_forward_args(
additional_forward_args)
expanded_target = _expand_target(
target, 2, expansion_type=ExpansionTypes.repeat)
wrapped_forward_func = self._construct_forward_func(
self.model,
(inputs, baselines),
expanded_target,
additional_forward_args,
)
def chunk_output_fn(
out: TensorOrTupleOfTensorsGeneric) -> Sequence:
if isinstance(out, Tensor):
return out.chunk(2)
return tuple(out_sub.chunk(2) for out_sub in out)
gradients, attrs = compute_layer_gradients_and_eval(
wrapped_forward_func,
self.layer,
inputs,
attribute_to_layer_input=attribute_to_layer_input,
output_fn=lambda out: chunk_output_fn(out),
)
attr_inputs = tuple(map(lambda attr: attr[0], attrs))
attr_baselines = tuple(map(lambda attr: attr[1], attrs))
gradients = tuple(map(lambda grad: grad[0], gradients))
if custom_attribution_func is None:
if self.multiplies_by_inputs:
attributions = tuple(
(input - baseline) * gradient
for input, baseline, gradient in zip(
attr_inputs, attr_baselines, gradients))
else:
attributions = gradients
else:
attributions = _call_custom_attribution_func(
custom_attribution_func, gradients, attr_inputs,
attr_baselines)
finally:
# remove hooks from all activations
self._remove_hooks(main_model_hooks)
undo_gradient_requirements(inputs, gradient_mask)
return _compute_conv_delta_and_format_attrs(
self,
return_convergence_delta,
attributions,
baselines,
inputs,
additional_forward_args,
target,
cast(Union[Literal[True], Literal[False]],
len(attributions) > 1),
)
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
target: TargetType = None,
additional_forward_args: Any = None,
) -> TensorOrTupleOfTensorsGeneric:
r"""
Args:
inputs (tensor or tuple of tensors): Input for which
attributions are 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
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 of *tensors* of **attributions**:
- **attributions** (*tensor* or tuple of *tensors*):
The input x gradient 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 InputXGradient interpreter
>>> input_x_gradient = InputXGradient(net)
>>> # Computes inputXgradient for class 4.
>>> attribution = input_x_gradient.attribute(input, target=4)
"""
# Keeps track whether original input is a tuple or not before
# converting it into a tuple.
is_inputs_tuple = _is_tuple(inputs)
inputs = _format_input(inputs)
gradient_mask = apply_gradient_requirements(inputs)
gradients = self.gradient_func(self.forward_func, inputs, target,
additional_forward_args)
attributions = tuple(input * gradient
for input, gradient in zip(inputs, gradients))
undo_gradient_requirements(inputs, gradient_mask)
return _format_output(is_inputs_tuple, attributions)
def attribute(
self,
inputs: Union[Tensor, Tuple[Tensor, ...]],
nt_type: str = "smoothgrad",
nt_samples: int = 5,
nt_samples_batch_size: int = None,
stdevs: Union[float, Tuple[float, ...]] = 1.0,
draw_baseline_from_distrib: bool = False,
**kwargs: Any,
) -> Union[
Union[
Tensor,
Tuple[Tensor, Tensor],
Tuple[Tensor, ...],
Tuple[Tuple[Tensor, ...], Tensor],
]
]:
r"""
Args:
inputs (tensor or tuple of tensors): Input for which integrated
gradients are 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, and if multiple input tensors
are provided, the examples must be aligned appropriately.
nt_type (string, optional): Smoothing type of the attributions.
`smoothgrad`, `smoothgrad_sq` or `vargrad`
Default: `smoothgrad` if `type` is not provided.
nt_samples (int, optional): The number of randomly generated examples
per sample in the input batch. Random examples are
generated by adding gaussian random noise to each sample.
Default: `5` if `nt_samples` is not provided.
nt_samples_batch_size (int, optional): The number of the `nt_samples`
that will be processed together. With the help
of this parameter we can avoid out of memory situation and
reduce the number of randomly generated examples per sample
in each batch.
Default: None if `nt_samples_batch_size` is not provided. In
this case all `nt_samples` will be processed together.
stdevs (float, or a tuple of floats optional): The standard deviation
of gaussian noise with zero mean that is added to each
input in the batch. If `stdevs` is a single float value
then that same value is used for all inputs. If it is
a tuple, then it must have the same length as the inputs
tuple. In this case, each stdev value in the stdevs tuple
corresponds to the input with the same index in the inputs
tuple.
Default: `1.0` if `stdevs` is not provided.
draw_baseline_from_distrib (bool, optional): Indicates whether to
randomly draw baseline samples from the `baselines`
distribution provided as an input tensor.
Default: False
**kwargs (Any, optional): Contains a list of arguments that are passed
to `attribution_method` attribution algorithm.
Any additional arguments that should be used for the
chosen attribution method should be included here.
For instance, such arguments include
`additional_forward_args` and `baselines`.
Returns:
**attributions** or 2-element tuple of **attributions**, **delta**:
- **attributions** (*tensor* or tuple of *tensors*):
Attribution 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.
- **delta** (*float*, returned if return_convergence_delta=True):
Approximation error computed by the
attribution algorithm. Not all attribution algorithms
return delta value. It is computed only for some
algorithms, e.g. integrated gradients.
Delta is computed for each input in the batch
and represents the arithmetic mean
across all `nt_samples` perturbed tensors for that input.
Examples::
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
>>> # and returns an Nx10 tensor of class probabilities.
>>> net = ImageClassifier()
>>> ig = IntegratedGradients(net)
>>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
>>> # Creates noise tunnel
>>> nt = NoiseTunnel(ig)
>>> # Generates 10 perturbed input tensors per image.
>>> # Computes integrated gradients for class 3 for each generated
>>> # input and averages attributions accros all 10
>>> # perturbed inputs per image
>>> attribution = nt.attribute(input, nt_type='smoothgrad',
>>> nt_samples=10, target=3)
"""
def add_noise_to_inputs(nt_samples_partition: int) -> Tuple[Tensor, ...]:
if isinstance(stdevs, tuple):
assert len(stdevs) == len(inputs), (
"The number of input tensors "
"in {} must be equal to the number of stdevs values {}".format(
len(inputs), len(stdevs)
)
)
else:
assert isinstance(
stdevs, float
), "stdevs must be type float. " "Given: {}".format(type(stdevs))
stdevs_ = (stdevs,) * len(inputs)
return tuple(
add_noise_to_input(input, stdev, nt_samples_partition).requires_grad_()
if self.is_gradient_method
else add_noise_to_input(input, stdev, nt_samples_partition)
for (input, stdev) in zip(inputs, stdevs_)
)
def add_noise_to_input(
input: Tensor, stdev: float, nt_samples_partition: int
) -> Tensor:
# batch size
bsz = input.shape[0]
# expand input size by the number of drawn samples
input_expanded_size = (bsz * nt_samples_partition,) + input.shape[1:]
# expand stdev for the shape of the input and number of drawn samples
stdev_expanded = torch.tensor(stdev, device=input.device).repeat(
input_expanded_size
)
# draws `np.prod(input_expanded_size)` samples from normal distribution
# with given input parametrization
# FIXME it look like it is very difficult to make torch.normal
# deterministic this needs an investigation
noise = torch.normal(0, stdev_expanded)
return input.repeat_interleave(nt_samples_partition, dim=0) + noise
def update_sum_attribution_and_sq(
sum_attribution: List[Tensor],
sum_attribution_sq: List[Tensor],
attribution: Tensor,
i: int,
nt_samples_batch_size_inter: int,
) -> None:
bsz = attribution.shape[0] // nt_samples_batch_size_inter
attribution_shape = cast(
Tuple[int, ...], (bsz, nt_samples_batch_size_inter)
)
if len(attribution.shape) > 1:
attribution_shape += cast(Tuple[int, ...], tuple(attribution.shape[1:]))
attribution = attribution.view(attribution_shape)
current_attribution_sum = attribution.sum(dim=1, keepdim=False)
current_attribution_sq = torch.sum(attribution ** 2, dim=1, keepdim=False)
sum_attribution[i] = (
current_attribution_sum
if not isinstance(sum_attribution[i], torch.Tensor)
else sum_attribution[i] + current_attribution_sum
)
sum_attribution_sq[i] = (
current_attribution_sq
if not isinstance(sum_attribution_sq[i], torch.Tensor)
else sum_attribution_sq[i] + current_attribution_sq
)
def compute_partial_attribution(
inputs_with_noise_partition: Tuple[Tensor, ...], kwargs_partition: Any
) -> Tuple[Tuple[Tensor, ...], bool, Union[None, Tensor]]:
# smoothgrad_Attr(x) = 1 / n * sum(Attr(x + N(0, sigma^2))
# NOTE: using __wrapped__ such that it does not log the inner logs
attributions = attr_func.__wrapped__( # type: ignore
self.attribution_method, # self
inputs_with_noise_partition
if is_inputs_tuple
else inputs_with_noise_partition[0],
**kwargs_partition,
)
delta = None
if self.is_delta_supported and return_convergence_delta:
attributions, delta = attributions
is_attrib_tuple = _is_tuple(attributions)
attributions = _format_tensor_into_tuples(attributions)
return (
cast(Tuple[Tensor, ...], attributions),
cast(bool, is_attrib_tuple),
delta,
)
def expand_partial(nt_samples_partition: int, kwargs_partial: dict) -> None:
# if the algorithm supports targets, baselines and/or
# additional_forward_args they will be expanded based
# on the nt_samples_partition and corresponding kwargs
# variables will be updated accordingly
_expand_and_update_additional_forward_args(
nt_samples_partition, kwargs_partial
)
_expand_and_update_target(nt_samples_partition, kwargs_partial)
_expand_and_update_baselines(
cast(Tuple[Tensor, ...], inputs),
nt_samples_partition,
kwargs_partial,
draw_baseline_from_distrib=draw_baseline_from_distrib,
)
_expand_and_update_feature_mask(nt_samples_partition, kwargs_partial)
def compute_smoothing(
expected_attributions: Tuple[Union[Tensor], ...],
expected_attributions_sq: Tuple[Union[Tensor], ...],
) -> Tuple[Tensor, ...]:
if NoiseTunnelType[nt_type] == NoiseTunnelType.smoothgrad:
return expected_attributions
if NoiseTunnelType[nt_type] == NoiseTunnelType.smoothgrad_sq:
return expected_attributions_sq
vargrad = tuple(
expected_attribution_sq - expected_attribution * expected_attribution
for expected_attribution, expected_attribution_sq in zip(
expected_attributions, expected_attributions_sq
)
)
return cast(Tuple[Tensor, ...], vargrad)
def update_partial_attribution_and_delta(
attributions_partial: Tuple[Tensor, ...],
delta_partial: Tensor,
sum_attributions: List[Tensor],
sum_attributions_sq: List[Tensor],
delta_partial_list: List[Tensor],
nt_samples_partial: int,
) -> None:
for i, attribution_partial in enumerate(attributions_partial):
update_sum_attribution_and_sq(
sum_attributions,
sum_attributions_sq,
attribution_partial,
i,
nt_samples_partial,
)
if self.is_delta_supported and return_convergence_delta:
delta_partial_list.append(delta_partial)
return_convergence_delta: bool
return_convergence_delta = (
"return_convergence_delta" in kwargs and kwargs["return_convergence_delta"]
)
with torch.no_grad():
nt_samples_batch_size = (
nt_samples
if nt_samples_batch_size is None
else min(nt_samples, nt_samples_batch_size)
)
