def test_relu_deepliftshap_baselines_as_function(self):
model = ReLULinearDeepLiftModel()
x1 = torch.tensor([[-10.0, 1.0, -5.0]])
x2 = torch.tensor([[3.0, 3.0, 1.0]])
def gen_baselines():
b1 = torch.tensor([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]])
b2 = torch.tensor([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]])
return (b1, b2)
def gen_baselines_with_inputs(inputs):
b1 = inputs[0].mean(0, keepdim=True)
b2 = inputs[1].mean(0, keepdim=True)
return (b1, b2)
def gen_baselines_returns_array():
b1 = [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]
b2 = [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]
return (b1, b2)
inputs = (x1, x2)
dl_shap = DeepLiftShap(model)
self._deeplift_assert(model, dl_shap, inputs, gen_baselines)
self._deeplift_assert(model, dl_shap, inputs, gen_baselines_with_inputs)
with self.assertRaises(AssertionError):
self._deeplift_assert(
model, DeepLiftShap(model), inputs, gen_baselines_returns_array
)
baselines = gen_baselines()
attributions = dl_shap.attribute(inputs, baselines)
attributions_with_func = dl_shap.attribute(inputs, gen_baselines)
assertTensorAlmostEqual(self, attributions[0], attributions_with_func[0])
assertTensorAlmostEqual(self, attributions[1], attributions_with_func[1])
def __init__(
self,
model: Module,
layer: Module,
multiply_by_inputs: bool = True,
) -> None:
r"""
Args:
model (torch.nn.Module): The reference to PyTorch model instance.
layer (torch.nn.Module): Layer for which attributions are computed.
The size and dimensionality of the attributions
corresponds to the size and dimensionality of the layer's
input or output depending on whether we attribute to the
inputs or outputs of the layer.
multiply_by_inputs (bool, optional): Indicates whether to factor
model inputs' multiplier in the final attribution scores.
In the literature this is also known as local vs global
attribution. If inputs' multiplier isn't factored in
then that type of attribution method is also called local
attribution. If it is, then that type of attribution
method is called global.
More detailed can be found here:
https://arxiv.org/abs/1711.06104
In case of LayerDeepLiftShap, if `multiply_by_inputs`
is set to True, final sensitivity scores are being
multiplied by
layer activations for inputs - layer activations for baselines
This flag applies only if `custom_attribution_func` is
set to None.
"""
LayerDeepLift.__init__(self, model, layer)
DeepLiftShap.__init__(self, model, multiply_by_inputs)
def test_relu_deepliftshap_with_hypothetical_contrib_func(self) -> None:
model = Conv1dDeepLiftModel()
rand_seq_data = torch.abs(torch.randn(2, 4, 1000))
rand_seq_ref = torch.abs(torch.randn(3, 4, 1000))
dls = DeepLiftShap(model)
attr = dls.attribute(
rand_seq_data,
rand_seq_ref,
custom_attribution_func=_hypothetical_contrib_func,
target=(0, 0),
)
self.assertEqual(attr.shape, rand_seq_data.shape)
def test_relu_linear_deepliftshap_compare_inplace(self) -> None:
model1 = ReLULinearDeepLiftModel(inplace=True)
x1 = torch.tensor([[-10.0, 1.0, -5.0], [2.0, 3.0, 4.0]], requires_grad=True)
x2 = torch.tensor([[3.0, 3.0, 1.0], [2.3, 5.0, 4.0]], requires_grad=True)
inputs = (x1, x2)
b1 = torch.tensor([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]])
b2 = torch.tensor([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]])
baselines = (b1, b2)
attributions1 = DeepLiftShap(model1).attribute(inputs, baselines)
model2 = ReLULinearDeepLiftModel()
attributions2 = DeepLiftShap(model2).attribute(inputs, baselines)
assertTensorAlmostEqual(self, attributions1[0], attributions2[0])
assertTensorAlmostEqual(self, attributions1[1], attributions2[1])
def test_relu_deepliftshap_with_custom_attr_func(self):
def custom_attr_func(multipliers, inputs, baselines):
return tuple(multiplier * 0.0 for multiplier in multipliers)
model = ReLULinearDeepLiftModel()
x1 = torch.tensor([[-10.0, 1.0, -5.0]])
x2 = torch.tensor([[3.0, 3.0, 1.0]])
b1 = torch.tensor([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]])
b2 = torch.tensor([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]])
inputs = (x1, x2)
baselines = (b1, b2)
dls = DeepLiftShap(model)
attr_w_func = dls.attribute(inputs,
baselines,
custom_attribution_func=custom_attr_func)
assertTensorAlmostEqual(self, attr_w_func[0], [[0.0, 0.0, 0.0]], 0.0)
assertTensorAlmostEqual(self, attr_w_func[1], [[0.0, 0.0, 0.0]], 0.0)
def test_softmax_classification_batch_multi_baseline(self):
num_in = 40
input = torch.arange(0.0, num_in * 2.0, requires_grad=True).reshape(2, num_in)
baselines = torch.randn(5, 40)
model = SoftmaxDeepLiftModel(num_in, 20, 10)
dl = DeepLiftShap(model)
self.softmax_classification(model, dl, input, baselines, torch.tensor(2))
