def test_layer_gradient_output(self) -> None:
model = BasicModel_MultiLayer()
input = torch.tensor([[5.0, 2.0, 1.0]], requires_grad=True)
grads, eval = compute_layer_gradients_and_eval(model,
model.linear2,
input,
target_ind=1)
assertArraysAlmostEqual(grads[0].squeeze(0).tolist(), [0.0, 1.0],
delta=0.01)
assertArraysAlmostEqual(eval[0].squeeze(0).tolist(), [26.0, 28.0],
delta=0.01)
def test_layer_gradient_linear1_inplace(self) -> None:
model = BasicModel_MultiLayer(inplace=True)
input = torch.tensor([[5.0, 2.0, 1.0]], requires_grad=True)
grads, eval, is_layer_tuple = compute_layer_gradients_and_eval(
model, model.linear1, input, target_ind=1)
assertArraysAlmostEqual(grads[0].squeeze(0).tolist(),
[0.0, 1.0, 1.0, 1.0],
delta=0.01)
assertArraysAlmostEqual(eval[0].squeeze(0).tolist(),
[-2.0, 9.0, 9.0, 9.0],
delta=0.01)
self.assertFalse(is_layer_tuple,
("Layer output should not be wrapped in "
"a tuple."))
def test_layer_gradient_relu_input_inplace(self) -> None:
model = BasicModel_MultiLayer(inplace=True)
input = torch.tensor([[5.0, 2.0, 1.0]], requires_grad=True)
grads, eval = compute_layer_gradients_and_eval(
model,
model.relu,
input,
target_ind=1,
attribute_to_layer_input=True)
assertArraysAlmostEqual(grads[0].squeeze(0).tolist(),
[0.0, 1.0, 1.0, 1.0],
delta=0.01)
assertArraysAlmostEqual(eval[0].squeeze(0).tolist(),
[-2.0, 9.0, 9.0, 9.0],
delta=0.01)
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 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: Tuple[Tensor, ...],
neuron_selector: Union[int, Tuple[int, ...], Callable],
baselines: Tuple[Union[Tensor, int, float], ...],
target: TargetType = None,
additional_forward_args: Any = None,
n_steps: int = 50,
method: str = "riemann_trapezoid",
attribute_to_neuron_input: bool = False,
step_sizes_and_alphas: Union[None, Tuple[List[float],
List[float]]] = None,
) -> Tuple[Tensor, ...]:
num_examples = inputs[0].shape[0]
total_batch = num_examples * n_steps
if step_sizes_and_alphas is None:
# retrieve step size and scaling factor for specified approximation method
step_sizes_func, alphas_func = approximation_parameters(method)
step_sizes, alphas = step_sizes_func(n_steps), alphas_func(n_steps)
else:
step_sizes, alphas = step_sizes_and_alphas
# Compute scaled inputs from baseline to final input.
scaled_features_tpl = tuple(
torch.cat(
[baseline + alpha * (input - baseline) for alpha in alphas],
dim=0).requires_grad_()
for input, baseline in zip(inputs, baselines))
additional_forward_args = _format_additional_forward_args(
additional_forward_args)
# apply number of steps to additional forward args
# currently, number of steps is applied only to additional forward arguments
# that are nd-tensors. It is assumed that the first dimension is
# the number of batches.
# dim -> (#examples * #steps x additional_forward_args[0].shape[1:], ...)
input_additional_args = (_expand_additional_forward_args(
additional_forward_args, n_steps) if additional_forward_args
is not None else None)
expanded_target = _expand_target(target, n_steps)
# Conductance Gradients - Returns gradient of output with respect to
# hidden layer and hidden layer evaluated at each input.
layer_gradients, layer_eval, input_grads = compute_layer_gradients_and_eval(
forward_fn=self.forward_func,
layer=self.layer,
inputs=scaled_features_tpl,
target_ind=expanded_target,
additional_forward_args=input_additional_args,
gradient_neuron_selector=neuron_selector,
device_ids=self.device_ids,
attribute_to_layer_input=attribute_to_neuron_input,
)
mid_grads = _verify_select_neuron(layer_gradients, neuron_selector)
scaled_input_gradients = tuple(
input_grad * mid_grads.reshape((total_batch, ) + (1, ) *
(len(input_grad.shape) - 1))
for input_grad in input_grads)