nt_samples_partition = nt_samples // nt_samples_batch_size
# Keeps track whether original input is a tuple or not before
# converting it into a tuple.
is_inputs_tuple = isinstance(inputs, tuple)
inputs = _format_input(inputs) # type: ignore
_validate_noise_tunnel_type(nt_type, SUPPORTED_NOISE_TUNNEL_TYPES)
kwargs_copy = kwargs.copy()
expand_partial(nt_samples_batch_size, kwargs_copy)
attr_func = self.attribution_method.attribute
sum_attributions: List[Union[None, Tensor]] = []
sum_attributions_sq: List[Union[None, Tensor]] = []
delta_partial_list: List[Tensor] = []
for _ in range(nt_samples_partition):
inputs_with_noise = add_noise_to_inputs(nt_samples_batch_size)
(
attributions_partial,
is_attrib_tuple,
delta_partial,
) = compute_partial_attribution(inputs_with_noise, kwargs_copy)
if len(sum_attributions) == 0:
sum_attributions = [None] * len(attributions_partial)
sum_attributions_sq = [None] * len(attributions_partial)
update_partial_attribution_and_delta(
cast(Tuple[Tensor, ...], attributions_partial),
cast(Tensor, delta_partial),
cast(List[Tensor], sum_attributions),
cast(List[Tensor], sum_attributions_sq),
delta_partial_list,
nt_samples_batch_size,
)
nt_samples_remaining = (
nt_samples - nt_samples_partition * nt_samples_batch_size
)
if nt_samples_remaining > 0:
inputs_with_noise = add_noise_to_inputs(nt_samples_remaining)
expand_partial(nt_samples_remaining, kwargs)
(
attributions_partial,
is_attrib_tuple,
delta_partial,
) = compute_partial_attribution(inputs_with_noise, kwargs)
update_partial_attribution_and_delta(
cast(Tuple[Tensor, ...], attributions_partial),
cast(Tensor, delta_partial),
cast(List[Tensor], sum_attributions),
cast(List[Tensor], sum_attributions_sq),
delta_partial_list,
nt_samples_remaining,
)
expected_attributions = tuple(
[
cast(Tensor, sum_attribution) * 1 / nt_samples
for sum_attribution in sum_attributions
]
)
expected_attributions_sq = tuple(
[
cast(Tensor, sum_attribution_sq) * 1 / nt_samples
for sum_attribution_sq in sum_attributions_sq
]
)
attributions = compute_smoothing(
cast(Tuple[Tensor, ...], expected_attributions),
cast(Tuple[Tensor, ...], expected_attributions_sq),
)
delta = None
if self.is_delta_supported and return_convergence_delta:
delta = torch.cat(delta_partial_list, dim=0)
return self._apply_checks_and_return_attributions(
attributions, is_attrib_tuple, return_convergence_delta, delta
)
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
neuron_selector: Union[int, Tuple[Union[int, slice], ...], Callable],
additional_forward_args: Any = None,
attribute_to_neuron_input: bool = False,
) -> TensorOrTupleOfTensorsGeneric:
r"""
Args:
inputs (tensor or tuple of tensors): Input for which neuron
gradients are 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, and if multiple input tensors
are provided, the examples must be aligned appropriately.
neuron_selector (int, callable, or tuple of ints or slices):
Selector for neuron
in given layer for which attribution is desired.
Neuron selector can be provided as:
- a single integer, if the layer output is 2D. This integer
selects the appropriate neuron column in the layer input
or output
- a tuple of integers or slice objects. Length of this
tuple must be one less than the number of dimensions
in the input / output of the given layer (since
dimension 0 corresponds to number of examples).
The elements of the tuple can be either integers or
slice objects (slice object allows indexing a
range of neurons rather individual ones).
If any of the tuple elements is a slice object, the
indexed output tensor is used for attribution. Note
that specifying a slice of a tensor would amount to
computing the attribution of the sum of the specified
neurons, and not the individual neurons independantly.
- a callable, which should
take the target layer as input (single tensor or tuple
if multiple tensors are in layer) and return a neuron or
aggregate of the layer's neurons for attribution.
For example, this function could return the
sum of the neurons in the layer or sum of neurons with
activations in a particular range. It is expected that
this function returns either a tensor with one element
or a 1D tensor with length equal to batch_size (one scalar
per input example)
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
attribute_to_neuron_input (bool, optional): Indicates whether to
compute the attributions with respect to the neuron input
or output. If `attribute_to_neuron_input` is set to True
then the attributions will be computed with respect to
neuron's inputs, otherwise it will be computed with respect
to neuron's outputs.
Note that currently it is assumed that either the input
or the output of internal neurons, 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:
*tensor* or tuple of *tensors* of **attributions**:
- **attributions** (*tensor* or tuple of *tensors*):
Gradients of particular neuron 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.
>>> # It contains an attribute conv1, which is an instance of nn.conv2d,
>>> # and the output of this layer has dimensions Nx12x32x32.
>>> net = ImageClassifier()
>>> neuron_ig = NeuronGradient(net, net.conv1)
>>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
>>> # To compute neuron attribution, we need to provide the neuron
>>> # index for which attribution is desired. Since the layer output
>>> # is Nx12x32x32, we need a tuple in the form (0..11,0..31,0..31)
>>> # which indexes a particular neuron in the layer output.
>>> # For this example, we choose the index (4,1,2).
>>> # Computes neuron gradient for neuron with
>>> # index (4,1,2).
>>> attribution = neuron_ig.attribute(input, (4,1,2))
"""
is_inputs_tuple = _is_tuple(inputs)
inputs = _format_input(inputs)
additional_forward_args = _format_additional_forward_args(
additional_forward_args
)
gradient_mask = apply_gradient_requirements(inputs)
_, input_grads = _forward_layer_eval_with_neuron_grads(
self.forward_func,
inputs,
self.layer,
additional_forward_args,
gradient_neuron_selector=neuron_selector,
device_ids=self.device_ids,
attribute_to_layer_input=attribute_to_neuron_input,
)
undo_gradient_requirements(inputs, gradient_mask)
return _format_output(is_inputs_tuple, input_grads)
def attribute(
self,
inputs: Union[Tensor, Tuple[Tensor, ...]],
layer_baselines: BaselineType = None,
target: TargetType = None,
additional_forward_args: Any = None,
layer_mask: Union[None, Tensor, Tuple[Tensor, ...]] = None,
attribute_to_layer_input: bool = False,
perturbations_per_eval: int = 1,
) -> Union[Tensor, Tuple[Tensor, ...]]:
r"""
Args:
inputs (tensor or tuple of tensors): Input for which layer
attributions are 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, and if multiple input tensors
are provided, the examples must be aligned appropriately.
layer_baselines (scalar, tensor, tuple of scalars or tensors, optional):
Layer baselines define reference values which replace each
layer input / output value when ablated.
Layer baselines should be a single tensor with dimensions
matching the input / output of the target layer (or
broadcastable to match it), based
on whether we are attributing to the input or output
of the target layer.
In the cases when `baselines` is not provided, we internally
use zero as the baseline for each neuron.
Default: None
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 forward_func in order following the
arguments in inputs.
Note that attributions are not computed with respect
to these arguments.
Default: None
layer_mask (tensor or tuple of tensors, optional):
layer_mask defines a mask for the layer, grouping
elements of the layer input / output which should be
ablated together.
layer_mask should be a single tensor with dimensions
matching the input / output of the target layer (or
broadcastable to match it), based
on whether we are attributing to the input or output
of the target layer. layer_mask
should contain integers in the range 0 to num_groups
- 1, and all elements with the same value are
considered to be in the same group.
If None, then a layer mask is constructed which assigns
each neuron within the layer as a separate group, which
is ablated independently.
Default: None
attribute_to_layer_input (bool, optional): Indicates whether to
compute the attributions 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's inputs, otherwise it will be computed with respect
to layer's outputs.
Note that currently it is assumed that either the input
or the output of the 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
perturbations_per_eval (int, optional): Allows ablation of multiple
neuron (groups) to be processed simultaneously in one
call to forward_fn.
Each forward pass will contain a maximum of
perturbations_per_eval * #examples samples.
For DataParallel models, each batch is split among the
available devices, so evaluations on each available
device contain at most
(perturbations_per_eval * #examples) / num_devices
samples.
Default: 1
Returns:
*tensor* or tuple of *tensors* of **attributions**:
- **attributions** (*tensor* or tuple of *tensors*):
Attribution 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.
Examples::
>>> # SimpleClassifier takes a single input tensor of size Nx4x4,
>>> # and returns an Nx3 tensor of class probabilities.
>>> # It contains an attribute conv1, which is an instance of nn.conv2d,
>>> # and the output of this layer has dimensions Nx12x3x3.
>>> net = SimpleClassifier()
>>> # Generating random input with size 2 x 4 x 4
>>> input = torch.randn(2, 4, 4)
>>> # Defining LayerFeatureAblation interpreter
>>> ablator = LayerFeatureAblation(net, net.conv1)
>>> # Computes ablation attribution, ablating each of the 108
>>> # neurons independently.
>>> attr = ablator.attribute(input, target=1)
>>> # Alternatively, we may want to ablate neurons in groups, e.g.
>>> # grouping all the layer outputs in the same row.
>>> # This can be done by creating a layer mask as follows, which
>>> # defines the groups of layer inputs / outouts, e.g.:
>>> # +---+---+---+
>>> # | 0 | 0 | 0 |
>>> # +---+---+---+
>>> # | 1 | 1 | 1 |
>>> # +---+---+---+
>>> # | 2 | 2 | 2 |
>>> # +---+---+---+
>>> # With this mask, all the 36 neurons in a row / channel are ablated
>>> # simultaneously, and the attribution for each neuron in the same
>>> # group (0 - 2) per example are the same.
>>> # The attributions can be calculated as follows:
>>> # layer mask has dimensions 1 x 3 x 3
>>> layer_mask = torch.tensor([[[0,0,0],[1,1,1],
>>> [2,2,2]]])
>>> attr = ablator.attribute(input, target=1,
>>> layer_mask=layer_mask)
"""
def layer_forward_func(*args):
layer_length = args[-1]
layer_input = args[:layer_length]
original_inputs = args[layer_length:-1]
device_ids = self.device_ids
if device_ids is None:
device_ids = getattr(self.forward_func, "device_ids", None)
all_layer_inputs = {}
if device_ids is not None:
scattered_layer_input = scatter(layer_input,
target_gpus=device_ids)
for device_tensors in scattered_layer_input:
all_layer_inputs[device_tensors[0].device] = device_tensors
else:
all_layer_inputs[layer_input[0].device] = layer_input
def forward_hook(module, inp, out=None):
device = _extract_device(module, inp, out)
is_layer_tuple = (isinstance(out, tuple) if out is not None
else isinstance(inp, tuple))
if device not in all_layer_inputs:
raise AssertionError(
"Layer input not placed on appropriate "
"device. If using a DataParallel model, either provide the "
"DataParallel model as forward_func or provide device ids"
" to the constructor.")
if not is_layer_tuple:
return all_layer_inputs[device][0]
return all_layer_inputs[device]
hook = None
try:
if attribute_to_layer_input:
hook = self.layer.register_forward_pre_hook(forward_hook)
else:
hook = self.layer.register_forward_hook(forward_hook)
eval = _run_forward(self.forward_func,
original_inputs,
target=target)
finally:
if hook is not None:
hook.remove()
return eval
with torch.no_grad():
inputs = _format_input(inputs)
additional_forward_args = _format_additional_forward_args(
additional_forward_args)
layer_eval = _forward_layer_eval(
self.forward_func,
inputs,
self.layer,
additional_forward_args,
device_ids=self.device_ids,
attribute_to_layer_input=attribute_to_layer_input,
)
layer_eval_len = (len(layer_eval), )
all_inputs = ((inputs + additional_forward_args + layer_eval_len)
if additional_forward_args is not None else inputs +
layer_eval_len)
ablator = FeatureAblation(layer_forward_func)
layer_attribs = ablator.attribute.__wrapped__(
ablator, # self
layer_eval,
baselines=layer_baselines,
additional_forward_args=all_inputs,
feature_mask=layer_mask,
perturbations_per_eval=perturbations_per_eval,
)
_attr = _format_output(len(layer_attribs) > 1, layer_attribs)
return _attr
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
baselines: BaselineType = None,
target: TargetType = None,
additional_forward_args: Any = None,
feature_mask: Union[None, TensorOrTupleOfTensorsGeneric] = None,
n_samples: int = 25,
perturbations_per_eval: int = 1,
show_progress: bool = False,
) -> TensorOrTupleOfTensorsGeneric:
r"""
NOTE: The feature_mask argument differs from other perturbation based
methods, since feature indices can overlap across tensors. See the
description of the feature_mask argument below for more details.
