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Source code for torch_uncertainty.metrics.classification.fpr

import torch
from torch import Tensor
from torchmetrics import Metric
from torchmetrics.utilities import rank_zero_warn
from torchmetrics.utilities.data import dim_zero_cat


[docs]class FPRx(Metric): is_differentiable: bool = False higher_is_better: bool = False full_state_update: bool = False conf: list[Tensor] targets: list[Tensor] def __init__(self, recall_level: float, pos_label: int, **kwargs) -> None: """The False Positive Rate at x% Recall metric. Args: recall_level (float): The recall level at which to compute the FPR. pos_label (int): The positive label. kwargs: Additional arguments to pass to the metric class. Reference: Improved from https://github.com/hendrycks/anomaly-seg and translated to torch. """ super().__init__(**kwargs) if recall_level < 0 or recall_level > 1: raise ValueError( f"Recall level must be between 0 and 1. Got {recall_level}." ) self.recall_level = recall_level self.pos_label = pos_label self.add_state("conf", [], dist_reduce_fx="cat") self.add_state("targets", [], dist_reduce_fx="cat") rank_zero_warn( f"Metric `FPR{int(recall_level*100)}` will save all targets and predictions" " in buffer. For large datasets this may lead to large memory" " footprint." )
[docs] def update(self, conf: Tensor, target: Tensor) -> None: """Update the metric state. Args: conf (Tensor): The confidence scores. target (Tensor): The target labels, 0 if ID, 1 if OOD. """ self.conf.append(conf) self.targets.append(target)
[docs] def compute(self) -> Tensor: """Compute the False Positive Rate at x% Recall. Returns: Tensor: The value of the FPRx. """ conf = dim_zero_cat(self.conf) targets = dim_zero_cat(self.targets) # map examples and labels to OOD first indx = torch.argsort(targets, descending=True) examples = conf[indx] labels = torch.zeros_like(targets, dtype=torch.bool, device=self.device) labels[: torch.count_nonzero(targets)] = True # sort examples and labels by decreasing confidence desc_scores_indx = torch.argsort(examples, descending=True) examples = examples[desc_scores_indx] labels = labels[desc_scores_indx] # Get the indices of the distinct values distinct_value_indices = torch.where(torch.diff(examples))[0] threshold_idxs = torch.cat( [ distinct_value_indices, torch.tensor( [labels.shape[0] - 1], dtype=torch.long, device=self.device ), ] ) # accumulate the true positives with decreasing threshold true_pos = torch.cumsum(labels, dim=0)[threshold_idxs] false_pos = ( 1 + threshold_idxs - true_pos ) # add one because of zero-based indexing # check that there is at least one OOD example if true_pos[-1] == 0: return torch.tensor([torch.nan], device=self.device) recall = true_pos / true_pos[-1] last_ind = torch.searchsorted(true_pos, true_pos[-1]) recall = torch.cat( [ recall[: last_ind + 1].flip(0), torch.tensor([1.0], device=self.device), ] ) false_pos = torch.cat( [ false_pos[: last_ind + 1].flip(0), torch.tensor([0.0], device=self.device), ] ) cutoff = torch.argmin(torch.abs(recall - self.recall_level)) return false_pos[cutoff] / (~labels).sum()
[docs]class FPR95(FPRx): def __init__(self, pos_label: int, **kwargs) -> None: """The False Positive Rate at 95% Recall metric. Args: pos_label (int): The positive label. kwargs: Additional arguments to pass to the metric class. """ super().__init__(recall_level=0.95, pos_label=pos_label, **kwargs)