# Mutliplies by appropriate step size.
scaled_grads = tuple(
scaled_input_gradient.contiguous().view(n_steps, -1) *
torch.tensor(step_sizes).view(n_steps, 1).to(
scaled_input_gradient.device)
for scaled_input_gradient in scaled_input_gradients)
# Aggregates across all steps for each tensor in the input tuple
total_grads = tuple(
_reshape_and_sum(scaled_grad, n_steps, num_examples,
input_grad.shape[1:])
for (scaled_grad, input_grad) in zip(scaled_grads, input_grads))
if self.multiplies_by_inputs:
# computes attribution for each tensor in input tuple
# attributions has the same dimensionality as inputs
attributions = tuple(total_grad * (input - baseline)
for total_grad, input, baseline in zip(
total_grads, inputs, baselines))
else:
attributions = total_grads
return attributions
def attribute( # type: ignore
self,
inputs: Union[Tensor, Tuple[Tensor, ...]],
baselines: Union[Tensor, Tuple[Tensor, ...]],
target: TargetType = None,
additional_forward_args: Any = None,
return_convergence_delta: bool = False,
attribute_to_layer_input: bool = False,
) -> Union[Tensor, Tuple[Tensor, ...], Tuple[Union[Tensor, Tuple[
Tensor, ...]], Tensor]]:
inputs, baselines = _format_input_baseline(inputs, baselines)
rand_coefficient = torch.tensor(
np.random.uniform(0.0, 1.0, inputs[0].shape[0]),
device=inputs[0].device,
dtype=inputs[0].dtype,
)
input_baseline_scaled = tuple(
_scale_input(input, baseline, rand_coefficient)
for input, baseline in zip(inputs, baselines))
grads, _ = compute_layer_gradients_and_eval(
self.forward_func,
self.layer,
input_baseline_scaled,
target,
additional_forward_args,
device_ids=self.device_ids,
attribute_to_layer_input=attribute_to_layer_input,
)
attr_baselines = _forward_layer_eval(
self.forward_func,
baselines,
self.layer,
additional_forward_args=additional_forward_args,
device_ids=self.device_ids,
attribute_to_layer_input=attribute_to_layer_input,
)
attr_inputs = _forward_layer_eval(
self.forward_func,
inputs,
self.layer,
additional_forward_args=additional_forward_args,
device_ids=self.device_ids,
attribute_to_layer_input=attribute_to_layer_input,
)
if self.multiplies_by_inputs:
input_baseline_diffs = tuple(
input - baseline
for input, baseline in zip(attr_inputs, attr_baselines))
attributions = tuple(input_baseline_diff * grad
for input_baseline_diff, grad in zip(
input_baseline_diffs, grads))
else:
attributions = grads
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: Tuple[Tensor, ...],
baselines: Tuple[Union[Tensor, int, float], ...],
target: TargetType = None,
additional_forward_args: Any = None,
n_steps: int = 50,
method: str = "gausslegendre",
attribute_to_layer_input: bool = False,
step_sizes_and_alphas: Union[None, Tuple[List[float],
List[float]]] = None,
) -> Union[Tensor, Tuple[Tensor, ...]]:
if step_sizes_and_alphas is None:
# retrieve step size and scaling factor for specified approximation method
step_sizes_func, alphas_func = approximation_parameters(method)
step_sizes, alphas = step_sizes_func(n_steps), alphas_func(n_steps)
else:
step_sizes, alphas = step_sizes_and_alphas
# Compute scaled inputs from baseline to final input.
scaled_features_tpl = tuple(
torch.cat(
[baseline + alpha * (input - baseline) for alpha in alphas],
dim=0).requires_grad_()
for input, baseline in zip(inputs, baselines))
additional_forward_args = _format_additional_forward_args(
additional_forward_args)
