_util.py

import torch
from typing import Optional, List

class AverageMeter(object):
    r"""Computes and stores the average and current value.

    Examples::

        >>> # Initialize a meter to record loss
        >>> losses = AverageMeter()
        >>> # Update meter after every minibatch update
        >>> losses.update(loss_value, batch_size)
    """
    def __init__(self, name: str, fmt: Optional[str] = ':f'):
        self.name = name
        self.fmt = fmt
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        if self.count > 0:
            self.avg = self.sum / self.count

    def __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)

def binary_accuracy(output: torch.Tensor, target: torch.Tensor) -> float:
    """Computes the accuracy for binary classification"""
    with torch.no_grad():
        batch_size = target.size(0)
        pred = (output >= 0.5).float().t().view(-1)
        correct = pred.eq(target.view(-1)).float().sum()
        correct.mul_(100. / batch_size)
        return correct