Args:
inputs (tensor or tuple of tensors): Input for which Shapley value
sampling attributions are 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.
baselines (scalar, tensor, tuple of scalars or tensors, optional):
Baselines define reference value which replaces each
feature when ablated.
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.
- a tuple of tensors or scalars, the baseline corresponding
to each tensor in the inputs' tuple can be:
- either a tensor with matching dimensions to
corresponding tensor in the inputs' tuple
or the first dimension is one and the remaining
dimensions match with the corresponding
input tensor.
- or a scalar, corresponding to a tensor in the
inputs' tuple. This scalar value is broadcasted
for corresponding input tensor.
In the cases when `baselines` is not provided, we internally
use zero scalar corresponding to each input tensor.
Default: None
target (int, tuple, tensor or list, optional): Output indices for
which difference is 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 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. 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
feature_mask (tensor or tuple of tensors, optional):
feature_mask defines a mask for the input, grouping
features which should be added together. feature_mask
should contain the same number of tensors as inputs.
Each tensor should
be the same size as the corresponding input or
broadcastable to match the input tensor. Values across
all tensors should be integers in the range 0 to
num_features - 1, and indices corresponding to the same
feature should have the same value.
Note that features are grouped across tensors
(unlike feature ablation and occlusion), so
if the same index is used in different tensors, those
features are still grouped and added simultaneously.
If the forward function returns a single scalar per batch,
we enforce that the first dimension of each mask must be 1,
since attributions are returned batch-wise rather than per
example, so the attributions must correspond to the
same features (indices) in each input example.
If None, then a feature mask is constructed which assigns
each scalar within a tensor as a separate feature
Default: None
n_samples (int, optional): The number of feature permutations
tested.
Default: `25` if `n_samples` is not provided.
perturbations_per_eval (int, optional): Allows multiple ablations
to be processed simultaneously in one call to forward_fn.
Each forward pass will contain a maximum of
perturbations_per_eval * #examples samples.
For DataParallel models, each batch is split among the
available devices, so evaluations on each available
device contain at most
(perturbations_per_eval * #examples) / num_devices
samples.
If the forward function returns a single scalar per batch,
perturbations_per_eval must be set to 1.
Default: 1
show_progress (bool, optional): Displays the progress of computation.
It will try to use tqdm if available for advanced features
(e.g. time estimation). Otherwise, it will fallback to
a simple output of progress.
Default: False
Returns:
*tensor* or tuple of *tensors* of **attributions**:
- **attributions** (*tensor* or tuple of *tensors*):
The attributions with respect to each input feature.
If the forward function returns
a scalar value per example, attributions will be
the same size as the provided inputs, with each value
providing the attribution of the corresponding input index.
If the forward function returns a scalar per batch, then
attribution tensor(s) will have first dimension 1 and
the remaining dimensions will match the input.
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::
>>> # SimpleClassifier takes a single input tensor of size Nx4x4,
>>> # and returns an Nx3 tensor of class probabilities.
>>> net = SimpleClassifier()
>>> # Generating random input with size 2 x 4 x 4
>>> input = torch.randn(2, 4, 4)
>>> # Defining ShapleyValueSampling interpreter
>>> svs = ShapleyValueSampling(net)
>>> # Computes attribution, taking random orderings
>>> # of the 16 features and computing the output change when adding
>>> # each feature. We average over 200 trials (random permutations).
>>> attr = svs.attribute(input, target=1, n_samples=200)
>>> # Alternatively, we may want to add features in groups, e.g.
>>> # grouping each 2x2 square of the inputs and adding them together.
>>> # This can be done by creating a feature mask as follows, which
>>> # defines the feature groups, e.g.:
>>> # +---+---+---+---+
>>> # | 0 | 0 | 1 | 1 |
>>> # +---+---+---+---+
>>> # | 0 | 0 | 1 | 1 |
>>> # +---+---+---+---+
>>> # | 2 | 2 | 3 | 3 |
>>> # +---+---+---+---+
>>> # | 2 | 2 | 3 | 3 |
>>> # +---+---+---+---+
>>> # With this mask, all inputs with the same value are added
>>> # together, and the attribution for each input in the same
>>> # group (0, 1, 2, and 3) per example are the same.
>>> # The attributions can be calculated as follows:
>>> # feature mask has dimensions 1 x 4 x 4
>>> feature_mask = torch.tensor([[[0,0,1,1],[0,0,1,1],
>>> [2,2,3,3],[2,2,3,3]]])
>>> attr = svs.attribute(input, target=1, feature_mask=feature_mask)
"""
# Keeps track whether original input is a tuple or not before
# converting it into a tuple.
is_inputs_tuple = _is_tuple(inputs)
inputs, baselines = _format_input_baseline(inputs, baselines)
additional_forward_args = _format_additional_forward_args(
additional_forward_args
)
feature_mask = _format_input(feature_mask) if feature_mask is not None else None
assert (
isinstance(perturbations_per_eval, int) and perturbations_per_eval >= 1
), "Ablations per evaluation must be at least 1."
with torch.no_grad():
baselines = _tensorize_baseline(inputs, baselines)
num_examples = inputs[0].shape[0]
if feature_mask is None:
feature_mask, total_features = _construct_default_feature_mask(inputs)
else:
total_features = int(
max(torch.max(single_mask).item() for single_mask in feature_mask)
+ 1
)
if show_progress:
attr_progress = progress(
desc=f"{self.get_name()} attribution",
total=self._get_n_evaluations(
total_features, n_samples, perturbations_per_eval
)
+ 1, # add 1 for the initial eval
)
attr_progress.update(0)
initial_eval = _run_forward(
self.forward_func, baselines, target, additional_forward_args
)
if show_progress:
attr_progress.update()
agg_output_mode = _find_output_mode_and_verify(
initial_eval, num_examples, perturbations_per_eval, feature_mask
)
# Initialize attribution totals and counts
total_attrib = [
torch.zeros_like(
input[0:1] if agg_output_mode else input, dtype=torch.float
)
for input in inputs
]
iter_count = 0
# Iterate for number of samples, generate a permutation of the features
# and evalute the incremental increase for each feature.
for feature_permutation in self.permutation_generator(
total_features, n_samples
):
iter_count += 1
prev_results = initial_eval
for (
current_inputs,
current_add_args,
current_target,
current_masks,
) in self._perturbation_generator(
inputs,
additional_forward_args,
target,
baselines,
feature_mask,
feature_permutation,
perturbations_per_eval,
):
if sum(torch.sum(mask).item() for mask in current_masks) == 0:
warnings.warn(
"Feature mask is missing some integers between 0 and "
"num_features, for optimal performance, make sure each"
" consecutive integer corresponds to a feature."
)
# modified_eval dimensions: 1D tensor with length
# equal to #num_examples * #features in batch
modified_eval = _run_forward(
self.forward_func,
current_inputs,
current_target,
current_add_args,
)
if show_progress:
attr_progress.update()
if agg_output_mode:
eval_diff = modified_eval - prev_results
prev_results = modified_eval
else:
all_eval = torch.cat((prev_results, modified_eval), dim=0)
eval_diff = all_eval[num_examples:] - all_eval[:-num_examples]
prev_results = all_eval[-num_examples:]
for j in range(len(total_attrib)):
current_eval_diff = eval_diff
if not agg_output_mode:
# current_eval_diff dimensions:
# (#features in batch, #num_examples, 1,.. 1)
# (contains 1 more dimension than inputs). This adds extra
# dimensions of 1 to make the tensor broadcastable with the
# inputs tensor.
current_eval_diff = current_eval_diff.reshape(
(-1, num_examples) + (len(inputs[j].shape) - 1) * (1,)
)
total_attrib[j] += (
current_eval_diff * current_masks[j].float()
).sum(dim=0)
if show_progress:
attr_progress.close()
# Divide total attributions by number of random permutations and return
# formatted attributions.
attrib = tuple(
tensor_attrib_total / iter_count for tensor_attrib_total in total_attrib
)
formatted_attr = _format_output(is_inputs_tuple, attrib)
return formatted_attr
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
baselines: BaselineType = None,
target: TargetType = None,
additional_forward_args: Any = None,
feature_mask: Union[None, TensorOrTupleOfTensorsGeneric] = None,
perturbations_per_eval: int = 1,
show_progress: bool = False,
) -> TensorOrTupleOfTensorsGeneric:
r"""
NOTE: The feature_mask argument differs from other perturbation based
methods, since feature indices can overlap across tensors. See the
description of the feature_mask argument below for more details.
Args:
inputs (tensor or tuple of tensors): Input for which Shapley value
sampling attributions are 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.
baselines (scalar, tensor, tuple of scalars or tensors, optional):
Baselines define reference value which replaces each
feature when ablated.
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.
- a tuple of tensors or scalars, the baseline corresponding
to each tensor in the inputs' tuple can be:
- either a tensor with matching dimensions to
corresponding tensor in the inputs' tuple
or the first dimension is one and the remaining
dimensions match with the corresponding
input tensor.
- or a scalar, corresponding to a tensor in the
inputs' tuple. This scalar value is broadcasted
for corresponding input tensor.
In the cases when `baselines` is not provided, we internally
use zero scalar corresponding to each input tensor.
Default: None
target (int, tuple, tensor or list, optional): Output indices for
which difference is 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 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. 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
feature_mask (tensor or tuple of tensors, optional):
feature_mask defines a mask for the input, grouping
features which should be added together. feature_mask
should contain the same number of tensors as inputs.
Each tensor should
be the same size as the corresponding input or
broadcastable to match the input tensor. Values across
all tensors should be integers in the range 0 to
num_features - 1, and indices corresponding to the same
feature should have the same value.
Note that features are grouped across tensors
(unlike feature ablation and occlusion), so
if the same index is used in different tensors, those
features are still grouped and added simultaneously.
If the forward function returns a single scalar per batch,
we enforce that the first dimension of each mask must be 1,
since attributions are returned batch-wise rather than per
example, so the attributions must correspond to the
same features (indices) in each input example.
If None, then a feature mask is constructed which assigns
each scalar within a tensor as a separate feature
Default: None
perturbations_per_eval (int, optional): Allows multiple ablations
to be processed simultaneously in one call to forward_fn.
Each forward pass will contain a maximum of
perturbations_per_eval * #examples samples.
For DataParallel models, each batch is split among the
available devices, so evaluations on each available
device contain at most
(perturbations_per_eval * #examples) / num_devices
samples.
If the forward function returns a single scalar per batch,
perturbations_per_eval must be set to 1.
Default: 1
show_progress (bool, optional): Displays the progress of computation.
It will try to use tqdm if available for advanced features
(e.g. time estimation). Otherwise, it will fallback to
a simple output of progress.
Default: False
Returns:
*tensor* or tuple of *tensors* of **attributions**:
- **attributions** (*tensor* or tuple of *tensors*):
The attributions with respect to each input feature.
If the forward function returns
a scalar value per example, attributions will be
the same size as the provided inputs, with each value
providing the attribution of the corresponding input index.
If the forward function returns a scalar per batch, then
attribution tensor(s) will have first dimension 1 and
the remaining dimensions will match the input.
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::
>>> # SimpleClassifier takes a single input tensor of size Nx4x4,
>>> # and returns an Nx3 tensor of class probabilities.
>>> net = SimpleClassifier()
>>> # Generating random input with size 2 x 4 x 4
>>> input = torch.randn(2, 4, 4)
>>> # We may want to add features in groups, e.g.