# apply number of steps to additional forward args
# currently, number of steps is applied only to additional forward arguments
# that are nd-tensors. It is assumed that the first dimension is
# the number of batches.
# dim -> (bsz * #steps x additional_forward_args[0].shape[1:], ...)
input_additional_args = (_expand_additional_forward_args(
additional_forward_args, n_steps) if additional_forward_args
is not None else None)
expanded_target = _expand_target(target, n_steps)
# Returns gradient of output with respect to hidden layer.
layer_gradients, _ = compute_layer_gradients_and_eval(
forward_fn=self.forward_func,
layer=self.layer,
inputs=scaled_features_tpl,
target_ind=expanded_target,
additional_forward_args=input_additional_args,
device_ids=self.device_ids,
attribute_to_layer_input=attribute_to_layer_input,
)
# flattening grads so that we can multiply it with step-size
# calling contiguous to avoid `memory whole` problems
scaled_grads = tuple(
layer_grad.contiguous().view(n_steps, -1) *
torch.tensor(step_sizes).view(n_steps, 1).to(layer_grad.device)
for layer_grad in layer_gradients)
# aggregates across all steps for each tensor in the input tuple
attrs = tuple(
_reshape_and_sum(scaled_grad, n_steps, inputs[0].shape[0],
layer_grad.shape[1:])
for scaled_grad, layer_grad in zip(scaled_grads, layer_gradients))
return _format_output(len(attrs) > 1, attrs)
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(
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 _attribute(
self,
inputs: Tuple[Tensor, ...],
baselines: Tuple[Union[Tensor, int, float], ...],
target: TargetType = None,
additional_forward_args: Any = None,
n_steps: int = 50,
method: str = "gausslegendre",
attribute_to_layer_input: bool = False,
step_sizes_and_alphas: Union[None, Tuple[List[float], List[float]]] = None,
) -> Union[Tensor, Tuple[Tensor, ...]]:
num_examples = inputs[0].shape[0]
if step_sizes_and_alphas is None:
# Retrieve scaling factors for specified approximation method
step_sizes_func, alphas_func = approximation_parameters(method)
alphas = alphas_func(n_steps + 1)
else:
_, alphas = step_sizes_and_alphas
# Compute scaled inputs from baseline to final input.
scaled_features_tpl = tuple(
torch.cat(
[baseline + alpha * (input - baseline) for alpha in alphas], dim=0
).requires_grad_()
for input, baseline in zip(inputs, baselines)
)
additional_forward_args = _format_additional_forward_args(
additional_forward_args
)
# apply number of steps to additional forward args
# currently, number of steps is applied only to additional forward arguments
# that are nd-tensors. It is assumed that the first dimension is
# the number of batches.
# dim -> (#examples * #steps x additional_forward_args[0].shape[1:], ...)
input_additional_args = (
_expand_additional_forward_args(additional_forward_args, n_steps + 1)
if additional_forward_args is not None
else None
)
expanded_target = _expand_target(target, n_steps + 1)
# Conductance Gradients - 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(
forward_fn=self.forward_func,
layer=self.layer,
inputs=scaled_features_tpl,
additional_forward_args=input_additional_args,
target_ind=expanded_target,
device_ids=self.device_ids,
attribute_to_layer_input=attribute_to_layer_input,
)
# Compute differences between consecutive evaluations of layer_eval.
# This approximates the total input gradient of each step multiplied
# by the step size.
grad_diffs = tuple(
layer_eval[num_examples:] - layer_eval[:-num_examples]
for layer_eval in layer_evals
)
# Element-wise multiply gradient of output with respect to hidden layer
# and summed gradients with respect to input (chain rule) and sum
# across stepped inputs.
attributions = tuple(
_reshape_and_sum(
grad_diff * layer_gradient[:-num_examples],
n_steps,
num_examples,
layer_eval.shape[1:],
)
for layer_gradient, layer_eval, grad_diff in zip(
layer_gradients, layer_evals, grad_diffs
)
)
return _format_output(len(attributions) > 1, attributions)