def test_relu_deepliftshap_baselines_as_func(self) -> None:
model = ReLULinearDeepLiftModel(inplace=True)
x1 = torch.tensor([[-10.0, 1.0, -5.0]])
x2 = torch.tensor([[3.0, 3.0, 1.0]])
def gen_baselines() -> Tuple[Tensor, ...]:
b1 = torch.tensor([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]])
b2 = torch.tensor([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]])
return (b1, b2)
def gen_baselines_scalar() -> Tuple[float, ...]:
return (0.0, 0.0001)
def gen_baselines_with_inputs(
inputs: Tuple[Tensor, ...]) -> Tuple[Tensor, ...]:
b1 = torch.cat([inputs[0], inputs[0] - 10])
b2 = torch.cat([inputs[1], inputs[1] - 10])
return (b1, b2)
def gen_baselines_returns_array() -> Tuple[List[List[float]], ...]:
b1 = [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]
b2 = [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]
return (b1, b2)
inputs = (x1, x2)
dl_shap = DeepLiftShap(model)
self._deeplift_assert(model, dl_shap, inputs, gen_baselines)
self._deeplift_assert(model, dl_shap, inputs,
gen_baselines_with_inputs)
with self.assertRaises(AssertionError):
self._deeplift_assert(model, DeepLiftShap(model), inputs,
gen_baselines_returns_array)
with self.assertRaises(AssertionError):
self._deeplift_assert(model, dl_shap, inputs, gen_baselines_scalar)
baselines = gen_baselines()
attributions = dl_shap.attribute(inputs, baselines)
attributions_with_func = dl_shap.attribute(inputs, gen_baselines)
assertTensorAlmostEqual(self, attributions[0],
attributions_with_func[0])
assertTensorAlmostEqual(self, attributions[1],
attributions_with_func[1])
def test_softmax_classification_multi_baseline(self):
num_in = 40
input = torch.arange(0.0, num_in * 1.0,
requires_grad=True).unsqueeze(0)
baselines = torch.randn(5, 40)
model = SoftmaxDeepLiftModel(num_in, 20, 10)
dl = DeepLiftShap(model)
self.softmax_classification(model, dl, input, baselines)
def test_relu_deepliftshap_multi_ref(self) -> None:
x1 = torch.tensor([[1.0]], requires_grad=True)
x2 = torch.tensor([[2.0]], requires_grad=True)
b1 = torch.tensor([[0.0], [0.0], [0.0], [0.0]], requires_grad=True)
b2 = torch.tensor([[0.0], [0.0], [0.0], [0.0]], requires_grad=True)
inputs = (x1, x2)
baselines = (b1, b2)
model = ReLUDeepLiftModel()
self._deeplift_assert(model, DeepLiftShap(model), inputs, baselines)
def test_relu_deepliftshap_batch_4D_input(self) -> None:
x1 = torch.ones(4, 1, 1, 1)
x2 = torch.tensor([[[[2.0]]]] * 4)
b1 = torch.zeros(4, 1, 1, 1)
b2 = torch.zeros(4, 1, 1, 1)
inputs = (x1, x2)
baselines = (b1, b2)
model = ReLUDeepLiftModel()
self._deeplift_assert(model, DeepLiftShap(model), inputs, baselines)
def test_relu_deepliftshap_batch_4D_input_wo_mutliplying_by_inputs(self) -> None:
x1 = torch.ones(4, 1, 1, 1)
x2 = torch.tensor([[[[2.0]]]] * 4)
b1 = torch.zeros(4, 1, 1, 1)
b2 = torch.zeros(4, 1, 1, 1)
inputs = (x1, x2)
baselines = (b1, b2)
model = ReLUDeepLiftModel()
attr = DeepLiftShap(model, multiply_by_inputs=False).attribute(
inputs, baselines
)
assertTensorAlmostEqual(self, attr[0], 2 * torch.ones(4, 1))
assertTensorAlmostEqual(self, attr[1], 0.5 * torch.ones(4, 1))
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
neuron_selector: Union[int, Tuple[Union[int, slice], ...], Callable],
baselines: Union[
TensorOrTupleOfTensorsGeneric, Callable[..., TensorOrTupleOfTensorsGeneric]
],
additional_forward_args: Any = None,
attribute_to_neuron_input: bool = False,
custom_attribution_func: Union[None, Callable[..., Tuple[Tensor, ...]]] = None,
) -> TensorOrTupleOfTensorsGeneric:
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.
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)
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.
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 neuron, 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*):
Computes attributions using Deeplift's rescale rule for
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.
>>> net = ImageClassifier()
>>> # creates an instance of LayerDeepLift to interpret target
>>> # class 1 with respect to conv4 layer.
>>> dl = NeuronDeepLiftShap(net, net.conv4)
>>> input = torch.randn(1, 3, 32, 32, requires_grad=True)
>>> # Computes deeplift attribution scores for conv4 layer and neuron
>>> # index (4,1,2).
>>> attribution = dl.attribute(input, (4,1,2))
"""
dl = DeepLiftShap(cast(Module, self.forward_func), self.multiplies_by_inputs)
dl.gradient_func = construct_neuron_grad_fn(
self.layer,
neuron_selector,
attribute_to_neuron_input=attribute_to_neuron_input,
)
# NOTE: using __wrapped__ to not log
return dl.attribute.__wrapped__( # type: ignore
dl, # self
inputs,
baselines,
additional_forward_args=additional_forward_args,
custom_attribution_func=custom_attribution_func,
)
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