>>> # grouping each 2x2 square of the inputs and adding them together.
>>> # This can be done by creating a feature mask as follows, which
>>> # defines the feature groups, e.g.:
>>> # +---+---+---+---+
>>> # | 0 | 0 | 1 | 1 |
>>> # +---+---+---+---+
>>> # | 0 | 0 | 1 | 1 |
>>> # +---+---+---+---+
>>> # | 2 | 2 | 3 | 3 |
>>> # +---+---+---+---+
>>> # | 2 | 2 | 3 | 3 |
>>> # +---+---+---+---+
>>> # With this mask, all inputs with the same value are added
>>> # together, and the attribution for each input in the same
>>> # group (0, 1, 2, and 3) per example are the same.
>>> # The attributions can be calculated as follows:
>>> # feature mask has dimensions 1 x 4 x 4
>>> feature_mask = torch.tensor([[[0,0,1,1],[0,0,1,1],
>>> [2,2,3,3],[2,2,3,3]]])
>>> # With only 4 features, it is feasible to compute exact
>>> # Shapley Values. These can be computed as follows:
>>> sv = ShapleyValues(net)
>>> attr = sv.attribute(input, target=1, feature_mask=feature_mask)
"""
if feature_mask is None:
total_features = sum(torch.numel(inp[0]) for inp in _format_input(inputs))
else:
total_features = (
int(max(torch.max(single_mask).item() for single_mask in feature_mask))
+ 1
)
if total_features >= 10:
warnings.warn(
"You are attempting to compute Shapley Values with at least 10 "
"features, which will likely be very computationally expensive."
"Consider using Shapley Value Sampling instead."
)
return super().attribute.__wrapped__(
self,
inputs=inputs,
baselines=baselines,
target=target,
additional_forward_args=additional_forward_args,
feature_mask=feature_mask,
perturbations_per_eval=perturbations_per_eval,
show_progress=show_progress,
)
def attribute(
self,
inputs: Union[Tensor, Tuple[Tensor, ...]],
nt_type: str = "smoothgrad",
n_samples: int = 5,
stdevs: Union[float, Tuple[float, ...]] = 1.0,
draw_baseline_from_distrib: bool = False,
**kwargs: Any,
):
r"""
Args:
inputs (tensor or tuple of tensors): Input for which integrated
gradients are 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, and if multiple input tensors
are provided, the examples must be aligned appropriately.
nt_type (string, optional): Smoothing type of the attributions.
`smoothgrad`, `smoothgrad_sq` or `vargrad`
Default: `smoothgrad` if `type` is not provided.
n_samples (int, optional): The number of randomly generated examples
per sample in the input batch. Random examples are
generated by adding gaussian random noise to each sample.
Default: `5` if `n_samples` is not provided.
stdevs (float, or a tuple of floats optional): The standard deviation
of gaussian noise with zero mean that is added to each
input in the batch. If `stdevs` is a single float value
then that same value is used for all inputs. If it is
a tuple, then it must have the same length as the inputs
tuple. In this case, each stdev value in the stdevs tuple
corresponds to the input with the same index in the inputs
tuple.
Default: `1.0` if `stdevs` is not provided.
draw_baseline_from_distrib (bool, optional): Indicates whether to
randomly draw baseline samples from the `baselines`
distribution provided as an input tensor.
Default: False
**kwargs (Any, optional): Contains a list of arguments that are passed
to `attribution_method` attribution algorithm.
Any additional arguments that should be used for the
chosen attribution method should be included here.
For instance, such arguments include
`additional_forward_args` and `baselines`.
Returns:
**attributions** or 2-element tuple of **attributions**, **delta**:
- **attributions** (*tensor* or tuple of *tensors*):
Attribution 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.
- **delta** (*float*, returned if return_convergence_delta=True):
Approximation error computed by the
attribution algorithm. Not all attribution algorithms
return delta value. It is computed only for some
algorithms, e.g. integrated gradients.
Delta is computed for each input in the batch
and represents the arithmetic mean
across all `n_sample` perturbed tensors for that input.
Examples::
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
>>> # and returns an Nx10 tensor of class probabilities.
>>> net = ImageClassifier()
>>> ig = IntegratedGradients(net)
>>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
>>> # Creates noise tunnel
>>> nt = NoiseTunnel(ig)
>>> # Generates 10 perturbed input tensors per image.
>>> # Computes integrated gradients for class 3 for each generated
>>> # input and averages attributions accros all 10
>>> # perturbed inputs per image
>>> attribution = nt.attribute(input, nt_type='smoothgrad',
>>> n_samples=10, target=3)
"""
def add_noise_to_inputs() -> Tuple[Tensor, ...]:
if isinstance(stdevs, tuple):
assert len(stdevs) == len(inputs), (
"The number of input tensors "
"in {} must be equal to the number of stdevs values {}".format(
len(inputs), len(stdevs)
)
)
else:
assert isinstance(
stdevs, float
), "stdevs must be type float. " "Given: {}".format(type(stdevs))
stdevs_ = (stdevs,) * len(inputs)
return tuple(
add_noise_to_input(input, stdev).requires_grad_()
if self.is_gradient_method
else add_noise_to_input(input, stdev)
for (input, stdev) in zip(inputs, stdevs_)
)
def add_noise_to_input(input: Tensor, stdev: float) -> Tensor:
# batch size
bsz = input.shape[0]
# expand input size by the number of drawn samples
input_expanded_size = (bsz * n_samples,) + input.shape[1:]
# expand stdev for the shape of the input and number of drawn samples
stdev_expanded = torch.tensor(stdev, device=input.device).repeat(
input_expanded_size
)
# draws `np.prod(input_expanded_size)` samples from normal distribution
# with given input parametrization
# FIXME it look like it is very difficult to make torch.normal
# deterministic this needs an investigation
noise = torch.normal(0, stdev_expanded)
return input.repeat_interleave(n_samples, dim=0) + noise
def compute_expected_attribution_and_sq(attribution):
bsz = attribution.shape[0] // n_samples
attribution_shape = (bsz, n_samples)
if len(attribution.shape) > 1:
attribution_shape += attribution.shape[1:]
attribution = attribution.view(attribution_shape)
expected_attribution = attribution.mean(dim=1, keepdim=False)
expected_attribution_sq = torch.mean(attribution ** 2, dim=1, keepdim=False)
return expected_attribution, expected_attribution_sq
with torch.no_grad():
# Keeps track whether original input is a tuple or not before
# converting it into a tuple.
is_inputs_tuple = isinstance(inputs, tuple)
inputs = _format_input(inputs)
_validate_noise_tunnel_type(nt_type, SUPPORTED_NOISE_TUNNEL_TYPES)
delta = None
inputs_with_noise = add_noise_to_inputs()
# if the algorithm supports targets, baselines and/or
# additional_forward_args they will be expanded based
# on the n_steps and corresponding kwargs
# variables will be updated accordingly
_expand_and_update_additional_forward_args(n_samples, kwargs)
_expand_and_update_target(n_samples, kwargs)
_expand_and_update_baselines(
inputs,
n_samples,
kwargs,
draw_baseline_from_distrib=draw_baseline_from_distrib,
)
# smoothgrad_Attr(x) = 1 / n * sum(Attr(x + N(0, sigma^2))
# NOTE: using __wrapped__ such that it does not log the inner logs
attr_func = self.attribution_method.attribute
attributions = attr_func.__wrapped__( # type: ignore
self.attribution_method, # self
inputs_with_noise if is_inputs_tuple else inputs_with_noise[0],
**kwargs,
)
return_convergence_delta = (
"return_convergence_delta" in kwargs
and kwargs["return_convergence_delta"]
)
if self.is_delta_supported and return_convergence_delta:
attributions, delta = attributions
is_attrib_tuple = _is_tuple(attributions)
attributions = _format_tensor_into_tuples(attributions)
expected_attributions = []
expected_attributions_sq = []
for attribution in attributions:
expected_attr, expected_attr_sq = compute_expected_attribution_and_sq(
attribution
)
expected_attributions.append(expected_attr)
expected_attributions_sq.append(expected_attr_sq)
if NoiseTunnelType[nt_type] == NoiseTunnelType.smoothgrad:
return self._apply_checks_and_return_attributions(
tuple(expected_attributions),
is_attrib_tuple,
return_convergence_delta,
delta,
)
if NoiseTunnelType[nt_type] == NoiseTunnelType.smoothgrad_sq:
return self._apply_checks_and_return_attributions(
tuple(expected_attributions_sq),
is_attrib_tuple,
return_convergence_delta,
delta,
)
vargrad = tuple(
expected_attribution_sq - expected_attribution * expected_attribution
for expected_attribution, expected_attribution_sq in zip(
expected_attributions, expected_attributions_sq
)
)
return self._apply_checks_and_return_attributions(
vargrad, is_attrib_tuple, return_convergence_delta, delta
)
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
target: TargetType = None,
additional_forward_args: Any = None,
return_convergence_delta: bool = False,
attribute_to_layer_input: bool = False,
verbose: bool = False,
) -> Union[Tensor, Tuple[Tensor, ...], List[Union[Tensor, Tuple[
Tensor, ...]]], Tuple[Union[Tensor, Tuple[Tensor, ...], List[Union[
Tensor, Tuple[Tensor, ...]]]], Union[Tensor,
List[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
attribute_to_layer_input (bool, optional): 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.
verbose (bool, optional): Indicates whether information on application
of rules is printed during propagation.
Default: False
Returns:
*tensor* or tuple of *tensors* of **attributions** or 2-element tuple of
**attributions**, **delta** or lists of **attributions** and **delta**:
- **attributions** (*tensor* or tuple of *tensors*):
The propagated relevance values 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. The sum of attributions
is one and not corresponding to the prediction score as in other
implementations. If attributions for all layers are returned
(layer=None) a list of tensors or tuples of tensors is returned
with entries for each layer.
- **delta** (*tensor* or list of *tensors*
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 input.
If attributions for all layers are returned (layer=None) a list
of tensors is returned with entries for
each layer.
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, net.conv1)
>>> 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 = []
self._get_layers(self.model)
self._check_and_attach_rules()
self.attribute_to_layer_input = attribute_to_layer_input
self.backward_handles = []
self.forward_handles = []
inputs = _format_input(inputs)
gradient_mask = apply_gradient_requirements(inputs)
try:
# 1. Forward pass
output = self._compute_output_and_change_weights(
inputs, target, additional_forward_args)
self._register_forward_hooks()
# 2. Forward pass + backward pass
_ = compute_gradients(self._forward_fn_wrapper, inputs, target,
additional_forward_args)
relevances = self._get_output_relevance(output)
finally:
self._restore_model()
undo_gradient_requirements(inputs, gradient_mask)
if return_convergence_delta:
delta: Union[Tensor, List[Tensor]]
if isinstance(self.layer, list):
delta = []
for relevance_layer in relevances:
delta.append(
self.compute_convergence_delta(relevance_layer,
output))
else:
delta = self.compute_convergence_delta(
cast(Tuple[Tensor, ...], relevances), output)
return relevances, delta # type: ignore
else:
return relevances # type: ignore
def sensitivity_max(
explanation_func: Callable,
inputs: TensorOrTupleOfTensorsGeneric,
perturb_func: Callable = default_perturb_func,
perturb_radius: float = 0.02,
n_perturb_samples: int = 10,
norm_ord: str = "fro",
max_examples_per_batch: int = None,
**kwargs: Any,
) -> Tensor:
r"""
Explanation sensitivity measures the extent of explanation change when
the input is slightly perturbed. It has been shown that the models that
have high explanation sensitivity are prone to adversarial attacks:
`Interpretation of Neural Networks is Fragile`
https://www.aaai.org/ojs/index.php/AAAI/article/view/4252
`sensitivity_max` metric measures maximum sensitivity of an explanation
using Monte Carlo sampling-based approximation. By default in order to
do so it samples multiple data points from a sub-space of an L-Infinity
ball that has a `perturb_radius` radius using `default_perturb_func`
default perturbation function. In a general case users can
use any L_p ball or any other custom sampling technique that they
prefer by providing a custom `perturb_func`.
