metric.py

import time
import torch


def accuracy(output, target, topk=(1,)):
    """Computes the precision@k 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].view(-1).float().sum().item()
            res.append(correct_k*100.0 / batch_size)

        if len(res)==1:
            return res[0]
        else:
            return res


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

    def __init__(self):
        self.reset()

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

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

    def update_count(self, multiplier):
        self.count = self.count * multiplier
        self.avg = float(self.sum) / self.count


class Timer(object):
    """
    """

    def __init__(self):
        self.reset()

    def reset(self):
        self.interval = 0
        self.time = time.time()

    def value(self):
        return time.time() - self.time

    def tic(self):
        self.time = time.time()

    def toc(self):
        self.interval = time.time() - self.time
        self.time = time.time()
        return self.interval