meters.py

import torch


class ProgressMeter(object):
    def __init__(self, num_batches, *meters, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix

    def print(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        print('\t'.join(entries))

    def _get_batch_fmtstr(self, num_batches):
        num_digits = len(str(num_batches // 1))
        fmt = '{:' + str(num_digits) + 'd}'
        return '[' + fmt + '/' + fmt.format(num_batches) + ']'


class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self, name='', fmt=':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
        self.avg = self.sum / self.count

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


class OnlineMeter(object):
    """Computes and stores the average and variance/std values of tensor"""

    def __init__(self):
        self.mean = torch.FloatTensor(1).fill_(-1)
        self.M2 = torch.FloatTensor(1).zero_()
        self.count = 0.
        self.needs_init = True

    def reset(self, x):
        self.mean = x.new(x.size()).zero_()
        self.M2 = x.new(x.size()).zero_()
        self.count = 0.
        self.needs_init = False

    def update(self, x):
        self.val = x
        if self.needs_init:
            self.reset(x)
        self.count += 1
        delta = x - self.mean
        self.mean.add_(delta / self.count)
        delta2 = x - self.mean
        self.M2.add_(delta * delta2)

    @property
    def var(self):
        if self.count < 2:
            return self.M2.clone().zero_()
        return self.M2 / (self.count - 1)

    @property
    def std(self):
        return self.var().sqrt()


def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res


class AccuracyMeter(object):
    """Computes and stores the average and current topk accuracy"""

    def __init__(self, topk=(1,)):
        self.topk = topk
        self.reset()

    def reset(self):
        self._meters = {}
        for k in self.topk:
            self._meters[k] = AverageMeter()

    def update(self, output, target):
        n = target.nelement()
        acc_vals = accuracy(output, target, self.topk)
        for i, k in enumerate(self.topk):
            self._meters[k].update(acc_vals[i])

    @property
    def val(self):
        return {n: meter.val for (n, meter) in self._meters.items()}

    @property
    def avg(self):
        return {n: meter.avg for (n, meter) in self._meters.items()}

    @property
    def avg_error(self):
        return {n: 100. - meter.avg for (n, meter) in self._meters.items()}