def __init__(
self,
forward_func: Callable,
layer: ModuleOrModuleList,
device_ids: Union[None, List[int]] = None,
) -> None:
r"""
Args:
forward_func (callable): The forward function of the model or any
modification of it
layer (torch.nn.Module or list(torch.nn.Module)): Layer or layers
for which attributions are computed.
Output size of attribute matches this layer's input or
output dimensions, depending on whether we attribute to
the inputs or outputs of the layer, corresponding to
attribution of each neuron in the input or output of
this layer. If multiple layers are provided, attributions
are returned as a list, each element corresponding to the
activations of the corresponding layer.
device_ids (list(int)): Device ID list, necessary only if forward_func
applies a DataParallel model. This allows reconstruction of
intermediate outputs from batched results across devices.
If forward_func is given as the DataParallel model itself,
then it is not necessary to provide this argument.
"""
LayerAttribution.__init__(self, forward_func, layer, device_ids)
def __init__(
self,
forward_func: Callable,
layer: Module,
device_ids: Optional[List[int]] = None,
) -> None:
r"""
Args:
forward_func (callable): The forward function of the model or any
modification of it
layer (torch.nn.Module): Layer for which attributions are computed.
Output size of attribute matches this layer's input or
output dimensions, depending on whether we attribute to
the inputs or outputs of the layer, corresponding to
the attribution of each neuron in the input or output
of this layer.
device_ids (list(int)): Device ID list, necessary only if forward_func
applies a DataParallel model. This allows reconstruction of
intermediate outputs from batched results across devices.
If forward_func is given as the DataParallel model itself,
then it is not necessary to provide this argument.
"""
LayerAttribution.__init__(self, forward_func, layer, device_ids=device_ids)
GradientAttribution.__init__(self, forward_func)
self.ig = IntegratedGradients(forward_func)
def __init__(self, model: Module, layer: ModuleOrModuleList) -> None:
"""
Args:
model (module): The forward function of the model or
any modification of it. Custom rules for a given layer need to
be defined as attribute
`module.rule` and need to be of type PropagationRule.
Model cannot contain any in-place nonlinear submodules;
these are not supported by the register_full_backward_hook
PyTorch API starting from PyTorch v1.9.
layer (torch.nn.Module or list(torch.nn.Module)): Layer or layers
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. If value is None, the
relevance for all layers is returned in attribution.
"""
LayerAttribution.__init__(self, model, layer)
LRP.__init__(self, model)
if hasattr(self.model, "device_ids"):
self.device_ids = cast(List[int], self.model.device_ids)
def __init__(self, model: Module, layer: Module):
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.
"""
LayerAttribution.__init__(self, model, layer)
DeepLift.__init__(self, model)
self.model = model
def __init__(self, model: Module, layer: ModuleOrModuleList) -> None:
"""
Args:
model (module): The forward function of the model or
any modification of it. Custom rules for a given layer need to
be defined as attribute
`module.rule` and need to be of type PropagationRule.
layer (torch.nn.Module or list(torch.nn.Module)): Layer or layers
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. If value is None, the
relevance for all layers is returned in attribution.
"""
LayerAttribution.__init__(self, model, layer)
LRP.__init__(self, model)
def __init__(
self,
forward_func: Callable,
layer: ModuleOrModuleList,
device_ids: Union[None, List[int]] = None,
multiply_by_inputs: bool = True,
) -> None:
r"""
Args:
forward_func (callable): The forward function of the model or any
modification of it
layer (torch.nn.Module or list(torch.nn.Module)): Layer or layers
for which attributions are computed.
Output size of attribute matches this layer's input or
output dimensions, depending on whether we attribute to
the inputs or outputs of the layer, corresponding to
attribution of each neuron in the input or output of
this layer. If multiple layers are provided, attributions
are returned as a list, each element corresponding to the
attributions of the corresponding layer.
device_ids (list(int)): Device ID list, necessary only if forward_func
applies a DataParallel model. This allows reconstruction of
intermediate outputs from batched results across devices.
If forward_func is given as the DataParallel model itself,
then it is not necessary to provide this argument.
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 this 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 layer gradient x activation, if `multiply_by_inputs`
is set to True, final sensitivity scores are being multiplied by
layer activations for inputs.
"""
LayerAttribution.__init__(self, forward_func, layer, device_ids)
GradientAttribution.__init__(self, forward_func)
self._multiply_by_inputs = multiply_by_inputs
def __init__(
self,
model: Module,
layer: Module,
multiply_by_inputs: bool = True,
) -> None:
r"""
Args:
model (nn.Module): The reference to PyTorch model instance. Model cannot
contain any in-place nonlinear submodules; these are not
supported by the register_full_backward_hook PyTorch API
starting from PyTorch v1.9.
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 Layer DeepLift, 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.
"""
LayerAttribution.__init__(self, model, layer)
DeepLift.__init__(self, model)
self.model = model
self._multiply_by_inputs = multiply_by_inputs
def __init__(
self,
forward_func: Callable,
layer: Module,
device_ids: Union[None, List[int]] = None,
) -> None:
r"""
Args:
forward_func (callable): The forward function of the model or any
modification of it
layer (torch.nn.Module): Layer for which attributions are computed.
Output size of attribute matches this layer's output
dimensions, except for dimension 2, which will be 1,
since GradCAM sums over channels.
device_ids (list(int)): Device ID list, necessary only if forward_func
applies a DataParallel model. This allows reconstruction of
intermediate outputs from batched results across devices.
If forward_func is given as the DataParallel model itself,
then it is not necessary to provide this argument.
"""
LayerAttribution.__init__(self, forward_func, layer, device_ids)
GradientAttribution.__init__(self, forward_func)
def __init__(
self,
forward_func: Callable,
layer: ModuleOrModuleList,
device_ids: Union[None, List[int]] = None,
multiply_by_inputs: bool = True,
) -> None:
r"""
Args:
forward_func (callable): The forward function of the model or any
modification of it
layer (ModuleOrModuleList):
Layer or list of layers for which attributions are computed.
For each layer the output size of the attribute matches
this layer's input or output dimensions, depending on
whether we attribute to the inputs or outputs of the
layer, corresponding to the attribution of each neuron
in the input or output of this layer.
Please note that layers to attribute on cannot be
dependent on each other. That is, a subset of layers in
`layer` cannot produce the inputs for another layer.
For example, if your model is of a simple linked-list
based graph structure (think nn.Sequence), e.g. x -> l1
-> l2 -> l3 -> output. If you pass in any one of those
layers, you cannot pass in another due to the
dependence, e.g. if you pass in l2 you cannot pass in
l1 or l3.
device_ids (list(int)): Device ID list, necessary only if forward_func
applies a DataParallel model. This allows reconstruction of
intermediate outputs from batched results across devices.
If forward_func is given as the DataParallel model itself,
then it is not necessary to provide this argument.
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 this 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 layer integrated gradients, if `multiply_by_inputs`
is set to True, final sensitivity scores are being multiplied by
layer activations for inputs - layer activations for baselines.
"""
LayerAttribution.__init__(self,
forward_func,
layer,
device_ids=device_ids)
GradientAttribution.__init__(self, forward_func)
self.ig = IntegratedGradients(forward_func, multiply_by_inputs)
if isinstance(layer, list) and len(layer) > 1:
warnings.warn(
"Multiple layers provided. Please ensure that each layer is"
"**not** solely solely dependent on the outputs of"
"another layer. Please refer to the documentation for more"
"detail.")
