Source code for torch_uncertainty.post_processing.conformal.thr

from typing import Literal

import torch
from torch import Tensor, nn
from torch.utils.data import DataLoader

from .abstract import Conformal




class ConformalClsTHR(Conformal):
    def __init__(
        self,
        alpha: float,
        model: nn.Module | None = None,
        ts_init_val: float = 1.0,
        ts_lr: float = 0.1,
        ts_max_iter: int = 100,
        enable_ts: bool = True,
        device: Literal["cpu", "cuda"] | torch.device | None = None,
    ) -> None:
        r"""Conformal prediction post-processing for calibrated models.

        Args:
            alpha (float): The confidence level, meaning we allow :math:`1-\alpha` error.
            model (nn.Module, optional): Model to be calibrated. Defaults to ``None``.
            ts_init_val (float, optional): Initial value for the temperature.
                Defaults to ``1.0``.
            ts_lr (float, optional): Learning rate for the optimizer. Defaults to ``0.1``.
            ts_max_iter (int, optional): Maximum number of iterations for the
                optimizer. Defaults to ``100``.
            enable_ts (bool): Whether to scale the logits. Defaults to ``True``.
            device (Literal["cpu", "cuda"] | torch.device | None, optional): device.
                Defaults to ``None``.

        Reference:
            - `Least ambiguous set-valued classifiers with bounded error levels, Sadinle, M. et al., (2016) <https://arxiv.org/abs/1609.00451>`_.

        Code inspired by TorchCP.
        """
        super().__init__(
            alpha=alpha,
            model=model,
            ts_init_val=ts_init_val,
            ts_lr=ts_lr,
            ts_max_iter=ts_max_iter,
            enable_ts=enable_ts,
            device=device,
        )

    def fit(self, dataloader: DataLoader) -> None:
        if self.enable_ts:
            self.model.fit(dataloader=dataloader)

        logit_list = []
        label_list = []
        with torch.no_grad():
            for images, labels in dataloader:
                images, labels = images.to(self.device), labels.to(self.device)
                logit_list.append(self.model_forward(images))
                label_list.append(labels)

        probs = torch.cat(logit_list)
        labels = torch.cat(label_list).long()
        true_class_probs = probs.gather(1, labels.unsqueeze(1)).squeeze(1)
        scores = 1.0 - true_class_probs
        self.q_hat = torch.quantile(scores, 1.0 - self.alpha).item()



    @torch.no_grad()
    def conformal(self, inputs: Tensor) -> Tensor:
        """Perform conformal prediction on the test set."""
        probs = self.model_forward(inputs)
        pred_set = probs >= 1.0 - self.quantile
        top1 = torch.argmax(probs, dim=1, keepdim=True)
        pred_set.scatter_(1, top1, True)  # Always include top-1 class
        return pred_set.float() / pred_set.sum(dim=1, keepdim=True)