Note that max sensitivity is similar to Lipschitz Continuity metric
however it is more robust and easier to estimate.
Since the explanation, for instance an attribution function,
may not always be continuous, can lead to unbounded
Lipschitz continuity. Therefore the latter isn't always appropriate.
More about the Lipschitz Continuity Metric can also be found here
`On the Robustness of Interpretability Methods`
https://arxiv.org/pdf/1806.08049.pdf
and
`Towards Robust Interpretability with Self-Explaining Neural Networks`
https://papers.nips.cc/paper\
8003-towards-robust-interpretability-
with-self-explaining-neural-networks.pdf
More details about sensitivity max can be found here:
`On the (In)fidelity and Sensitivity of Explanations`
https://arxiv.org/pdf/1901.09392.pdf
Args:
explanation_func (callable):
This function can be the `attribute` method of an
attribution algorithm or any other explanation method
that returns the explanations.
inputs (tensor or tuple of tensors): Input for which
explanations are computed. If `explanation_func` takes a
single tensor as input, a single input tensor should
be provided.
If `explanation_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.
perturb_func (callable):
The perturbation function of model inputs. This function takes
model inputs and optionally `perturb_radius` if
the function takes more than one argument and returns
perturbed inputs.
If there are more than one inputs passed to sensitivity function those
will be passed to `perturb_func` as tuples in the same order as they
are passed to sensitivity function.
It is important to note that for performance reasons `perturb_func`
isn't called for each example individually but on a batch of
input examples that are repeated `max_examples_per_batch / batch_size`
times within the batch.
Default: default_perturb_func
perturb_radius (float, optional): The epsilon radius used for sampling.
In the `default_perturb_func` it is used as the radius of
the L-Infinity ball. In a general case it can serve as a radius of
any L_p nom.
This argument is passed to `perturb_func` if it takes more than
one argument.
Default: 0.02
n_perturb_samples (int, optional): The number of times input tensors
are perturbed. Each input example in the inputs tensor is
expanded `n_perturb_samples` times before calling
`perturb_func` function.
Default: 10
norm_ord (int, float, inf, -inf, 'fro', 'nuc', optional): The type of norm
that is used to compute the
norm of the sensitivity matrix which is defined as the difference
between the explanation function at its input and perturbed input.
Default: 'fro'
max_examples_per_batch (int, optional): The number of maximum input
examples that are processed together. In case the number of
examples (`input batch size * n_perturb_samples`) exceeds
`max_examples_per_batch`, they will be sliced
into batches of `max_examples_per_batch` examples and processed
in a sequential order. If `max_examples_per_batch` is None, all
examples are processed together. `max_examples_per_batch` should
at least be equal `input batch size` and at most
`input batch size * n_perturb_samples`.
Default: None
**kwargs (Any, optional): Contains a list of arguments that are passed
to `explanation_func` explanation function which in some cases
could be the `attribute` function of an attribution algorithm.
Any additional arguments that need be passed to the explanation
function should be included here.
For instance, such arguments include:
`additional_forward_args`, `baselines` and `target`.
Returns:
sensitivities (tensor): A tensor of scalar sensitivity scores per
input example. The first dimension is equal to the
number of examples in the input batch and the second
dimension is one. Returned sensitivities are normalized by
the magnitudes of the input explanations.
Examples::
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
>>> # and returns an Nx10 tensor of class probabilities.
>>> net = ImageClassifier()
>>> saliency = Saliency(net)
>>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
>>> # Computes sensitivity score for saliency maps of class 3
>>> sens = sensitivity_max(saliency.attribute, input, target = 3)
"""
def _generate_perturbations(
current_n_perturb_samples: int, ) -> TensorOrTupleOfTensorsGeneric:
r"""
The perturbations are generated for each example
`current_n_perturb_samples` times.
For perfomance reasons we are not calling `perturb_func` on each example but
on a batch that contains `current_n_perturb_samples` repeated instances
per example.
"""
inputs_expanded: Union[Tensor, Tuple[Tensor, ...]] = tuple(
torch.repeat_interleave(input, current_n_perturb_samples, dim=0)
for input in inputs)
if len(inputs_expanded) == 1:
inputs_expanded = inputs_expanded[0]
return (perturb_func(inputs_expanded, perturb_radius)
if len(signature(perturb_func).parameters) > 1 else
perturb_func(inputs_expanded))
def max_values(input_tnsr: Tensor) -> Tensor:
return torch.max(input_tnsr, dim=1).values # type: ignore
kwarg_expanded_for = None
kwargs_copy: Any = None
def _next_sensitivity_max(current_n_perturb_samples: int) -> Tensor:
inputs_perturbed = _generate_perturbations(current_n_perturb_samples)
# copy kwargs and update some of the arguments that need to be expanded
nonlocal kwarg_expanded_for
nonlocal kwargs_copy
if (kwarg_expanded_for is None
or kwarg_expanded_for != current_n_perturb_samples):
kwarg_expanded_for = current_n_perturb_samples
kwargs_copy = deepcopy(kwargs)
_expand_and_update_additional_forward_args(
current_n_perturb_samples, kwargs_copy)
_expand_and_update_target(current_n_perturb_samples, kwargs_copy)
if "baselines" in kwargs:
baselines = kwargs["baselines"]
baselines = _format_baseline(baselines,
cast(Tuple[Tensor, ...], inputs))
if (isinstance(baselines[0], Tensor)
and baselines[0].shape == inputs[0].shape):
_expand_and_update_baselines(
cast(Tuple[Tensor, ...], inputs),
current_n_perturb_samples,
kwargs_copy,
)
expl_perturbed_inputs = explanation_func(inputs_perturbed,
**kwargs_copy)
# tuplize `expl_perturbed_inputs` in case it is not
expl_perturbed_inputs = _format_tensor_into_tuples(
expl_perturbed_inputs)
expl_inputs_expanded = tuple(
expl_input.repeat_interleave(current_n_perturb_samples, dim=0)
for expl_input in expl_inputs)
sensitivities = torch.cat(
[(expl_input - expl_perturbed).view(expl_perturbed.size(0), -1)
for expl_perturbed, expl_input in zip(expl_perturbed_inputs,
expl_inputs_expanded)],
dim=1,
)
# compute the norm of original input explanations
expl_inputs_norm_expanded = torch.norm(
torch.cat(
[
expl_input.view(expl_input.size(0), -1)
for expl_input in expl_inputs
],
dim=1,
),
p=norm_ord,
dim=1,
keepdim=True,
).repeat_interleave(current_n_perturb_samples, dim=0)
expl_inputs_norm_expanded = torch.where(
expl_inputs_norm_expanded == 0.0,
torch.tensor(
1.0,
device=expl_inputs_norm_expanded.device,
dtype=expl_inputs_norm_expanded.dtype,
),
expl_inputs_norm_expanded,
)
# compute the norm for each input noisy example
sensitivities_norm = (
torch.norm(sensitivities, p=norm_ord, dim=1, keepdim=True) /
expl_inputs_norm_expanded)
return max_values(sensitivities_norm.view(bsz, -1))
inputs = _format_input(inputs) # type: ignore
bsz = inputs[0].size(0)
with torch.no_grad():
expl_inputs = explanation_func(inputs, **kwargs)
metrics_max = _divide_and_aggregate_metrics(
cast(Tuple[Tensor, ...], inputs),
n_perturb_samples,
_next_sensitivity_max,
max_examples_per_batch=max_examples_per_batch,
agg_func=torch.max,
)
return metrics_max
def _format_input_baseline(
inputs: Union[Tensor, Tuple[Tensor, ...]], baselines: BaselineType
) -> Tuple[Tuple[Tensor, ...], Tuple[Union[Tensor, int, float], ...]]:
inputs = _format_input(inputs)
baselines = _format_baseline(baselines, inputs)
return inputs, baselines
def attribute(
self,
inputs: Union[Tensor, Tuple[Tensor, ...]],
target: TargetType = None,
additional_forward_args: Any = None,
attribute_to_layer_input: bool = False,
) -> Union[Tensor, Tuple[Tensor, ...], List[Union[Tensor, Tuple[Tensor, ...]]]]:
r"""
Args:
inputs (tensor or tuple of tensors): Input for which attributions
are 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, 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 forward_func in order following the
arguments in inputs.
Note that attributions are not computed with respect
to these arguments.
Default: None
attribute_to_layer_input (bool, optional): 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.
Default: False
Returns:
*tensor* or tuple of *tensors* or *list* of **attributions**:
- **attributions** (*tensor* or tuple of *tensors* or *list*):
Product of gradient and activation for each
neuron in given layer output.
Attributions will always be the same size as the
output of the given layer.
Attributions are returned in a tuple if
the layer inputs / outputs contain multiple tensors,
otherwise a single tensor is returned.
If multiple layers are provided, attributions
are returned as a list, each element corresponding to the
activations of the corresponding layer.
Examples::
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
>>> # and returns an Nx10 tensor of class probabilities.
>>> # It contains an attribute conv1, which is an instance of nn.conv2d,
>>> # and the output of this layer has dimensions Nx12x32x32.
>>> net = ImageClassifier()
>>> layer_ga = LayerGradientXActivation(net, net.conv1)
>>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
>>> # Computes layer activation x gradient for class 3.
>>> # attribution size matches layer output, Nx12x32x32
>>> attribution = layer_ga.attribute(input, 3)
"""
inputs = _format_input(inputs)
additional_forward_args = _format_additional_forward_args(
additional_forward_args
)
gradient_mask = apply_gradient_requirements(inputs)
# Returns gradient of output with respect to
# hidden layer and hidden layer evaluated at each input.
layer_gradients, layer_evals = compute_layer_gradients_and_eval(
self.forward_func,
self.layer,
inputs,
target,
additional_forward_args,
device_ids=self.device_ids,
attribute_to_layer_input=attribute_to_layer_input,
)
undo_gradient_requirements(inputs, gradient_mask)
if isinstance(self.layer, Module):
return _format_output(
len(layer_evals) > 1,
self.multiply_gradient_acts(layer_gradients, layer_evals),
)
else:
return [
_format_output(
len(layer_evals[i]) > 1,
self.multiply_gradient_acts(layer_gradients[i], layer_evals[i]),
)
for i in range(len(self.layer))
]
def jit_test_assert(self) -> None:
model_1 = model
attr_args = args
if (mode is JITCompareMode.data_parallel_jit_trace
or JITCompareMode.data_parallel_jit_script):
if not torch.cuda.is_available() or torch.cuda.device_count(
) == 0:
raise unittest.SkipTest(
"Skipping GPU test since CUDA not available.")
