def test_gradient_target_int(self) -> None:
model = BasicModel2()
input1 = torch.tensor([[4.0, -1.0]], requires_grad=True)
input2 = torch.tensor([[2.0, 5.0]], requires_grad=True)
grads0 = compute_gradients(model, (input1, input2), target_ind=0)
grads1 = compute_gradients(model, (input1, input2), target_ind=1)
assertArraysAlmostEqual(grads0[0].squeeze(0).tolist(), [1.0, 0.0],
delta=0.01)
assertArraysAlmostEqual(grads0[1].squeeze(0).tolist(), [-1.0, 0.0],
delta=0.01)
assertArraysAlmostEqual(grads1[0].squeeze(0).tolist(), [0.0, 0.0],
delta=0.01)
assertArraysAlmostEqual(grads1[1].squeeze(0).tolist(), [0.0, 0.0],
delta=0.01)
def test_gradient_multiinput(self) -> None:
model = BasicModel6_MultiTensor()
input1 = torch.tensor([[-3.0, -5.0]], requires_grad=True)
input2 = torch.tensor([[-5.0, 2.0]], requires_grad=True)
grads = compute_gradients(model, (input1, input2))
assertArraysAlmostEqual(grads[0].squeeze(0).tolist(), [0.0, 1.0],
delta=0.01)
assertArraysAlmostEqual(grads[1].squeeze(0).tolist(), [0.0, 1.0],
delta=0.01)
def test_gradient_basic_2(self) -> None:
model = BasicModel()
input = torch.tensor([[-3.0]], requires_grad=True)
input.grad = torch.tensor([[14.0]])
grads = compute_gradients(model, input)[0]
assertArraysAlmostEqual(grads.squeeze(0).tolist(), [1.0], delta=0.01)
# Verify grad attribute is not altered
assertArraysAlmostEqual(input.grad.squeeze(0).tolist(), [14.0],
delta=0.0)
def test_gradient_additional_args_2(self) -> None:
model = BasicModel5_MultiArgs()
input1 = torch.tensor([[-10.0]], requires_grad=True)
input2 = torch.tensor([[6.0]], requires_grad=True)
grads = compute_gradients(model, (input1, input2),
additional_forward_args=([3, -4], ))
assertArraysAlmostEqual(grads[0].squeeze(0).tolist(), [0.0],
delta=0.01)
assertArraysAlmostEqual(grads[1].squeeze(0).tolist(), [4.0],
delta=0.01)
def test_gradient_target_tuple(self) -> None:
model = BasicModel()
input = torch.tensor(
[[[4.0, 2.0], [-1.0, -2.0]], [[3.0, -4.0], [10.0, 5.0]]],
requires_grad=True)
grads = compute_gradients(model, input, target_ind=(0, 1))[0]
assertArraysAlmostEqual(
torch.flatten(grads).tolist(),
[0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
delta=0.01,
)
def test_gradient_target_list(self) -> None:
model = BasicModel2()
input1 = torch.tensor([[4.0, -1.0], [3.0, 10.0]], requires_grad=True)
input2 = torch.tensor([[2.0, -5.0], [-2.0, 1.0]], requires_grad=True)
grads = compute_gradients(model, (input1, input2), target_ind=[0, 1])
assertArraysAlmostEqual(torch.flatten(grads[0]).tolist(),
[1.0, 0.0, 0.0, 1.0],
delta=0.01)
assertArraysAlmostEqual(torch.flatten(grads[1]).tolist(),
[-1.0, 0.0, 0.0, -1.0],
delta=0.01)
def _get_multiargs_basic_config(
) -> Tuple[Module, Tuple[Tensor, ...], Tuple[Tensor, ...], Any]:
model = BasicModel5_MultiArgs()
additional_forward_args = ([2, 3], 1)
inputs = (
torch.tensor([[1.5, 2.0, 34.3], [3.4, 1.2, 2.0]], requires_grad=True),
torch.tensor([[3.0, 3.5, 23.2], [2.3, 1.2, 0.3]], requires_grad=True),
)
grads = compute_gradients(model,
inputs,
additional_forward_args=additional_forward_args)
return model, inputs, grads, additional_forward_args
def _get_multiargs_basic_config_large(
) -> Tuple[Module, Tuple[Tensor, ...], Tuple[Tensor, ...], Any]:
model = BasicModel5_MultiArgs()
additional_forward_args = ([2, 3], 1)
inputs = (
torch.tensor([[10.5, 12.0, 34.3], [43.4, 51.2, 32.0]],
requires_grad=True).repeat_interleave(3, dim=0),
torch.tensor([[1.0, 3.5, 23.2], [2.3, 1.2, 0.3]],
requires_grad=True).repeat_interleave(3, dim=0),
)
grads = compute_gradients(model,
inputs,
additional_forward_args=additional_forward_args)
return model, inputs, grads, additional_forward_args
def test_gradient_inplace(self) -> None:
model = BasicModel_MultiLayer(inplace=True)
input = torch.tensor([[1.0, 6.0, -3.0]], requires_grad=True)
grads = compute_gradients(model, input, target_ind=0)[0]
assertArraysAlmostEqual(grads.squeeze(0).tolist(), [3.0, 3.0, 3.0],
delta=0.01)
def test_gradient_basic_2(self) -> None:
model = BasicModel()
input = torch.tensor([[-3.0]], requires_grad=True)
grads = compute_gradients(model, input)[0]
assertArraysAlmostEqual(grads.squeeze(0).tolist(), [1.0], delta=0.01)
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_tensor_into_tuples(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 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)
