Source code for catalyst.dl.utils.criterion.f1_score

import torch

from catalyst.utils import get_activation_fn


[docs]def f1_score( outputs: torch.Tensor, targets: torch.Tensor, beta: float = 1.0, eps: float = 1e-7, threshold: float = None, activation: str = "Sigmoid" ): """ Source https://github.com/qubvel/segmentation_models.pytorch Args: outputs (torch.Tensor): A list of predicted elements targets (torch.Tensor): A list of elements that are to be predicted eps (float): epsilon to avoid zero division beta (float): beta param for f_score threshold (float): threshold for outputs binarization activation (str): An torch.nn activation applied to the outputs. Must be one of ["none", "Sigmoid", "Softmax2d"] Returns: float: F_1 score """ activation_fn = get_activation_fn(activation) outputs = activation_fn(outputs) if threshold is not None: outputs = (outputs > threshold).float() true_positive = torch.sum(targets * outputs) false_positive = torch.sum(outputs) - true_positive false_negative = torch.sum(targets) - true_positive precision_plus_recall = (1 + beta ** 2) * true_positive + \ beta ** 2 * false_negative + false_positive + eps score = ((1 + beta**2) * true_positive + eps) / precision_plus_recall return score
__all__ = ["f1_score"]