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Source code for torch_uncertainty.models.wrappers.checkpoint_ensemble

import copy

import torch
from torch import nn


[docs]class CheckpointEnsemble(nn.Module): def __init__( self, model: nn.Module, save_schedule: list[int] | None = None, use_final_checkpoint: bool = True, ) -> None: """Ensemble of models at different points in the training trajectory. Args: model (nn.Module): The model to train and ensemble. save_schedule (list[int]): The epochs at which to save the model. If save schedule is None, save the model at every epoch. Defaults to None. use_final_checkpoint (bool, optional): Whether to use the final model as a checkpoint. Defaults to True. Reference: Checkpoint Ensembles: Ensemble Methods from a Single Training Process. Hugh Chen, Scott Lundberg, Su-In Lee. In ArXiv 2018. """ super().__init__() self.core_model = model self.save_schedule = save_schedule self.use_final_checkpoint = use_final_checkpoint self.num_estimators = int(use_final_checkpoint) self.saved_models = [] self.num_estimators = 1
[docs] @torch.no_grad() def update_wrapper(self, epoch: int) -> None: """Save the model at the given epoch if included in the schedule. Args: epoch (int): The current epoch. """ if self.save_schedule is None or epoch in self.save_schedule: self.saved_models.append(copy.deepcopy(self.core_model)) self.num_estimators += 1
[docs] def eval_forward(self, x: torch.Tensor) -> torch.Tensor: """Forward pass for evaluation. If the model is in evaluation mode, this method will return the ensemble prediction. Otherwise, it will return the prediction of the current model. Args: x (torch.Tensor): The input tensor. Returns: torch.Tensor: The model or ensemble output. """ if not len(self.saved_models): return self.core_model.forward(x) preds = torch.cat([model.forward(x) for model in self.saved_models], dim=0) if self.use_final_checkpoint: model_forward = self.core_model.forward(x) preds = torch.cat([model_forward, preds], dim=0) return preds
def forward(self, x: torch.Tensor) -> torch.Tensor: if self.training: return self.core_model.forward(x) return self.eval_forward(x)