def __init__(self,
model: Module,
layer: Module,
device_ids: Union[None, List[int]] = None) -> None:
r"""
Args:
model (Module): The reference to PyTorch model instance.
layer (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
attribution of each neuron in the input or output of
this layer.
Currently, it is assumed that the inputs or the outputs
of the layer, depending on which one is used for
attribution, can only be a single tensor.
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.
"""
NeuronAttribution.__init__(self, model, layer, device_ids)
GradientAttribution.__init__(self, model)
self.deconv = Deconvolution(model)
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: Module, device_ids: Union[None, List[int]] = None
) -> None:
r"""
Args:
model (nn.Module): The reference to PyTorch model instance. Model cannot
contain any in-place ReLU submodules; these are not
supported by the register_full_backward_hook PyTorch API
starting from PyTorch v1.9.
layer (Module): Layer for which neuron attributions are computed.
Attributions for a particular neuron in the output of
this layer are computed using the argument neuron_selector
in the attribute method.
Currently, only layers with a single tensor output are
supported.
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.
"""
NeuronAttribution.__init__(self, model, layer, device_ids)
GradientAttribution.__init__(self, model)
self.guided_backprop = GuidedBackprop(model)
def __init__(self,
forward_func: Callable,
multiply_by_inputs=True) -> None:
r"""
Args:
forward_func (function): The forward function of the model or
any modification of it
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 gradient shap, if `multiply_by_inputs`
is set to True, the sensitivity scores of scaled inputs
are being multiplied by (inputs - baselines).
"""
GradientAttribution.__init__(self, forward_func)
self._multiply_by_inputs = multiply_by_inputs
def __init__(self, model: Module, multiply_by_inputs: bool = True) -> None:
r"""
Args:
model (nn.Module): The reference to PyTorch model instance.
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 DeepLift, if `multiply_by_inputs`
is set to True, final sensitivity scores
are being multiplied by (inputs - baselines).
This flag applies only if `custom_attribution_func` is
set to None.
"""
GradientAttribution.__init__(self, model)
self.model = model
self.forward_handles: List[RemovableHandle] = []
self.backward_handles: List[RemovableHandle] = []
self._multiply_by_inputs = multiply_by_inputs
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 neuron attributions are computed.
Attributions for a particular neuron for the input or output
of this layer are computed using the argument neuron_selector
in the attribute method.
Currently, it is assumed that the inputs or the outputs
of the layer, depending on which one is used for
attribution, can only be a single tensor.
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 Neuron DeepLift, if `multiply_by_inputs`
is set to True, final sensitivity scores
are being multiplied by (inputs - baselines).
This flag applies only if `custom_attribution_func` is
set to None.
"""
NeuronAttribution.__init__(self, model, layer)
GradientAttribution.__init__(self, model)
self._multiply_by_inputs = multiply_by_inputs
def __init__(self, forward_func: Callable) -> None:
r"""
Args:
forward_func (callable): The forward function of the model or any
modification of it
"""
GradientAttribution.__init__(self, forward_func)
def __init__(self, model: Module) -> None:
r"""
Args:
model (nn.Module): The reference to PyTorch model instance.
"""
GradientAttribution.__init__(self, model)
self.model = model
self.forward_handles: List[RemovableHandle] = []
self.backward_handles: List[RemovableHandle] = []
def __init__(self, model: Module) -> None:
r"""
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. If no rule is
specified for a layer, a pre-defined default rule for the module type
is used.
"""
GradientAttribution.__init__(self, model)
self.model = model
self._check_rules()
def __init__(
self,
forward_func: Callable,
layer: Module,
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): Layer for which neuron attributions are computed.
Attributions for a particular neuron in the input or output
of this layer are computed using the argument neuron_selector
in the attribute method.
Currently, only layers with a single tensor input or output
are supported.
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
attribution of each neuron in the input or output of
this layer.
Currently, it is assumed that the inputs or the outputs
of the layer, depending on which one is used for
attribution, can only be a single tensor.
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 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 Neuron Conductance,
if `multiply_by_inputs` is set to True, final
sensitivity scores are being multiplied
by (inputs - baselines).
"""
NeuronAttribution.__init__(self, forward_func, layer, device_ids)
GradientAttribution.__init__(self, forward_func)
self._multiply_by_inputs = multiply_by_inputs
def __init__(self, model: Module, use_relu_grad_output: bool = False):
r"""
Args:
model (nn.Module): The reference to PyTorch model instance.
"""
GradientAttribution.__init__(self, model)
self.model = model
self.backward_hooks: List[RemovableHandle] = []
self.use_relu_grad_output = use_relu_grad_output
assert isinstance(self.model, torch.nn.Module), (
"Given model must be an instance of torch.nn.Module to properly hook"
" ReLU layers."
)
def __init__(
self,
forward_func: Callable,
m: float = 20.0,
) -> None:
r"""
Args:
forward_func (callable): The forward function of the model or any
modificationof it
m (float): The hyperparameter forRiemman approximation of the integration
(https://arxiv.org/abs/1703.01365).
"""
GradientAttribution.__init__(self, forward_func)
self.
self.m = m
def __init__(self, model: Module) -> None:
r"""
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. If no rule is
specified for a layer, a pre-defined default rule for the module type
is used. 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.
"""
GradientAttribution.__init__(self, model)
self.model = model
self._check_rules()
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,
device_ids: Union[None, List[int]] = None) -> None:
r"""
Args:
model (nn.Module): The reference to PyTorch model instance.
layer (torch.nn.Module): Layer for which GradCAM attributions are computed.
Currently, only layers with a single tensor output are
supported.
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.
"""
GradientAttribution.__init__(self, model)
self.grad_cam = LayerGradCam(model, layer, device_ids)
self.guided_backprop = GuidedBackprop(model)
def __init__(
self,
model: Module,
multiply_by_inputs: bool = True,
eps: float = 1e-10,
) -> 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.
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 DeepLift, if `multiply_by_inputs`
is set to True, final sensitivity scores
are being multiplied by (inputs - baselines).
This flag applies only if `custom_attribution_func` is
set to None.
eps (float, optional): A value at which to consider output/input change
significant when computing the gradients for non-linear layers.
This is useful to adjust, depending on your model's bit depth,
to avoid numerical issues during the gradient computation.
Default: 1e-10
"""
GradientAttribution.__init__(self, model)
self.model = model
self.eps = eps
self.forward_handles: List[RemovableHandle] = []
self.backward_handles: List[RemovableHandle] = []
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.")