# Construct cuda_args, moving all tensor inputs in args to CUDA device
cuda_args = {}
for key in args:
if isinstance(args[key], Tensor):
cuda_args[key] = args[key].cuda()
elif isinstance(args[key], tuple):
cuda_args[key] = tuple(
elem.cuda() if isinstance(elem, Tensor) else elem
for elem in args[key])
else:
cuda_args[key] = args[key]
attr_args = cuda_args
model_1 = model_1.cuda()
# Initialize models based on JITCompareMode
if (mode is JITCompareMode.cpu_jit_script
or JITCompareMode.data_parallel_jit_script):
model_2 = torch.jit.script(model_1) # type: ignore
elif (mode is JITCompareMode.cpu_jit_trace
or JITCompareMode.data_parallel_jit_trace):
all_inps = _format_input(args["inputs"]) + (
_format_additional_forward_args(
args["additional_forward_args"])
if "additional_forward_args" in args and
args["additional_forward_args"] is not None else tuple())
model_2 = torch.jit.trace(model_1, all_inps) # type: ignore
else:
raise AssertionError("JIT compare mode type is not valid.")
attr_method_1 = algorithm(model_1)
attr_method_2 = algorithm(model_2)
if noise_tunnel:
attr_method_1 = NoiseTunnel(attr_method_1)
attr_method_2 = NoiseTunnel(attr_method_2)
if attr_method_1.has_convergence_delta():
attributions_1, delta_1 = attr_method_1.attribute(
return_convergence_delta=True, **attr_args)
self.setUp()
attributions_2, delta_2 = attr_method_2.attribute(
return_convergence_delta=True, **attr_args)
assertTensorTuplesAlmostEqual(self,
attributions_1,
attributions_2,
mode="max")
assertTensorTuplesAlmostEqual(self,
delta_1,
delta_2,
mode="max")
else:
attributions_1 = attr_method_1.attribute(**attr_args)
self.setUp()
attributions_2 = attr_method_2.attribute(**attr_args)
assertTensorTuplesAlmostEqual(self,
attributions_1,
attributions_2,
mode="max")
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
target: TargetType = None,
additional_forward_args: Any = None,
interpolate_mode: str = "nearest",
attribute_to_layer_input: bool = False,
) -> TensorOrTupleOfTensorsGeneric:
r"""
Args:
inputs (tensor or tuple of tensors): Input for which attributions
are 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, 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 forward_func in order following the
arguments in inputs.
Note that attributions are not computed with respect
to these arguments.
Default: None
interpolate_mode (str, optional): Method for interpolation, which
must be a valid input interpolation mode for
torch.nn.functional. These methods are
"nearest", "area", "linear" (3D-only), "bilinear"
(4D-only), "bicubic" (4D-only), "trilinear" (5D-only)
based on the number of dimensions of the chosen layer
output (which must also match the number of
dimensions for the input tensor). Note that
the original GradCAM paper uses "bilinear"
interpolation, but we default to "nearest" for
applicability to any of 3D, 4D or 5D tensors.
Default: "nearest"
attribute_to_layer_input (bool, optional): Indicates whether to
compute the attribution with respect to the layer input
or output in `LayerGradCam`.
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:
*tensor* of **attributions**:
- **attributions** (*tensor*):
Element-wise product of (upsampled) GradCAM
and Guided Backprop attributions.
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.
Attributions will be the same size as the provided inputs,
with each value providing the attribution of the
corresponding input index.
If the GradCAM attributions cannot be upsampled to the shape
of a given input tensor, None is returned in the corresponding
index position.
Examples::
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
>>> # and returns an Nx10 tensor of class probabilities.
>>> # It contains an attribute conv4, which is an instance of nn.conv2d,
>>> # and the output of this layer has dimensions Nx50x8x8.
>>> # It is the last convolution layer, which is the recommended
>>> # use case for GuidedGradCAM.
>>> net = ImageClassifier()
>>> guided_gc = GuidedGradCam(net, net.conv4)
>>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
>>> # Computes guided GradCAM attributions for class 3.
>>> # attribution size matches input size, Nx3x32x32
>>> attribution = guided_gc.attribute(input, 3)
"""
is_inputs_tuple = _is_tuple(inputs)
inputs = _format_input(inputs)
grad_cam_attr = self.grad_cam.attribute.__wrapped__(
self.grad_cam, # self
inputs=inputs,
target=target,
additional_forward_args=additional_forward_args,
attribute_to_layer_input=attribute_to_layer_input,
relu_attributions=True,
)
if isinstance(grad_cam_attr, tuple):
assert len(grad_cam_attr) == 1, (
"GuidedGradCAM attributions for layer with multiple inputs / "
"outputs is not supported.")
grad_cam_attr = grad_cam_attr[0]
guided_backprop_attr = self.guided_backprop.attribute.__wrapped__(
self.guided_backprop, # self
inputs=inputs,
target=target,
additional_forward_args=additional_forward_args,
)
output_attr: List[Tensor] = []
for i in range(len(inputs)):
try:
output_attr.append(guided_backprop_attr[i] *
LayerAttribution.interpolate(
grad_cam_attr,
inputs[i].shape[2:],
interpolate_mode=interpolate_mode,
))
except Exception:
warnings.warn(
"Couldn't appropriately interpolate GradCAM attributions for some "
"input tensors, returning empty tensor for corresponding "
"attributions.")
output_attr.append(torch.empty(0))
return _format_output(is_inputs_tuple, tuple(output_attr))
def infidelity(
forward_func: Callable,
perturb_func: Callable,
inputs: TensorOrTupleOfTensorsGeneric,
attributions: TensorOrTupleOfTensorsGeneric,
baselines: BaselineType = None,
additional_forward_args: Any = None,
target: TargetType = None,
n_perturb_samples: int = 10,
max_examples_per_batch: int = None,
normalize: bool = False,
) -> Tensor:
r"""
Explanation infidelity represents the expected mean-squared error
between the explanation multiplied by a meaningful input perturbation
and the differences between the predictor function at its input
and perturbed input.
More details about the measure can be found in the following paper:
https://arxiv.org/pdf/1901.09392.pdf
It is derived from the completeness property of well-known attribution
algorithms and is a computationally more efficient and generalized
notion of Sensitivy-n. The latter measures correlations between the sum
of the attributions and the differences of the predictor function at
its input and fixed baseline. More details about the Sensitivity-n can
be found here:
https://arxiv.org/pdf/1711.06104.pdfs
The users can perturb the inputs any desired way by providing any
perturbation function that takes the inputs (and optionally baselines)
and returns perturbed inputs or perturbed inputs and corresponding
perturbations.
This specific implementation is primarily tested for attribution-based
explanation methods but the idea can be expanded to use for non
attribution-based interpretability methods as well.
Args:
forward_func (callable):
The forward function of the model or any modification of it.
perturb_func (callable):
The perturbation function of model inputs. This function takes
model inputs and optionally baselines as input arguments and returns
either a tuple of perturbations and perturbed inputs or just
perturbed inputs. For example:
>>> def my_perturb_func(inputs):
>>> <MY-LOGIC-HERE>
>>> return perturbations, perturbed_inputs
If we want to only return perturbed inputs and compute
perturbations internally then we can wrap perturb_func with
`infidelity_perturb_func_decorator` decorator such as:
>>> from captum.metrics import infidelity_perturb_func_decorator
>>> @infidelity_perturb_func_decorator(<multipy_by_inputs flag>)
>>> def my_perturb_func(inputs):
>>> <MY-LOGIC-HERE>
>>> return perturbed_inputs
In case `multipy_by_inputs` is False we compute perturbations by
`input - perturbed_input` difference and in case `multipy_by_inputs`
flag is True we compute it by dividing
(input - perturbed_input) by (input - baselines).
The user needs to only return perturbed inputs in `perturb_func`
as described above.
`infidelity_perturb_func_decorator` needs to be used with
`multipy_by_inputs` flag set to False in case infidelity
score is being computed for attribution maps that are local aka
that do not factor in inputs in the final attribution score.
Such attribution algorithms include Saliency, GradCam, Guided Backprop,
or Integrated Gradients and DeepLift attribution scores that are already
computed with `multipy_by_inputs=False` flag.
If there are more than one inputs passed to infidelity function those
will be passed to `perturb_func` as tuples in the same order as they
are passed to infidelity function.
If inputs
- is a single tensor, the function needs to return a tuple
of perturbations and perturbed input such as:
perturb, perturbed_input and only perturbed_input in case
`infidelity_perturb_func_decorator` is used.
- is a tuple of tensors, corresponding perturbations and perturbed
inputs must be computed and returned as tuples in the
following format:
(perturb1, perturb2, ... perturbN), (perturbed_input1,
perturbed_input2, ... perturbed_inputN)
Similar to previous case here as well we need to return only
perturbed inputs in case `infidelity_perturb_func_decorator`
decorates out `perturb_func`.
It is important to note that for performance reasons `perturb_func`
isn't called for each example individually but on a batch of
input examples that are repeated `max_examples_per_batch / batch_size`
times within the batch.
inputs (tensor or tuple of tensors): Input for which
attributions are 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.
baselines (scalar, tensor, tuple of scalars or tensors, optional):
Baselines define reference values which sometimes represent ablated
values and are used to compare with the actual inputs to compute
importance scores in attribution algorithms. They can be represented
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.
- a tuple of tensors or scalars, the baseline corresponding
to each tensor in the inputs' tuple can be:
- either a tensor with matching dimensions to
corresponding tensor in the inputs' tuple
or the first dimension is one and the remaining
dimensions match with the corresponding
input tensor.
- or a scalar, corresponding to a tensor in the
inputs' tuple. This scalar value is broadcasted
for corresponding input tensor.
Default: None
attributions (tensor or tuple of tensors):
Attribution scores computed based on an attribution algorithm.
This attribution scores can be computed using the implementations
provided in the `captum.attr` package. Some of those attribution
approaches are so called global methods, which means that
they factor in model inputs' multiplier, as described in:
https://arxiv.org/pdf/1711.06104.pdf
Many global attribution algorithms can be used in local modes,
meaning that the inputs multiplier isn't factored in the
attribution scores.
This can be done duing the definition of the attribution algorithm
by passing `multipy_by_inputs=False` flag.
For example in case of Integrated Gradients (IG) we can obtain
local attribution scores if we define the constructor of IG as:
ig = IntegratedGradients(multipy_by_inputs=False)
Some attribution algorithms are inherently local.
Examples of inherently local attribution methods include:
Saliency, Guided GradCam, Guided Backprop and Deconvolution.
For local attributions we can use real-valued perturbations
whereas for global attributions that perturbation is binary.
https://arxiv.org/pdf/1901.09392.pdf
If we want to compute the infidelity of global attributions we
can use a binary perturbation matrix that will allow us to select
a subset of features from `inputs` or `inputs - baselines` space.
This will allow us to approximate sensitivity-n for a global
attribution algorithm.
`infidelity_perturb_func_decorator` function decorator is a helper
function that computes perturbations under the hood if perturbed
inputs are provided.
For more details about how to use `infidelity_perturb_func_decorator`,
please, read the documentation about `perturb_func`
Attributions have the same shape and dimensionality as the inputs.
If inputs is a single tensor then the attributions is a single
tensor as well. If inputs is provided as a tuple of tensors
then attributions will be tuples of tensors as well.
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 the perturbations are not computed with respect
to these arguments. This means that these arguments aren't
being passed to `perturb_func` as an input argument.
Default: None
target (int, tuple, tensor or list, optional): Indices for selecting
predictions from output(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
n_perturb_samples (int, optional): The number of times input tensors
are perturbed. Each input example in the inputs tensor is expanded
`n_perturb_samples`
times before calling `perturb_func` function.
Default: 10
max_examples_per_batch (int, optional): The number of maximum input
examples that are processed together. In case the number of
examples (`input batch size * n_perturb_samples`) exceeds
`max_examples_per_batch`, they will be sliced
into batches of `max_examples_per_batch` examples and processed
in a sequential order. If `max_examples_per_batch` is None, all
examples are processed together. `max_examples_per_batch` should
at least be equal `input batch size` and at most
`input batch size * n_perturb_samples`.
Default: None
normalize (bool, optional): Normalize the dot product of the input
perturbation and the attribution so the infidelity value is invariant
to constant scaling of the attribution values. The normalization factor
beta is defined as the ratio of two mean values:
$$ \beta = \frac{
\mathbb{E}_{I \sim \mu_I} [ I^T \Phi(f, x) (f(x) - f(x - I)) ]
}{
\mathbb{E}_{I \sim \mu_I} [ (I^T \Phi(f, x))^2 ]
} $$.
Please refer the original paper for the meaning of the symbols. Same
normalization can be found in the paper's official implementation
https://github.com/chihkuanyeh/saliency_evaluation
Default: False
Returns:
infidelities (tensor): A tensor of scalar infidelity scores per
input example. The first dimension is equal to the
number of examples in the input batch and the second
dimension is one.
Examples::
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
>>> # and returns an Nx10 tensor of class probabilities.
