Module laplace.utils.feature_extractor

Classes

class FeatureReduction (value, names=None, *, module=None, qualname=None, type=None, start=1)

Possible choices of feature reduction before applying last-layer Laplace.

Ancestors

• builtins.str
• enum.Enum

Class variables

var PICK_FIRST

var PICK_LAST

var AVERAGE

class FeatureExtractor (model: nn.Module, last_layer_name: str | None = None, enable_backprop: bool = False, feature_reduction: FeatureReduction | str | None = None)

Feature extractor for a PyTorch neural network. A wrapper which can return the output of the penultimate layer in addition to the output of the last layer for each forward pass. If the name of the last layer is not known, it can determine it automatically. It assumes that the last layer is linear and that for every forward pass the last layer is the same. If the name of the last layer is known, it can be passed as a parameter at initilization; this is the safest way to use this class. Based on https://gist.github.com/fkodom/27ed045c9051a39102e8bcf4ce31df76.

Parameters

model : torch.nn.Module

PyTorch model

last_layer_name : str, default=None

if the name of the last layer is already known, otherwise it will be determined automatically.

enable_backprop : bool, default=False

whether to enable backprop through the feature extactor to get the gradients of the inputs. Useful for e.g. Bayesian optimization.

feature_reduction : FeatureReduction or str, default=None

when the last-layer features is a tensor of dim >= 3, this tells how to reduce it into a dim-2 tensor. E.g. in LLMs for non-language modeling problems, the penultultimate output is a tensor of shape (batch_size, seq_len, embd_dim). But the last layer maps (batch_size, embd_dim) to (batch_size, n_classes). Note: Make sure that this option faithfully reflects the reduction in the model definition. When inputting a string, available options are {'pick_first', 'pick_last', 'average'}.

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Ancestors

• torch.nn.modules.module.Module

Class variables

var dump_patches : bool

var training : bool

var call_super_init : bool

Methods

def forward(self, x: torch.Tensor | MutableMapping[str, torch.Tensor | Any]) ‑> Callable[..., Any]

Forward pass. If the last layer is not known yet, it will be determined when this function is called for the first time.

Parameters

x : torch.Tensor or a dict-like object containing the input tensors

one batch of data to use as input for the forward pass

def forward_with_features(self, x: torch.Tensor | MutableMapping[str, torch.Tensor | Any]) ‑> tuple[torch.Tensor, torch.Tensor]

Forward pass which returns the output of the penultimate layer along with the output of the last layer. If the last layer is not known yet, it will be determined when this function is called for the first time.

Parameters

x : torch.Tensor or a dict-like object containing the input tensors

one batch of data to use as input for the forward pass

def set_last_layer(self, last_layer_name: str) ‑> None

Set the last layer of the model by its name. This sets the forward hook to get the output of the penultimate layer.

Parameters

last_layer_name : str

the name of the last layer (fixed in model.named_modules()).

def find_last_layer(self, x: torch.Tensor | MutableMapping[str, torch.Tensor | Any]) ‑> torch.Tensor

Automatically determines the last layer of the model with one forward pass. It assumes that the last layer is the same for every forward pass and that it is an instance of torch.nn.Linear. Might not work with every architecture, but is tested with all PyTorch torchvision classification models (besides SqueezeNet, which has no linear last layer).

Parameters

x : torch.Tensor or dict-like object containing the input tensors

one batch of data to use as input for the forward pass