>>> net = ImageClassifier()
>>> saliency = Saliency(net)
>>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
>>> # Computes saliency maps for class 3.
>>> attribution = saliency.attribute(input, target=3)
>>> # define a perturbation function for the input
>>> def perturb_fn(inputs):
>>> noise = torch.tensor(np.random.normal(0, 0.003, inputs.shape)).float()
>>> return noise, inputs - noise
>>> # Computes infidelity score for saliency maps
>>> infid = infidelity(net, perturb_fn, input, attribution)
"""
def _generate_perturbations(
current_n_perturb_samples: int,
) -> Tuple[TensorOrTupleOfTensorsGeneric, TensorOrTupleOfTensorsGeneric]:
r"""
The perturbations are generated for each example
`current_n_perturb_samples` times.
For performance reasons we are not calling `perturb_func` on each example but
on a batch that contains `current_n_perturb_samples`
repeated instances per example.
"""
def call_perturb_func():
r""" """
baselines_pert = None
inputs_pert: Union[Tensor, Tuple[Tensor, ...]]
if len(inputs_expanded) == 1:
inputs_pert = inputs_expanded[0]
if baselines_expanded is not None:
baselines_pert = cast(Tuple, baselines_expanded)[0]
else:
inputs_pert = inputs_expanded
baselines_pert = baselines_expanded
return (
perturb_func(inputs_pert, baselines_pert)
if baselines_pert is not None
else perturb_func(inputs_pert)
)
inputs_expanded = tuple(
torch.repeat_interleave(input, current_n_perturb_samples, dim=0)
for input in inputs
)
baselines_expanded = baselines
if baselines is not None:
baselines_expanded = tuple(
baseline.repeat_interleave(current_n_perturb_samples, dim=0)
if isinstance(baseline, torch.Tensor)
and baseline.shape[0] == input.shape[0]
and baseline.shape[0] > 1
else baseline
for input, baseline in zip(inputs, cast(Tuple, baselines))
)
return call_perturb_func()
def _validate_inputs_and_perturbations(
inputs: Tuple[Tensor, ...],
inputs_perturbed: Tuple[Tensor, ...],
perturbations: Tuple[Tensor, ...],
) -> None:
# asserts the sizes of the perturbations and inputs
assert len(perturbations) == len(inputs), (
"""The number of perturbed
inputs and corresponding perturbations must have the same number of
elements. Found number of inputs is: {} and perturbations:
{}"""
).format(len(perturbations), len(inputs))
# asserts the shapes of the perturbations and perturbed inputs
for perturb, input_perturbed in zip(perturbations, inputs_perturbed):
assert perturb[0].shape == input_perturbed[0].shape, (
"""Perturbed input
and corresponding perturbation must have the same shape and
dimensionality. Found perturbation shape is: {} and the input shape
is: {}"""
).format(perturb[0].shape, input_perturbed[0].shape)
def _next_infidelity_tensors(
current_n_perturb_samples: int,
) -> Union[Tuple[Tensor], Tuple[Tensor, Tensor, Tensor]]:
perturbations, inputs_perturbed = _generate_perturbations(
current_n_perturb_samples
)
perturbations = _format_tensor_into_tuples(perturbations)
inputs_perturbed = _format_tensor_into_tuples(inputs_perturbed)
_validate_inputs_and_perturbations(
cast(Tuple[Tensor, ...], inputs),
cast(Tuple[Tensor, ...], inputs_perturbed),
cast(Tuple[Tensor, ...], perturbations),
)
targets_expanded = _expand_target(
target,
current_n_perturb_samples,
expansion_type=ExpansionTypes.repeat_interleave,
)
additional_forward_args_expanded = _expand_additional_forward_args(
additional_forward_args,
current_n_perturb_samples,
expansion_type=ExpansionTypes.repeat_interleave,
)
inputs_perturbed_fwd = _run_forward(
forward_func,
inputs_perturbed,
targets_expanded,
additional_forward_args_expanded,
)
inputs_fwd = _run_forward(forward_func, inputs, target, additional_forward_args)
inputs_fwd = torch.repeat_interleave(
inputs_fwd, current_n_perturb_samples, dim=0
)
perturbed_fwd_diffs = inputs_fwd - inputs_perturbed_fwd
attributions_expanded = tuple(
torch.repeat_interleave(attribution, current_n_perturb_samples, dim=0)
for attribution in attributions
)
attributions_times_perturb = tuple(
(attribution_expanded * perturbation).view(attribution_expanded.size(0), -1)
for attribution_expanded, perturbation in zip(
attributions_expanded, perturbations
)
)
attr_times_perturb_sums = sum(
torch.sum(attribution_times_perturb, dim=1)
for attribution_times_perturb in attributions_times_perturb
)
attr_times_perturb_sums = cast(Tensor, attr_times_perturb_sums)
# reshape as Tensor(bsz, current_n_perturb_samples)
attr_times_perturb_sums = attr_times_perturb_sums.view(bsz, -1)
perturbed_fwd_diffs = perturbed_fwd_diffs.view(bsz, -1)
if normalize:
# in order to normalize, we have to aggregate the following tensors
# to calculate MSE in its polynomial expansion:
# (a-b)^2 = a^2 - 2ab + b^2
return (
attr_times_perturb_sums.pow(2).sum(-1),
(attr_times_perturb_sums * perturbed_fwd_diffs).sum(-1),
perturbed_fwd_diffs.pow(2).sum(-1),
)
else:
# returns (a-b)^2 if no need to normalize
return ((attr_times_perturb_sums - perturbed_fwd_diffs).pow(2).sum(-1),)
def _sum_infidelity_tensors(agg_tensors, tensors):
return tuple(agg_t + t for agg_t, t in zip(agg_tensors, tensors))
# perform argument formattings
inputs = _format_input(inputs) # type: ignore
if baselines is not None:
baselines = _format_baseline(baselines, cast(Tuple[Tensor, ...], inputs))
additional_forward_args = _format_additional_forward_args(additional_forward_args)
attributions = _format_tensor_into_tuples(attributions) # type: ignore
# Make sure that inputs and corresponding attributions have matching sizes.
assert len(inputs) == len(attributions), (
"""The number of tensors in the inputs and
attributions must match. Found number of tensors in the inputs is: {} and in the
attributions: {}"""
).format(len(inputs), len(attributions))
for inp, attr in zip(inputs, attributions):
assert inp.shape == attr.shape, (
"""Inputs and attributions must have
matching shapes. One of the input tensor's shape is {} and the
attribution tensor's shape is: {}"""
).format(inp.shape, attr.shape)
bsz = inputs[0].size(0)
with torch.no_grad():
# if not normalize, directly return aggrgated MSE ((a-b)^2,)
# else return aggregated MSE's polynomial expansion tensors (a^2, ab, b^2)
agg_tensors = _divide_and_aggregate_metrics(
cast(Tuple[Tensor, ...], inputs),
n_perturb_samples,
_next_infidelity_tensors,
agg_func=_sum_infidelity_tensors,
max_examples_per_batch=max_examples_per_batch,
)
if normalize:
beta_num = agg_tensors[1]
beta_denorm = agg_tensors[0]
beta = safe_div(
beta_num,
beta_denorm,
torch.tensor(1.0, dtype=beta_denorm.dtype, device=beta_denorm.device),
)
infidelity_values = (
beta ** 2 * agg_tensors[0] - 2 * beta * agg_tensors[1] + agg_tensors[2]
)
else:
infidelity_values = agg_tensors[0]
infidelity_values /= n_perturb_samples
return infidelity_values
def perturb(
self,
inputs: TensorOrTupleOfTensorsGeneric,
radius: float,
step_size: float,
step_num: int,
target: Any,
additional_forward_args: Any = None,
targeted: bool = False,
random_start: bool = False,
norm: str = "Linf",
) -> TensorOrTupleOfTensorsGeneric:
r"""
This method computes and returns the perturbed input for each input tensor.
It supports both targeted and non-targeted attacks.
Args:
inputs (tensor or tuple of tensors): Input for which adversarial
attack is computed. It can be provided as a single
tensor or a tuple of multiple tensors. If multiple
input tensors are provided, the batch sizes must be
aligned accross all tensors.
radius (float): Radius of the neighbor ball centered around inputs.
The perturbation should be within this range.
step_size (float): Step size of each gradient step.
step_num (int): Step numbers. It usually guarantees that the perturbation
can reach the border.
target (any): True labels of inputs if non-targeted attack is
desired. Target class of inputs if targeted attack
is desired. Target will be passed to the loss function
to compute loss, so the type needs to match the
argument type of the loss function.
If using the default negative log as loss function,
labels should be of type int, tuple, tensor or list.
For general 2D outputs, labels 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 label for the corresponding example.
For outputs with > 2 dimensions, labels can be either:
- A single tuple, which contains #output_dims - 1
elements. This label 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
label for the corresponding example.
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. These arguments are provided to
forward_func in order following the arguments in inputs.
Default: None.
targeted (bool, optional): If attack should be targeted.
Default: False.
random_start (bool, optional): If a random initialization is added to
inputs. Default: False.
norm (str, optional): Specifies the norm to calculate distance from
original inputs: 'Linf'|'L2'.
Default: 'Linf'.
Returns:
- **perturbed inputs** (*tensor* or tuple of *tensors*):
Perturbed input for each
input tensor. The perturbed inputs have the same shape and
dimensionality as the inputs.
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.
"""
def _clip(inputs: Tensor, outputs: Tensor) -> Tensor:
diff = outputs - inputs
if norm == "Linf":
return inputs + torch.clamp(diff, -radius, radius)
elif norm == "L2":
return inputs + torch.renorm(diff, 2, 0, radius)
else:
raise AssertionError("Norm constraint must be L2 or Linf.")
is_inputs_tuple = _is_tuple(inputs)
formatted_inputs = _format_input(inputs)
perturbed_inputs = formatted_inputs
if random_start:
perturbed_inputs = tuple(
self.bound(
self._random_point(formatted_inputs[i], radius, norm))
for i in range(len(formatted_inputs)))
for _i in range(step_num):
perturbed_inputs = self.fgsm.perturb(perturbed_inputs, step_size,
target,
additional_forward_args,
targeted)
perturbed_inputs = tuple(
_clip(formatted_inputs[j], perturbed_inputs[j])
for j in range(len(perturbed_inputs)))
# Detaching inputs to avoid dependency of gradient between steps
perturbed_inputs = tuple(
self.bound(perturbed_inputs[j]).detach()
for j in range(len(perturbed_inputs)))
return _format_output(is_inputs_tuple, perturbed_inputs)
def attribute(
self,
inputs: Union[Tensor, Tuple[Tensor, ...]],
baselines: Union[Tensor, Tuple[Tensor, ...],
Callable[..., Union[Tensor, Tuple[Tensor, ...]]]],
target: TargetType = None,
additional_forward_args: Any = None,
return_convergence_delta: bool = False,
attribute_to_layer_input: bool = False,
custom_attribution_func: Union[None, Callable[..., Tuple[Tensor,
...]]] = None,
) -> Union[Tensor, Tuple[Tensor, ...], Tuple[Union[Tensor, Tuple[
Tensor, ...]], Tensor]]:
r"""
Args:
inputs (tensor or tuple of tensors): Input for which layer
attributions are 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.
baselines (tensor, tuple of tensors, callable):
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
the first dimension equal to the number of examples
in the baselines' distribution. The remaining dimensions
must match with input tensor's dimension starting from
the second dimension.
- a tuple of tensors, if inputs is a tuple of tensors,
with the first dimension of any tensor inside the tuple
equal to the number of examples in the baseline's
distribution. The remaining dimensions must match
the dimensions of the corresponding input tensor
starting from the second dimension.
- callable function, optionally takes `inputs` as an
argument and either returns a single tensor
or a tuple of those.
It is recommended that the number of samples in the baselines'
tensors is larger than one.
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
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
attribute_to_layer_input (bool, optional): Indicates whether to
compute the attributions 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 assumes that both the inputs and
outputs of internal layers are single tensors.
Support for multiple tensors will be added later.
Default: False
custom_attribution_func (callable, optional): 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
Returns:
**attributions** or 2-element tuple of **attributions**, **delta**:
- **attributions** (*tensor* or tuple of *tensors*):
Attribution score computed based on DeepLift's rescale rule
with respect to layer's inputs or outputs. Attributions
will always be the same size as the provided layer's inputs
or outputs, depending on whether we attribute to the inputs
or outputs of the layer.
Attributions are returned in a tuple based on whether
the layer inputs / outputs are contained in a tuple
from a forward hook. For standard modules, inputs of
a single tensor are usually wrapped in a tuple, while
outputs of a single tensor are not.
- **delta** (*tensor*, returned if return_convergence_delta=True):
This is computed using the property that the
total sum of forward_func(inputs) - forward_func(baselines)
must be very close to the total sum of attributions
computed based on approximated SHAP values using
DeepLift's rescale rule.
Delta is calculated for each example input and baseline pair,
meaning that the number of elements in returned delta tensor
is equal to the
`number of examples in input` * `number of examples
in baseline`. The deltas are ordered in the first place by
input example, followed by the baseline.
Note that the logic described for deltas is guaranteed
when the default logic for attribution computations is used,
meaning that the `custom_attribution_func=None`, otherwise
it is not guaranteed and depends on the specifics of the
`custom_attribution_func`.
Examples::
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
>>> # and returns an Nx10 tensor of class probabilities.
>>> net = ImageClassifier()
>>> # creates an instance of LayerDeepLift to interpret target
>>> # class 1 with respect to conv4 layer.
>>> dl = LayerDeepLiftShap(net, net.conv4)
>>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
>>> # Computes shap values using deeplift for class 3.
>>> attribution = dl.attribute(input, target=3)
"""
inputs = _format_input(inputs)
baselines = _format_callable_baseline(baselines, inputs)
assert isinstance(
baselines[0], torch.Tensor
) and baselines[0].shape[0] > 1, (
"Baselines distribution has to be provided in form of a torch.Tensor"
" with more than one example but found: {}."
" If baselines are provided in shape of scalars or with a single"
" baseline example, `LayerDeepLift`"
" approach can be used instead.".format(baselines[0]))
# batch sizes
inp_bsz = inputs[0].shape[0]
base_bsz = baselines[0].shape[0]
(
exp_inp,
exp_base,
exp_target,
exp_addit_args,
) = DeepLiftShap._expand_inputs_baselines_targets(
self, baselines, inputs, target, additional_forward_args)
attributions = LayerDeepLift.attribute.__wrapped__( # type: ignore
self,
exp_inp,
exp_base,
target=exp_target,
additional_forward_args=exp_addit_args,
return_convergence_delta=cast(Literal[True, False],
return_convergence_delta),
attribute_to_layer_input=attribute_to_layer_input,
custom_attribution_func=custom_attribution_func,
)
if return_convergence_delta:
attributions, delta = attributions
if isinstance(attributions, tuple):
attributions = tuple(
DeepLiftShap._compute_mean_across_baselines(
self, inp_bsz, base_bsz, cast(Tensor, attrib))
for attrib in attributions)
else:
attributions = DeepLiftShap._compute_mean_across_baselines(
self, inp_bsz, base_bsz, attributions)
if return_convergence_delta:
return attributions, delta
else:
return attributions
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
baselines: BaselineType = None,
target: TargetType = None,
additional_forward_args: Any = None,
feature_mask: Union[None, Tensor, Tuple[Tensor, ...]] = None,
perturbations_per_eval: int = 1,
**kwargs: Any
) -> TensorOrTupleOfTensorsGeneric:
r"""
Args:
inputs (tensor or tuple of tensors): Input for which ablation
attributions are 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.
baselines (scalar, tensor, tuple of scalars or tensors, optional):
Baselines define reference value which replaces each
feature when ablated.
Baselines can be provided as:
- a single tensor, if inputs is a single tensor, with
exactly the same dimensions as inputs or
broadcastable to match the dimensions of inputs
- a single scalar, if inputs is a single tensor, which will
be broadcasted for each input value in input tensor.
- a tuple of tensors or scalars, the baseline corresponding
to each tensor in the inputs' tuple can be:
- either a tensor with matching dimensions to
corresponding tensor in the inputs' tuple
or the first dimension is one and the remaining
dimensions match with the corresponding
input tensor.
- or a scalar, corresponding to a tensor in the
inputs' tuple. This scalar value is broadcasted
for corresponding input tensor.
In the cases when `baselines` is not provided, we internally
use zero scalar corresponding to each input tensor.
Default: None
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 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. 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
feature_mask (tensor or tuple of tensors, optional):
feature_mask defines a mask for the input, grouping
features which should be ablated together. feature_mask
should contain the same number of tensors as inputs.
Each tensor should
be the same size as the corresponding input or
broadcastable to match the input tensor. Each tensor
should contain integers in the range 0 to num_features
- 1, and indices corresponding to the same feature should
have the same value.
Note that features within each input tensor are ablated
independently (not across tensors).
If the forward function returns a single scalar per batch,
we enforce that the first dimension of each mask must be 1,
since attributions are returned batch-wise rather than per
example, so the attributions must correspond to the
same features (indices) in each input example.
If None, then a feature mask is constructed which assigns
each scalar within a tensor as a separate feature, which
is ablated independently.
Default: None
perturbations_per_eval (int, optional): Allows ablation of multiple
features to be processed simultaneously in one call to
forward_fn.
Each forward pass will contain a maximum of
perturbations_per_eval * #examples samples.
For DataParallel models, each batch is split among the
available devices, so evaluations on each available
device contain at most
(perturbations_per_eval * #examples) / num_devices
samples.
If the forward function's number of outputs does not
change as the batch size grows (e.g. if it outputs a
scalar value), you must set perturbations_per_eval to 1
and use a single feature mask to describe the features
for all examples in the batch.
Default: 1
**kwargs (Any, optional): Any additional arguments used by child
classes of FeatureAblation (such as Occlusion) to construct
ablations. These arguments are ignored when using
FeatureAblation directly.
Default: None
Returns:
*tensor* or tuple of *tensors* of **attributions**:
- **attributions** (*tensor* or tuple of *tensors*):
The attributions with respect to each input feature.
If the forward function returns
a scalar value per example, attributions will be
the same size as the provided inputs, with each value
providing the attribution of the corresponding input index.
If the forward function returns a scalar per batch, then
attribution tensor(s) will have first dimension 1 and
the remaining dimensions will match the input.
If a single tensor is provided as inputs, a single tensor is
returned. If a tuple of tensors is provided for inputs, a
tuple of corresponding sized tensors is returned.
Examples::
>>> # SimpleClassifier takes a single input tensor of size Nx4x4,
>>> # and returns an Nx3 tensor of class probabilities.
>>> net = SimpleClassifier()
>>> # Generating random input with size 2 x 4 x 4
>>> input = torch.randn(2, 4, 4)
>>> # Defining FeatureAblation interpreter
>>> ablator = FeatureAblation(net)
>>> # Computes ablation attribution, ablating each of the 16
>>> # scalar input independently.
>>> attr = ablator.attribute(input, target=1)
>>> # Alternatively, we may want to ablate features in groups, e.g.
>>> # grouping each 2x2 square of the inputs and ablating them together.
>>> # This can be done by creating a feature mask as follows, which
>>> # defines the feature groups, e.g.:
>>> # +---+---+---+---+
>>> # | 0 | 0 | 1 | 1 |
>>> # +---+---+---+---+
>>> # | 0 | 0 | 1 | 1 |
>>> # +---+---+---+---+
>>> # | 2 | 2 | 3 | 3 |
>>> # +---+---+---+---+
>>> # | 2 | 2 | 3 | 3 |
>>> # +---+---+---+---+
>>> # With this mask, all inputs with the same value are ablated
>>> # simultaneously, and the attribution for each input in the same
>>> # group (0, 1, 2, and 3) per example are the same.
>>> # The attributions can be calculated as follows:
>>> # feature mask has dimensions 1 x 4 x 4
>>> feature_mask = torch.tensor([[[0,0,1,1],[0,0,1,1],
>>> [2,2,3,3],[2,2,3,3]]])
>>> attr = ablator.attribute(input, target=1, feature_mask=feature_mask)
"""
# Keeps track whether original input is a tuple or not before
# converting it into a tuple.
is_inputs_tuple = _is_tuple(inputs)
inputs, baselines = _format_input_baseline(inputs, baselines)
additional_forward_args = _format_additional_forward_args(
additional_forward_args
)
num_examples = inputs[0].shape[0]
feature_mask = _format_input(feature_mask) if feature_mask is not None else None
assert (
isinstance(perturbations_per_eval, int) and perturbations_per_eval >= 1
), "Perturbations per evaluation must be an integer and at least 1."
with torch.no_grad():
# Computes initial evaluation with all features, which is compared
# to each ablated result.
initial_eval = _run_forward(
self.forward_func, inputs, target, additional_forward_args
)
agg_output_mode = FeatureAblation._find_output_mode(
perturbations_per_eval, feature_mask
)
# get as a 2D tensor (if it is not a scalar)
if isinstance(initial_eval, torch.Tensor):
initial_eval = initial_eval.reshape(1, -1)
num_outputs = initial_eval.shape[1]
else:
num_outputs = 1
if not agg_output_mode:
assert (
isinstance(initial_eval, torch.Tensor)
and num_outputs == num_examples
), (
"expected output of `forward_func` to have "
+ "`batch_size` elements for perturbations_per_eval > 1 "
+ "and all feature_mask.shape[0] > 1"
)
# Initialize attribution totals and counts
attrib_type = cast(
dtype,
initial_eval.dtype
if isinstance(initial_eval, Tensor)
else type(initial_eval),
)
total_attrib = [
torch.zeros(
(num_outputs,) + input.shape[1:],
dtype=attrib_type,
device=input.device,
)
for input in inputs
]
# Weights are used in cases where ablations may be overlapping.
if self.use_weights:
weights = [
torch.zeros(
(num_outputs,) + input.shape[1:], device=input.device
).float()
for input in inputs
]
# Iterate through each feature tensor for ablation
for i in range(len(inputs)):
# Skip any empty input tensors
if torch.numel(inputs[i]) == 0:
continue
for (
current_inputs,
current_add_args,
current_target,
current_mask,
) in self._ablation_generator(
i,
inputs,
additional_forward_args,
target,
baselines,
feature_mask,
perturbations_per_eval,
**kwargs
):
# modified_eval dimensions: 1D tensor with length
# equal to #num_examples * #features in batch
modified_eval = _run_forward(
self.forward_func,
current_inputs,
current_target,
current_add_args,
)
# (contains 1 more dimension than inputs). This adds extra
# dimensions of 1 to make the tensor broadcastable with the inputs
# tensor.
if not isinstance(modified_eval, torch.Tensor):
eval_diff = initial_eval - modified_eval
else:
if not agg_output_mode:
assert (
modified_eval.numel() == current_inputs[0].shape[0]
), """expected output of forward_func to grow with
batch_size. If this is not the case for your model
please set perturbations_per_eval = 1"""
eval_diff = (
initial_eval - modified_eval.reshape((-1, num_outputs))
).reshape((-1, num_outputs) + (len(inputs[i].shape) - 1) * (1,))
if self.use_weights:
weights[i] += current_mask.float().sum(dim=0)
total_attrib[i] += (eval_diff * current_mask.to(attrib_type)).sum(
dim=0
)
# Divide total attributions by counts and return formatted attributions
if self.use_weights:
attrib = tuple(
single_attrib.float() / weight
for single_attrib, weight in zip(total_attrib, weights)
)
else:
attrib = tuple(total_attrib)
_result = _format_output(is_inputs_tuple, attrib)
return _result
